Energy Policy

Public Administration and Information Technology

Volume 10

Series Editor Christopher G. Reddick San Antonio, Texas, USA

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More information about this series at http://www.springer.com/series/10796

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Marijn Janssen • Maria A. Wimmer Ameneh Deljoo Editors

Policy Practice and Digital Science

Integrating Complex Systems, Social Simulation and Public Administration in Policy Research

2123

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Editors Marijn Janssen Ameneh Deljoo Faculty of Technology, Policy, and Faculty of Technology, Policy, and Management Management Delft University of Technology Delft University of Technology Delft Delft The Netherlands The Netherlands

Maria A. Wimmer Institute for Information Systems Research University of Koblenz-Landau Koblenz Germany

ISBN 978-3-319-12783-5 ISBN 978-3-319-12784-2 (eBook) Public Administration and Information Technology DOI 10.1007/978-3-319-12784-2

Library of Congress Control Number: 2014956771

Springer Cham Heidelberg New York London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.

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Preface

The last economic and financial crisis has heavily threatened European and other economies around the globe. Also, the Eurozone crisis, the energy and climate change crises, challenges of demographic change with high unemployment rates, and the most recent conflicts in the Ukraine and the near East or the Ebola virus disease in Africa threaten the wealth of our societies in different ways. The inability to predict or rapidly deal with dramatic changes and negative trends in our economies and societies can seriously hamper the wealth and prosperity of the European Union and its Member States as well as the global networks. These societal and economic challenges demonstrate an urgent need for more effective and efficient processes of governance and policymaking, therewith specifically addressing crisis management and economic/welfare impact reduction.

Therefore, investing in the exploitation of innovative information and commu- nication technology (ICT) in the support of good governance and policy modeling has become a major effort of the European Union to position itself and its Member States well in the global digital economy. In this realm, the European Union has laid out clear strategic policy objectives for 2020 in the Europe 2020 strategy1: In a changing world, we want the EU to become a smart, sustainable, and inclusive economy. These three mutually reinforcing priorities should help the EU and the Member States deliver high levels of employment, productivity, and social cohesion. Concretely, the Union has set five ambitious objectives—on employment, innovation, education, social inclusion, and climate/energy—to be reached by 2020. Along with this, Europe 2020 has established four priority areas—smart growth, sustainable growth, inclusive growth, and later added: A strong and effective system of eco- nomic governance—designed to help Europe emerge from the crisis stronger and to coordinate policy actions between the EU and national levels.

To specifically support European research in strengthening capacities, in overcom- ing fragmented research in the field of policymaking, and in advancing solutions for

1 Europe 2020 http://ec.europa.eu/europe2020/index_en.htm

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vi Preface

ICT supported governance and policy modeling, the European Commission has co- funded an international support action called eGovPoliNet2. The overall objective of eGovPoliNet was to create an international, cross-disciplinary community of re- searchers working on ICT solutions for governance and policy modeling. In turn, the aim of this community was to advance and sustain research and to share the insights gleaned from experiences in Europe and globally. To achieve this, eGovPo- liNet established a dialogue, brought together experts from distinct disciplines, and collected and analyzed knowledge assets (i.e., theories, concepts, solutions, findings, and lessons on ICT solutions in the field) from different research disciplines. It built on case material accumulated by leading actors coming from distinct disciplinary backgrounds and brought together the innovative knowledge in the field. Tools, meth- ods, and cases were drawn from the academic community, the ICT sector, specialized policy consulting firms as well as from policymakers and governance experts. These results were assembled in a knowledge base and analyzed in order to produce com- parative analyses and descriptions of cases, tools, and scientific approaches to enrich a common knowledge base accessible via www.policy-community.eu.

This book, entitled “Policy Practice and Digital Science—Integrating Complex Systems, Social Simulation, and Public Administration in Policy Research,” is one of the exciting results of the activities of eGovPoliNet—fusing community building activities and activities of knowledge analysis. It documents findings of comparative analyses and brings in experiences of experts from academia and from case descrip- tions from all over the globe. Specifically, it demonstrates how the explosive growth in data, computational power, and social media creates new opportunities for policy- making and research. The book provides a first comprehensive look on how to take advantage of the development in the digital world with new approaches, concepts, instruments, and methods to deal with societal and computational complexity. This requires the knowledge traditionally found in different disciplines including public administration, policy analyses, information systems, complex systems, and com- puter science to work together in a multidisciplinary fashion and to share approaches. This book provides the foundation for strongly multidisciplinary research, in which the various developments and disciplines work together from a comprehensive and holistic policymaking perspective. A wide range of aspects for social and professional networking and multidisciplinary constituency building along the axes of technol- ogy, participative processes, governance, policy modeling, social simulation, and visualization are tackled in the 19 papers.

With this book, the project makes an effective contribution to the overall objec- tives of the Europe 2020 strategy by providing a better understanding of different approaches to ICT enabled governance and policy modeling, and by overcoming the fragmented research of the past. This book provides impressive insights into various theories, concepts, and solutions of ICT supported policy modeling and how stake- holders can be more actively engaged in public policymaking. It draws conclusions

2 eGovPoliNet is cofunded under FP 7, Call identifier FP7-ICT-2011-7, URL: www.policy- community.eu

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Preface vii

of how joint multidisciplinary research can bring more effective and resilient find- ings for better predicting dramatic changes and negative trends in our economies and societies.

It is my great pleasure to provide the preface to the book resulting from the eGovPoliNet project. This book presents stimulating research by researchers coming from all over Europe and beyond. Congratulations to the project partners and to the authors!—Enjoy reading!

Thanassis Chrissafis Project officer of eGovPoliNet European Commission DG CNECT, Excellence in Science, Digital Science

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Contents

1 Introduction to Policy-Making in the Digital Age . . . . . . . . . . . . . . . . . 1 Marijn Janssen and Maria A. Wimmer

2 Educating Public Managers and Policy Analysts in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Christopher Koliba and Asim Zia

3 The Quality of Social Simulation: An Example from Research Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Petra Ahrweiler and Nigel Gilbert

4 Policy Making and Modelling in a Complex World . . . . . . . . . . . . . . . . 57 Wander Jager and Bruce Edmonds

5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Erik Pruyt

6 Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making: A Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter Davis and Petra Ahrweiler

7 A Comparative Analysis of Tools and Technologies for Policy Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee and David Price

8 Value Sensitive Design of Complex Product Systems . . . . . . . . . . . . . . . 157 Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers, Paulier Herder and Jeroen van den Hoven

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x Contents

9 Stakeholder Engagement in Policy Development: Observations and Lessons from International Experience . . . . . . . . . . . . . . . . . . . . . . 177 Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink and Catherine Gerald Mkude

10 Values in Computational Models Revalued . . . . . . . . . . . . . . . . . . . . . . . 205 Rebecca Moody and Lasse Gerrits

11 The Psychological Drivers of Bureaucracy: Protecting the Societal Goals of an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Tjeerd C. Andringa

12 Active and Passive Crowdsourcing in Government . . . . . . . . . . . . . . . . 261 Euripidis Loukis and Yannis Charalabidis

13 Management of Complex Systems: Toward Agent-Based Gaming for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Wander Jager and Gerben van der Vegt

14 The Role of Microsimulation in the Development of Public Policy . . . 305 Roy Lay-Yee and Gerry Cotterell

15 Visual Decision Support for Policy Making: Advancing Policy Analysis with Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli, Stefano Bragaglia, Federico Chesani, Michela Milano and Jörn Kohlhammer

16 Analysis of Five Policy Cases in the Field of Energy Policy . . . . . . . . . 355 Dominik Bär, Maria A.Wimmer, Jozef Glova, Anastasia Papazafeiropoulou and Laurence Brooks

17 Challenges to Policy-Making in Developing Countries and the Roles of Emerging Tools, Methods and Instruments: Experiences from Saint Petersburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Dmitrii Trutnev, Lyudmila Vidyasova and Andrei Chugunov

18 Sustainable Urban Development, Governance and Policy: A Comparative Overview of EU Policies and Projects . . . . . . . . . . . . . 393 Diego Navarra and Simona Milio

19 eParticipation, Simulation Exercise and Leadership Training in Nigeria: Bridging the Digital Divide . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Tanko Ahmed

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Contributors

Tanko Ahmed National Institute for Policy and Strategic Studies (NIPSS), Jos, Nigeria

Petra Ahrweiler EA European Academy of Technology and Innovation Assess- ment GmbH, Bad Neuenahr-Ahrweiler, Germany

Tjeerd C. Andringa University College Groningen, Institute of Artificial In- telligence and Cognitive Engineering (ALICE), University of Groningen, AB, Groningen, the Netherlands

Tina Balke University of Surrey, Surrey, UK

Dominik Bär University of Koblenz-Landau, Koblenz, Germany

Cees van Beers Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Stefano Bragaglia University of Bologna, Bologna, Italy

Laurence Brooks Brunel University, Uxbridge, UK

Yannis Charalabidis University of the Aegean, Samos, Greece

Federico Chesani University of Bologna, Bologna, Italy

Andrei Chugunov ITMO University, St. Petersburg, Russia

Gerry Cotterell Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Auckland, New Zealand

Jens Dambruch Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Peter Davis Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Auckland, New Zealand

Sharon Dawes Center for Technology in Government, University at Albany, Albany, New York, USA

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xii Contributors

Zamira Dzhusupova Department of PublicAdministration and Development Man- agement, United Nations Department of Economic and Social Affairs (UNDESA), NewYork, USA

Bruce Edmonds Manchester Metropolitan University, Manchester, UK

Theo Fens Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Marco Gavanelli University of Ferrara, Ferrara, Italy

Lasse Gerrits Department of Public Administration, Erasmus University Rotterdam, Rotterdam, The Netherlands

Nigel Gilbert University of Surrey, Guildford, UK

Jozef Glova Technical University Kosice, Kosice, Slovakia

Natalie Helbig Center for Technology in Government, University at Albany, Albany, New York, USA

Paulier Herder Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Jeroen van den Hoven Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Wander Jager Groningen Center of Social Complexity Studies, University of Groningen, Groningen, The Netherlands

Marijn Janssen Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Geerten van de Kaa Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Eleni Kamateri Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece

Bram Klievink Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands

Jörn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD, Darmstadt, Germany

Christopher Koliba University of Vermont, Burlington, VT, USA

Michel Krämer Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Roy Lay-Yee Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Auckland, New Zealand

Deirdre Lee INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland

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Contributors xiii

Andreas Ligtvoet Faculty of Technology, Policy, and Management, Delft Univer- sity of Technology, Delft, The Netherlands

Euripidis Loukis University of the Aegean, Samos, Greece

Dragana Majstorovic University of Koblenz-Landau, Koblenz, Germany

Michela Milano University of Bologna, Bologna, Italy

Simona Milio London School of Economics, Houghton Street, London, UK

Catherine Gerald Mkude Institute for IS Research, University of Koblenz-Landau, Koblenz, Germany

Rebecca Moody Department of Public Administration, Erasmus University Rotterdam, Rotterdam, The Netherlands

Diego Navarra Studio Navarra, London, UK

Adegboyega Ojo INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland

Eleni Panopoulou Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece

Anastasia Papazafeiropoulou Brunel University, Uxbridge, UK

David Price Thoughtgraph Ltd, Somerset, UK

Erik Pruyt Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands; Netherlands Institute for Advanced Study, Wassenaar, The Netherlands

Tobias Ruppert Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Efthimios Tambouris Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece; University of Macedonia, Thessaloniki, Greece

Konstantinos Tarabanis Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece; University of Macedonia, Thessa- loniki, Greece

Dmitrii Trutnev ITMO University, St. Petersburg, Russia

Gerben van derVegt Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands

Lyudmila Vidyasova ITMO University, St. Petersburg, Russia

Maria A. Wimmer University of Koblenz-Landau, Koblenz, Germany

Asim Zia University of Vermont, Burlington, VT, USA

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Chapter 1 Introduction to Policy-Making in the Digital Age

Marijn Janssen and Maria A. Wimmer

We are running the 21st century using 20th century systems on top of 19th century political structures. . . . John Pollock, contributing editor MIT technology review

Abstract The explosive growth in data, computational power, and social media creates new opportunities for innovating governance and policy-making. These in- formation and communications technology (ICT) developments affect all parts of the policy-making cycle and result in drastic changes in the way policies are devel- oped. To take advantage of these developments in the digital world, new approaches, concepts, instruments, and methods are needed, which are able to deal with so- cietal complexity and uncertainty. This field of research is sometimes depicted as e-government policy, e-policy, policy informatics, or data science. Advancing our knowledge demands that different scientific communities collaborate to create practice-driven knowledge. For policy-making in the digital age disciplines such as complex systems, social simulation, and public administration need to be combined.

1.1 Introduction

Policy-making and its subsequent implementation is necessary to deal with societal problems. Policy interventions can be costly, have long-term implications, affect groups of citizens or even the whole country and cannot be easily undone or are even irreversible. New information and communications technology (ICT) and models can help to improve the quality of policy-makers. In particular, the explosive growth in data, computational power, and social media creates new opportunities for in- novating the processes and solutions of ICT-based policy-making and research. To

M. Janssen (�) Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands e-mail: [email protected]

M. A. Wimmer University of Koblenz-Landau, Koblenz, Germany

© Springer International Publishing Switzerland 2015 1 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_1

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2 M. Janssen and M. A. Wimmer

take advantage of these developments in the digital world, new approaches, con- cepts, instruments, and methods are needed, which are able to deal with societal and computational complexity. This requires the use of knowledge which is traditionally found in different disciplines, including (but not limited to) public administration, policy analyses, information systems, complex systems, and computer science. All these knowledge areas are needed for policy-making in the digital age. The aim of this book is to provide a foundation for this new interdisciplinary field in which various traditional disciplines are blended.

Both policy-makers and those in charge of policy implementations acknowledge that ICT is becoming more and more important and is changing the policy-making process, resulting in a next generation policy-making based on ICT support. The field of policy-making is changing driven by developments such as open data, computa- tional methods for processing data, opinion mining, simulation, and visualization of rich data sets, all combined with public engagement, social media, and participatory tools. In this respect Web 2.0 and even Web 3.0 point to the specific applications of social networks and semantically enriched and linked data which are important for policy-making. In policy-making vast amount of data are used for making predictions and forecasts. This should result in improving the outcomes of policy-making.

Policy-making is confronted with an increasing complexity and uncertainty of the outcomes which results in a need for developing policy models that are able to deal with this. To improve the validity of the models policy-makers are harvesting data to generate evidence. Furthermore, they are improving their models to capture complex phenomena and dealing with uncertainty and limited and incomplete information. Despite all these efforts, there remains often uncertainty concerning the outcomes of policy interventions. Given the uncertainty, often multiple scenarios are developed to show alternative outcomes and impact. A condition for this is the visualization of policy alternatives and its impact. Visualization can ensure involvement of nonexpert and to communicate alternatives. Furthermore, games can be used to let people gain insight in what can happen, given a certain scenario. Games allow persons to interact and to experience what happens in the future based on their interventions.

Policy-makers are often faced with conflicting solutions to complex problems, thus making it necessary for them to test out their assumptions, interventions, and resolutions. For this reason policy-making organizations introduce platforms facili- tating policy-making and citizens engagements and enabling the processing of large volumes of data. There are various participative platforms developed by government agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010; Welch 2012). Platforms can be viewed as a kind of regulated environment that enable developers, users, and others to interact with each other, share data, services, and applications, enable gov- ernments to more easily monitor what is happening and facilitate the development of innovative solutions (Janssen and Estevez 2013). Platforms should provide not only support for complex policy deliberations with citizens but should also bring to- gether policy-modelers, developers, policy-makers, and other stakeholders involved in policy-making. In this way platforms provide an information-rich, interactive

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1 Introduction to Policy-Making in the Digital Age 3

environment that brings together relevant stakeholders and in which complex phe- nomena can be modeled, simulated, visualized, discussed, and even the playing of games can be facilitated.

1.2 Complexity and Uncertainty in Policy-Making

Policy-making is driven by the need to solve societal problems and should result in interventions to solve these societal problems. Examples of societal problems are unemployment, pollution, water quality, safety, criminality, well-being, health, and immigration. Policy-making is an ongoing process in which issues are recognized as a problem, alternative courses of actions are formulated, policies are affected, implemented, executed, and evaluated (Stewart et al. 2007). Figure 1.1 shows the typical stages of policy formulation, implementation, execution, enforcement, and evaluation. This process should not be viewed as linear as many interactions are necessary as well as interactions with all kind of stakeholders. In policy-making processes a vast amount of stakeholders are always involved, which makes policy- making complex.

Once a societal need is identified, a policy has to be formulated. Politicians, members of parliament, executive branches, courts, and interest groups may be involved in these formulations. Often contradictory proposals are made, and the impact of a proposal is difficult to determine as data is missing, models cannot

citizens

Policy formulation

Policy implementation

Policy execution

Policy enforcement and

evaluation

politicians

Policy- makers

Administrative organizations

businesses

Inspection and enforcement agencies

experts

Fig. 1.1 Overview of policy cycle and stakeholders

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4 M. Janssen and M. A. Wimmer

capture the complexity, and the results of policy models are difficult to interpret and even might be interpreted in an opposing way. This is further complicated as some proposals might be good but cannot be implemented or are too costly to implement. There is a large uncertainty concerning the outcomes.

Policy implementation is done by organizations other than those that formulated the policy. They often have to interpret the policy and have to make implemen- tation decisions. Sometimes IT can block quick implementation as systems have to be changed. Although policy-making is the domain of the government, private organizations can be involved to some extent, in particular in the execution of policies.

Once all things are ready and decisions are made, policies need to be executed. During the execution small changes are typically made to fine tune the policy formu- lation, implementation decisions might be more difficult to realize, policies might bring other benefits than intended, execution costs might be higher and so on. Typ- ically, execution is continually changing. Evaluation is part of the policy-making process as it is necessary to ensure that the policy-execution solved the initial so- cietal problem. Policies might become obsolete, might not work, have unintended affects (like creating bureaucracy) or might lose its support among elected officials, or other alternatives might pop up that are better.

Policy-making is a complex process in which many stakeholders play a role. In the various phases of policy-making different actors are dominant and play a role. Figure 1.1 shows only some actors that might be involved, and many of them are not included in this figure. The involvement of so many actors results in fragmentation and often actors are even not aware of the decisions made by other actors. This makes it difficult to manage a policy-making process as each actor has other goals and might be self-interested.

Public values (PVs) are a way to try to manage complexity and give some guidance. Most policies are made to adhere to certain values. Public value management (PVM) represents the paradigm of achieving PVs as being the primary objective (Stoker 2006). PVM refers to the continuous assessment of the actions performed by public officials to ensure that these actions result in the creation of PV (Moore 1995). Public servants are not only responsible for following the right procedure, but they also have to ensure that PVs are realized. For example, civil servants should ensure that garbage is collected. The procedure that one a week garbage is collected is secondary. If it is necessary to collect garbage more (or less) frequently to ensure a healthy environment then this should be done. The role of managers is not only to ensure that procedures are followed but they should be custodians of public assets and maximize a PV.

There exist a wide variety of PVs (Jørgensen and Bozeman 2007). PVs can be long-lasting or might be driven by contemporary politics. For example, equal access is a typical long-lasting value, whereas providing support for students at universities is contemporary, as politicians might give more, less, or no support to students. PVs differ over times, but also the emphasis on values is different in the policy-making cycle as shown in Fig. 1.2. In this figure some of the values presented by Jørgensen and Bozeman (2007) are mapped onto the four policy-making stages. Dependent on the problem at hand other values might play a role that is not included in this figure.

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1 Introduction to Policy-Making in the Digital Age 5

Policy formulation

Policy implementation

Policy execution

Policy enforcement

and evaluation

efficiency

efficiency

accountability

transparancy

responsiveness

public interest

will of the people

listening

citizen involvement

evidence-based

protection of individual rights

accountability

transparancy

evidence-based

equal access

balancing of interests

robust

honesty fair

timelessness

reliable

flexible

fair

Fig. 1.2 Public values in the policy cycle

Policy is often formulated by politicians in consultation with experts. In the PVM paradigm, public administrations aim at creating PVs for society and citizens. This suggests a shift from talking about what citizens expect in creating a PV. In this view public officials should focus on collaborating and creating a dialogue with citizens in order to determine what constitutes a PV.

1.3 Developments

There is an infusion of technology that changes policy processes at both the individual and group level. There are a number of developments that influence the traditional way of policy-making, including social media as a means to interact with the public (Bertot et al. 2012), blogs (Coleman and Moss 2008), open data (Janssen et al. 2012; Zuiderwijk and Janssen 2013), freedom of information (Burt 2011), the wisdom of the crowds (Surowiecki 2004), open collaboration and transparency in policy simulation (Wimmer et al. 2012a, b), agent-based simulation and hybrid modeling techniques (Koliba and Zia 2012) which open new ways of innovative policy-making. Whereas traditional policy-making is executed by experts, now the public is involved to fulfill requirements of good governance according to open government principles.

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6 M. Janssen and M. A. Wimmer

Also, the skills and capabilities of crowds can be explored and can lead to better and more transparent democratic policy decisions. All these developments can be used for enhancing citizen’s engagement and to involve citizens better in the policy-making process. We want to emphasize three important developments.

1.3.1 The Availability of Big and Open Linked Data (BOLD)

Policy-making heavily depends on data about existing policies and situations to make decisions. Both public and private organizations are opening their data for use by others. Although information could be requested for in the past, governments have changed their strategy toward actively publishing open data in formats that are readily and easily accessible (for example, European_Commission 2003; Obama 2009). Multiple perspectives are needed to make use of and stimulate new practices based on open data (Zuiderwijk et al. 2014). New applications and innovations can be based solely on open data, but often open data are enriched with data from other sources. As data can be generated and provided in huge amounts, specific needs for processing, curation, linking, visualization, and maintenance appear. The latter is often denoted with big data in which the value is generated by combining different datasets (Janssen et al. 2014). Current advances in processing power and memory allows for the processing of a huge amount of data. BOLD allows for analyzing policies and the use of these data in models to better predict the effect of new policies.

1.3.2 Rise of Hybrid Simulation Approaches

In policy implementation and execution, many actors are involved and there are a huge number of factors influencing the outcomes; this complicates the prediction of the policy outcomes. Simulation models are capable of capturing the interdepen- dencies between the many factors and can include stochastic elements to deal with the variations and uncertainties. Simulation is often used in policy-making as an instrument to gain insight in the impact of possible policies which often result in new ideas for policies. Simulation allows decision-makers to understand the essence of a policy, to identify opportunities for change, and to evaluate the effect of pro- posed changes in key performance indicators (Banks 1998; Law and Kelton 1991). Simulation heavily depends on data and as such can benefit from big and open data.

Simulation models should capture the essential aspects of reality. Simulation models do not rely heavily on mathematical abstraction and are therefore suitable for modeling complex systems (Pidd 1992). Already the development of a model can raise discussions about what to include and what factors are of influence, in this way contributing to a better understanding of the situation at hand. Furthermore, experimentation using models allows one to investigate different settings and the influence of different scenarios in time on the policy outcomes.

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1 Introduction to Policy-Making in the Digital Age 7

The effects of policies are hard to predict and dealing with uncertainty is a key aspect in policy modeling. Statistical representation of real-world uncertainties is an integral part of simulation models (Law and Kelton 1991). The dynamics asso- ciated with many factors affecting policy-making, the complexity associated with the interdependencies between individual parts, and the stochastic elements asso- ciated with the randomness and unpredictable behavior of transactions complicates the simulations. Computer simulations for examining, explaining, and predicting so- cial processes and relationships as well as measuring the possible impact of policies has become an important part of policy-making. Traditional models are not able to address all aspects of complex policy interactions, which indicates the need for the development of hybrid simulation models consisting of a combinatory set of models built on different modeling theories (Koliba and Zia 2012). In policy-making it can be that multiple models are developed, but it is also possible to combine various types of simulation in a single model. For this purpose agent-based modeling and simulation approaches can be used as these allow for combining different type of models in a single simulation.

1.3.3 Ubiquitous User Engagement

Efforts to design public policies are confronted with considerable complexity, in which (1) a large number of potentially relevant factors needs to be considered, (2) a vast amount of data needs to be processed, (3) a large degree of uncertainty may exist, and (4) rapidly changing circumstances need to be dealt with. Utilizing computational methods and various types of simulation and modeling methods is often key to solving these kinds of problems (Koliba and Zia 2012). The open data and social media movements are making large quantities of new data available. At the same time enhancements in computational power have expanded the repertoire of instruments and tools available for studying dynamic systems and their interdependencies. In addition, sophisticated techniques for data gathering, visualization, and analysis have expanded our ability to understand, display, and disseminate complex, temporal, and spatial information to diverse audiences. These problems can only be addressed from a complexity science perspective and with a multitude of views and contributions from different disciplines. Insights and methods of complexity science should be applied to assist policy-makers as they tackle societal problems in policy areas such as environmental protection, economics, energy, security, or public safety and health. This demands user involvement which is supported by visualization techniques and which can be actively involved by employing (serious) games. These methods can show what hypothetically will happen when certain policies are implemented.

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8 M. Janssen and M. A. Wimmer

1.4 Combining Disciplines in E-government Policy-Making

This new field has been shaped using various names, including e-policy-making, digital policy science, computational intelligence, digital sciences, data sciences, and policy informatics (Dawes and Janssen 2013). The essence of this field it that it is

1. Practice-driven 2. Employs modeling techniques 3. Needs the knowledge coming from various disciplines 4. It focused on governance and policy-making

This field is practice-driven by taking as a starting point the public policy problem and defining what information is relevant for addressing the problem under study. This requires understanding of public administration and policy-making processes. Next, it is a key to determine how to obtain, store, retrieve, process, model, and interpret the results. This is the field of e-participation, policy-modeling, social simulation, and complex systems. Finally, it should be agreed upon how to present and disseminate the results so that other researchers, decision-makers, and practitioners can use it. This requires in-depth knowledge of practice, of structures of public administration and constitutions, political cultures, processes and culture and policy-making.

Based on the ideas, the FP7 project EgovPoliNet project has created an inter- national community in ICT solutions for governance and policy-modeling. The “policy-making 2.0” LinkedIn community has a large number of members from dif- ferent disciplines and backgrounds representing practice and academia. This book is the product of this project in which a large number of persons from various dis- ciplines and representing a variety of communities were involved. The book shows experiences and advances in various areas of policy-making. Furthermore, it contains comparative analyses and descriptions of cases, tools, and scientific approaches from the knowledge base created in this project. Using this book, practices and knowl- edge in this field is shared among researchers. Furthermore, this book provides the foundations in this area. The covered expertise include a wide range of aspects for so- cial and professional networking and multidisciplinary constituency building along the axes of technology, participative processes, governance, policy-modeling, social simulation, and visualization. In this way eGovPoliNet has advanced the way re- search, development, and practice is performed worldwide in using ICT solutions for governance and policy-modeling.

Although in Europe the term “e-government policy” or “e-policy,” for short, is often used to refer to these types of phenomena, whereas in the USA often the term “policy informatics” is used. This is similar to that in the USA the term digital government is often used, whereas in Europe the term e-government is preferred. Policy informatics is defined as “the study of how information is leveraged and efforts are coordinated towards solving complex public policy problems” (Krishnamurthy et al. 2013, p. 367). These authors view policy informatics as an emerging research space to navigate through the challenges of complex layers of uncertainty within

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1 Introduction to Policy-Making in the Digital Age 9

governance processes. Policy informatics community has created Listserv called Policy Informatics Network (PIN-L).

E-government policy-making is closely connected to “data science.” Data science is the ability to find answers from larger volumes of (un)structured data (Davenport and Patil 2012). Data scientists find and interpret rich data sources, manage large amounts of data, create visualizations to aid in understanding data, build mathemat- ical models using the data, present and communicate the data insights/findings to specialists and scientists in their team, and if required to a nonexpert audience. These are activities which are at the heart of policy-making.

1.5 Overview of Chapters

In total 54 different authors were involved in the creation of this book. Some chapters have a single author, but most of the chapters have multiple authors. The authors rep- resent a wide range of disciplines as shown in Fig. 1.2. The focus has been on targeting five communities that make up the core field for ICT-enabled policy-making. These communities include e-government/e-participation, information systems, complex systems, public administration, and policy research and social simulation. The com- bination of these disciplines and communities are necessary to tackle policy problems in new ways. A sixth category was added for authors not belonging to any of these communities, such as philosophy and economics. Figure 1.3 shows that the authors are evenly distributed among the communities, although this is less with the chapter. Most of the authors can be classified as belonging to the e-government/e-participation community, which is by nature interdisciplinary.

Foundation The first part deals with the foundations of the book. In their Chap. 2 Chris Koliba and Asim Zia start with a best practice to be incorporated in public administration educational programs to embrace the new developments sketched in

EGOV

IS

Complex Systems

Public Administration and Policy Research

Social Simulation

other (philosophy, energy, economics, )

Fig. 1.3 Overview of the disciplinary background of the authors

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10 M. Janssen and M. A. Wimmer

this chapter. They identify two types of public servants that need to be educated. The policy informatics include the savvy public manager and the policy informatics analyst. This chapter can be used as a basis to adopt interdisciplinary approaches and include policy informatics in the public administration curriculum.

Petra Ahrweiler and Nigel Gilbert discuss the need for the quality of simulation modeling in their Chap. 3. Developing simulation is always based on certain as- sumptions and a model is as good as the developer makes it. The user community is proposed to assess the quality of a policy-modeling exercise. Communicative skills, patience, willingness to compromise on both sides, and motivation to bridge the formal world of modelers and the narrative world of policy-makers are suggested as key competences. The authors argue that user involvement is necessary in all stages of model development.

Wander Jager and Bruce Edmonds argue that due to the complexity that many social systems are unpredictable by nature in their Chap. 4. They discuss how some insights and tools from complexity science can be used in policy-making. In particular they discuss the strengths and weaknesses of agent-based modeling as a way to gain insight in the complexity and uncertainty of policy-making.

In the Chap. 5, Erik Pruyt sketches the future in which different systems modeling schools and modeling methods are integrated. He shows that elements from policy analysis, data science, machine learning, and computer science need to be combined to deal with the uncertainty in policy-making. He demonstrates the integration of various modeling and simulation approaches and related disciplines using three cases.

Modeling approaches are compared in the Chap. 6 authored by Dragana Majs- torovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis,and Petra Ahrweiler. Like in the previous chapter they argue that none of the theories on its own is able to address all aspects of complex policy interactions, and the need for hybrid simulation models is advocated.

The next chapter is complimentary to the previous chapter and includes a com- parison of ICT tools and technologies. The Chap. 7 is authored by Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee, and David Price. This chapter can be used as a basis for tool selecting and includes visualization, argumentation, e-participation, opinion mining, simula- tion, persuasive, social network analysis, big data analytics, semantics, linked data tools, and serious games.

Social Aspects, Stakeholders and Values Although much emphasis is put on mod- eling efforts, the social aspects are key to effective policy-making. The role of values is discussed in the Chap. 8 authored by Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers, Paulien Herder, and Jeroen van den Hoven. Using the case of the design of smart meters in energy networks they argue that policy-makers would do well by not only addressing functional requirements but also by taking individual stakeholder and PVs into consideration.

In policy-making a wide range of stakeholders are involved in various stages of the policy-making process. Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink, and Catherine Gerald Mkude analyze five case studies of stakeholder

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1 Introduction to Policy-Making in the Digital Age 11

engagement in policy-making in their Chap. 9. Various engagement tools are dis- cussed and factors identified which support the effective use of particular tools and technologies.

The Chap. 10 investigates the role of values and trust in computational models in the policy process. This chapter is authored by Rebecca Moody and Lasse Gerrits. The authors found that a large diversity exists in values within the cases. By the authors important explanatory factors were found including (1) the role of the designer of the model, (2) the number of different actors (3) the level of trust already present, and (4) and the limited control of decision-makers over the models.

Bureaucratic organizations are often considered to be inefficient and not customer friendly. Tjeerd Andringa presents and discusses a multidisciplinary framework con- taining the drivers and causes of bureaucracy in the Chap. 11. He concludes that the reduction of the number of rules and regulations is important, but that motivating workers to understand their professional roles and to learn to oversee the impact of their activities is even more important.

Crowdsourcing has become an important policy instrument to gain access to expertise (“wisdom”) outside own boundaries. In the Chap. 12, Euripids Loukis and Yannis Charalabidis discuss Web 2.0 social media for crowdsourcing. Passive crowdsourcing exploits the content generated by users, whereas active crowdsourcing stimulates content postings and idea generation by users. Synergy can be created by combining both approaches. The results of passive crowdsourcing can be used for guiding active crowdsourcing to avoid asking users for similar types of input.

Policy, Collaboration and Games Agent-based gaming (ABG) is used as a tool to explore the possibilities to manage complex systems in the Chap. 13 by Wander Jager and Gerben van der Vegt. ABG allows for modeling a virtual and autonomous population in a computer game setting to exploit various management and leadership styles. In this way ABG contribute to the development of the required knowledge on how to manage social complex behaving systems.

Micro simulation focuses on modeling individual units and the micro-level pro- cesses that affect their development. The concepts of micro simulation are explained by Roy Lay-Yee and Gerry Cotterell in the Chap. 14. Micro simulation for pol- icy development is useful to combine multiple sources of information in a single contextualized model to answer “what if” questions on complex social phenomena.

Visualization is essential to communicate the model and the results to a variety of stakeholders. These aspects are discussed in the Chap. 15 by Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli, Stefano Bragaglia, Federico Chesani, Michela Milano, and Jörn Kohlhammer. They argue that despite the significance to use evidence in policy-making, this is seldom realized. Three case studies that have been conducted in two European research projects for policy- modeling are presented. In all the cases access for nonexperts to the computational models by information visualization technologies was realized.

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12 M. Janssen and M. A. Wimmer

Applications and Practices Different projects have been initiated to study the best suitable transition process towards renewable energy. In the Chap. 16 by Dominik Bär, Maria A. Wimmer, Jozef Glova, Anastasia Papazafeiropoulou,and Laurence Brooks five of these projects are analyzed and compared. They please for transferring models from one country to other countries to facilitate learning.

Lyudmila Vidyasova, Andrei Chugunov, and Dmitrii Trutnev present experiences from Russia in their Chap. 17. They argue that informational, analytical, and fore- casting activities for the processes of socioeconomic development are an important element in policy-making. The authors provide a brief overview of the history, the current state of the implementation of information processing techniques, and prac- tices for the purpose of public administration in the Russian Federation. Finally, they provide a range of recommendations to proceed.

Urban policy for sustainability is another important area which is directly linked to the first chapter in this section. In the Chap. 18, Diego Navarra and Simona Milio demonstrate a system dynamics model to show how urban policy and governance in the future can support ICT projects in order to reduce energy usage, rehabilitate the housing stock, and promote sustainability in the urban environment. This chapter contains examples of sustainable urban development policies as well as case studies.

In the Chap. 19, Tanko Ahmed discusses the digital divide which is blocking online participation in policy-making processes. Structuration, institutional and actor-network theories are used to analyze a case study of political zoning. The author recommends stronger institutionalization of ICT support and legislation for enhancing participation in policy-making and bridging the digital divide.

1.6 Conclusions

This book is the first comprehensive book in which the various development and disci- plines are covered from the policy-making perspective driven by ICT developments. A wide range of aspects for social and professional networking and multidisciplinary constituency building along the axes of technology, participative processes, gover- nance, policy-modeling, social simulation, and visualization are investigated. Policy- making is a complex process in which many stakeholders are involved. PVs can be used to guide policy-making efforts and to ensure that the many stakeholders have an understanding of the societal value that needs to be created. There is an infusion of technology resulting in changing policy processes and stakeholder involvement. Technologies like social media provides a means to interact with the public, blogs can be used to express opinions, big and open data provide input for evidence-based policy-making, the integration of various types of modeling and simulation tech- niques (hybrid models) can provide much more insight and reliable outcomes, gam- ing in which all kind of stakeholders are involved open new ways of innovative policy- making. In addition trends like the freedom of information, the wisdom of the crowds, and open collaboration changes the landscape further. The policy-making landscape is clearly changing and this demands a strong need for interdisciplinary research.

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1 Introduction to Policy-Making in the Digital Age 13

References

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Bertot JC, Jaeger PT, Hansen D (2012) The impact of polices on government social media usage: Issues, challenges, and recommendations. Gov Inform Q 29:30–40

Burt E (2011) Introduction to the freedom of information special edition: emerging perspectives, critical reflections, and the need for further research. Inform Polit 16(2):91–92.

Coleman S, Moss G (2008) Governing at a distance—politicians in the blogosphere. Inform Polit 12(1–2):7–20.

Davenport TH, Patil DJ (2012) Data scientist: the sexiest job of the 21st century. Harv Bus Rev 90(10):70–76

Dawes SS, Janssen M (2013) Policy informatics: addressing complex problems with rich data, com- putational tools, and stakeholder engagement. Paper presented at the 14th annual international conference on digital government research, Quebec City, Canada

De Reuver M, Stein S, Hampe F (2013) From eparticipation to mobile participation: designing a service platform and business model for mobile participation. Inform Polit 18(1):57–73

European_Commission (2003) Directive 2003/98/EC of the European Parliament and of the coun- cil of 17 November 2003 on the re-use of public sector information. http://ec.europa.eu/ information_society/policy/psi/rules/eu/index_en.htm. Accessed 12 Dec 2012

Janssen M, Estevez E (2013) Lean government and platform-based governance—doing more with less. Gov Inform Quert 30(suppl 1):S1–S8

Janssen M, CharalabidisY, Zuiderwijk A (2012) Benefits, adoption barriers and myths of open data and open government. Inform Syst Manage 29(4):258–268

Janssen M, Estevez E, Janowski T (2014) Interoperability in big, open, and linked data— organizational maturity, capabilities, and data portfolios. Computer 47(10):26–31

Jørgensen TB, Bozeman B (2007) Public values: an inventory. Adm Soc 39(3):354–381 Koliba C, Zia A (2012) Governance Informatics: using computer simulation models to deepen

situational awareness and governance design considerations policy informatics. MIT Press, Cambridge.

Krishnamurthy R, Bhagwatwar A, Johnston EW, Desouza KC (2013) A glimpse into policy in- formatics: the case of participatory platforms that generate synthetic empathy. Commun Assoc Inform Syst 33(Article 21):365–380.

Law AM, Kelton WD (1991) Simulation modeling and analysis 2nd ed. McGraw-Hill, New York Moore MH (1995) Creating public value: strategic management in government. Harvard University

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Pidd M (1992) Computer simulation in management science, 3rd ed. John Wiley, Chichester Slaviero C, Maciel C, Alencar F, Santana E, Souza P (2010) Designing a platform to facilitate

the development of virtual e-participation environments. Paper presented at the ICEGOV ’10 proceedings of the 4th international conference on theory and practice of electronic governance, Beijing

Stewart JJ, Hedge DM, Lester JP (2007) Public policy: an evolutionary approach 3rd edn. Cengage Learning, Wadsworth

Stoker G (2006) Public value management: a new narrative for networked governance? Am Rev Public Adm 3(1):41–57

Surowiecki J (2004) The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business economies, societies and nations. Doubleday

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Wimmer MA, Furdik K, Bicking M, Mach M, Sabol T, Butka P (2012a) Open collaboration in policy development: concept and architecture to integrate scenario development and formal policy modelling. In: Charalabidis Y, Koussouris S (eds) Empowering open and collaborative governance. Technologies and methods for online citizen engagement in public policy making. Springer, Berlin, pp 199–219

Wimmer MA, Scherer S, Moss S, Bicking M (2012b) Method and tools to support stakeholder engagement in policy development the OCOPOMO project. Int J Electron Gov Res (IJEGR) 8(3):98–119

Zuiderwijk A, Janssen M (2013) A coordination theory perspective to improve the use of open data in policy-making. Paper presented at the 12th conference on Electronic Government (EGOV), Koblenz

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Chapter 2 Educating Public Managers and Policy Analysts in an Era of Informatics

Christopher Koliba and Asim Zia

Abstract In this chapter, two ideal types of practitioners who may use or cre- ate policy informatics projects, programs, or platforms are introduced: the policy informatics-savvy public manager and the policy informatics analyst. Drawing from our experiences in teaching an informatics-friendly graduate curriculum, we dis- cuss the range of learning competencies needed for traditional public managers and policy informatics-oriented analysts to thrive in an era of informatics. The chapter begins by describing the two different types of students who are, or can be touched by, policy informatics-friendly competencies, skills, and attitudes. Competencies ranging from those who may be users of policy informatics and sponsors of policy informatics projects and programs to those analysts designing and executing policy informatics projects and programs will be addressed. The chapter concludes with an illustration of how one Master of Public Administration (MPA) program with a policy informatics-friendly mission, a core curriculum that touches on policy infor- matics applications, and a series of program electives that allows students to develop analysis and modeling skills, designates its informatics-oriented competencies.

2.1 Introduction

The range of policy informatics opportunities highlighted in this volume will require future generations of public managers and policy analysts to adapt to the oppor- tunities and challenges posed by big data and increasing computational modeling capacities afforded by the rapid growth in information technologies. It will be up to the field’s Master of Public Administration (MPA) and Master of Public Policy (MPP) programs to provide this next generation with the tools needed to harness the wealth of data, information, and knowledge increasingly at the disposal of public

C. Koliba (�) University of Vermont, 103 Morrill Hall, 05405 Burlington, VT, USA e-mail: [email protected]

A. Zia University of Vermont, 205 Morrill Hall, 05405 Burlington, VT, USA e-mail: [email protected]

© Springer International Publishing Switzerland 2015 15 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_2

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16 C. Koliba and A. Zia

administrators and policy analysts. In this chapter, we discuss the role of policy infor- matics in the development of present and future public managers and policy analysts. Drawing from our experiences in teaching an informatics-friendly graduate curricu- lum, we discuss the range of learning competencies needed for traditional public managers and policy informatics-oriented analysts to thrive in an era of informatics. The chapter begins by describing the two different types of students who are, or can be touched by, policy informatics-friendly competencies, skills, and attitudes. Com- petencies ranging from those who may be users of policy informatics and sponsors of policy informatics projects and programs to those analysts designing and executing policy informatics projects and programs will be addressed. The chapter concludes with an illustration of how one MPA program with a policy informatics-friendly mission, a core curriculum that touches on policy informatics applications, and a series of program electives that allows students to develop analysis and modeling skills, designates its informatics-oriented competencies.

2.2 Two Types of Practitioner Orientations to Policy Informatics

Drawn from our experience, we find that there are two “ideal types” of policy infor- matics practitioner, each requiring greater and greater levels of technical mastery of analytics techniques and approaches. These ideal types are: policy informatics-savvy public managers and policy informatics analysts.

A policy informatics-savvy public manager may take on one of two possible roles relative to policy informatics projects, programs, or platforms. They may play instru- mental roles in catalyzing and implementing informatics initiatives on behalf of their organizations, agencies, or institutions. In the manner, they may work with technical experts (analysts) to envision possible uses for data, visualizations, simulations, and the like. Public managers may also be in the role of using policy informatics projects, programs, or platforms. They may be in positions to use these initiatives to ground decision making, allocate resources, and otherwise guide the performance of their organizations.

A policy informatics analyst is a person who is positioned to actually execute a policy informatics initiative. They may be referred to as analysts, researchers, modelers, or programmers and provide the technical assistance needed to analyze databases, build and run models, simulations, and otherwise construct useful and effective policy informatics projects, programs, or platforms.

To succeed in either and both roles, managers and analysts will require a certain set of skills, knowledge, or competencies. Drawing on some of the prevailing literature and our own experiences, we lay out an initial list of potential competencies for consideration.

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 17

2.2.1 Policy Informatics-Savvy Public Managers

To successfully harness policy informatics, public managers will likely not need to know how to explicitly build models or manipulate big data. Instead, they will need to know what kinds of questions that policy informatics projects or programs can answer or not answer. They will need to know how to contract with and/or manage data managers, policy analysts, and modelers. They will need to be savvy consumers of data analysis and computational models, but not necessarily need to know how to technically execute them. Policy informatics projects, programs, and platforms are designed and executed in some ways, as any large-scale, complex project.

In writing about the stages of informatics project development using “big data,” DeSouza lays out project development along three stages: planning, execution, and postimplementation. Throughout the project life cycle, he emphasizes the role of understanding the prevailing policy and legal environment, the need to venture into coalition building, the importance of communicating the broader opportunities af- forded by the project, the need to develop performance indicators, and the importance of lining up adequate financial and human resources (2014).

Framing what traditional public managers need to know and do to effectively interface with policy informatics projects and programs requires an ability to be a “systems thinker,” an effective evaluator, a capacity to integrate informatics into performance and financial management systems, effective communication skills, and a capacity to draw on social media, information technology, and e-governance approaches to achieve common objectives. We briefly review each of these capacities below.

Systems Thinking Knowing the right kinds of questions that may be asked through policy informatics projects and programs requires public managers to possess a “sys- tems” view. Much has been written about the importance of “systems thinking” for public managers (Katz and Kahn 1978; Stacey 2001; Senge 1990; Korton 2001). Taking a systems perspective allows public managers to understand the relationship between the “whole” and the “parts.” Systems-oriented public managers will possess a level of situational awareness (Endsley 1995) that allows them to see and under- stand patterns of interaction and anticipate future events and orientations. Situational awareness allows public mangers to understand and evaluate where data are coming from, how best data are interpreted, and the kinds of assumptions being used in specific interpretations (Koliba et al. 2011). The concept of system thinking laid out here can be associated with the notion of transition management (Loorbach 2007).

Process Orientations to Public Policy The capacity to view the policy making and implementation process as a process that involves certain levels of coordination and conflict between policy actors is of critical importance for policy informatics- savvy public managers and analysts. Understanding how data are used to frame problems and policy solutions, how complex governance arrangements impact policy implementation (Koliba et al. 2010), and how data visualization can be used to

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18 C. Koliba and A. Zia

facilitate the setting of policy agendas and open policy windows (Kingdon 1984) is of critical importance for public management and policy analysts alike.

Research Methodologies Another basic competency needed for any public manager using policy informatics is a foundational understanding of research methods, par- ticularly quantitative reasoning and methodologies. A foundational understanding of data validity, analytical rigor and relevance, statistical significance, and the like are needed to be effective consumers of informatics. That said, traditional public man- agers should also be exposed to qualitative methods as well, refining their powers of observation, understanding how symbols, stories, and numbers are used to govern, and how data and data visualization and computer simulations play into these mental models.

Performance Management A key feature of systems thinking as applied to policy informatics is the importance of understanding how data and analysis are to be used and who the intended users of the data are (Patton 2008). The integration of policy informatics into strategic planning (Bryson 2011), performance management systems (Moynihan 2008), and ultimately woven into an organization’s capacity to learn, adapt, and evolve (Argyis and Schön 1996) are critically important in this vein. As policy informatics trends evolve, public managers will likely need to be exposed to uses of decision support tools, dashboards, and other computationally driven models and visualizations to support organizational performance.

Financial Management Since the first systemic budgeting systems were put in place, public managers have been urged to use the budgeting process as a planning and eval- uation tool (Willoughby 1918). This approach was formally codified in the 1960s with the planning–programming–budgeting (PPB) system with its focus on plan- ning, managerial, and operational control (Schick 1966) and later adopted into more contemporary approaches to budgeting (Caiden 1981). Using informative projects, programs, or platforms to make strategic resource allocation decisions is a necessary given and a capacity that effective public managers must master. Likewise, the pol- icy analyst will likely need to integrate financial resource flows and costs into their projects.

Collaborative and Cooperative Capacity Building The development and use of pol- icy informatics projects, programs, or platforms is rarely, if ever, undertaken as an individual, isolated endeavor. It is more likely that such initiatives will require interagency, interorganizational, or intergroup coordination. It is also likely that content experts will need to be partnered with analysts and programmers to com- plete tasks and execute designs. The public manager and policy analyst must both possess the capacity to facilitate collaborative management functions (O’Leary and Bingham 2009).

Basic Communication Skills This perhaps goes without saying, but the heart of any informatics project lies in the ability to effectively communicate findings and ideas through the analysis of data.

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 19

Social Media, Information Technology, and e-Governance Awareness A final com- petency concerns public managers’ capacity to deepen their understanding of how social media, Web-based tools, and related information technologies are being em- ployed to foster various e-government, e-governance, and related initiatives (Mergel 2013). Placing policy informatics projects and programs within the context of these larger trends and uses is something that public managers must be exposed to.

Within our MPA program, we have operationalized these capacities within a four- point rubric that outlines what a student needs to do to demonstrate meeting these standards. The rubric below highlights 8 of our program’s 18 capacities. All 18 of these capacities are situated under 1 of the 5 core competencies tied to the accred- itation standards of the Network of Schools of Public Affairs and Administration (NASPAA), the professional accrediting association in the USA, and increasingly in other countries as well, for MPA and MPP programs. A complete list of these core competencies and the 18 capacities nested under them are provided in Appendix of this chapter.

The eight capacities that we have singled out as being the most salient to the role of policy informatics in public administration are provided in Table 2.1. The rubric follows a four-point scale, ranging from “does not meet standard,” “approaches standard,” “meets standard,” and “exceeds standard.”

2.2.2 Policy Informatics Analysts

A second type of practitioner to be considered is what we are referring to as a “policy informatics analyst.” When considering the kinds of competencies that policy infor- matics analysts need to be successful, we first assume that the basic competencies outlined in the prior section apply here as well. In other words, effective policy in- formatics analysts must be systems thinkers in order to place data and their analysis into context, be cognizant of current uses of decision support systems (and related platforms) to enable organizational learning, performance, and strategic planning, and possess an awareness of e-governance and e-government initiatives and how they are transforming contemporary public management and policy planning practices. In addition, policy analysts must possess a capacity to understand policy systems: How policies are made and implemented? This baseline understanding can then be used to consider the placement, purpose, and design of policy informatics projects or programs. We lay out more specific analyst capacities below.

Advanced Research Methods of Information Technology Applications In many in- stances, policy informatics analysts will need to move beyond meeting the standard. This is particularly true in the area of exceeding the public manager standards for re- search methods and utilization of information technology. It is assumed that effective policy informatics analysts will have a strong foundation in quantitative methodolo- gies and applications. To obtain these skills, policy analysts will need to move beyond basic surveys of research methods into more advanced research methods curriculum.

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20 C. Koliba and A. Zia

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up on

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,a nd

to un

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pr ob

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di ff

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pl oy

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pr ob

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re al

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m m

as .C

an ar

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so ci

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se ar

ch m

et ho

ds to

a fo

cu s

ar ea

.C an

ex pl

ai n

w hy

it is

im po

rt an

tt o

un de

rt ak

e pr

og ra

m or

pr oj

ec t

ev al

ua tio

n, bu

tp os

se ss

es lim

ite d

ca pa

ci ty

to ac

tu al

ly ca

rr yi

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ou t

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on st

ra te

s a

ca pa

ci ty

to em

pl oy

su rv

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nt er

vi ew

,o r

ot he

r so

ci al

re se

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m et

ho ds

to a

fo cu

s ar

ea an

d an

un de

rs ta

nd in

g of

ho w

su ch

da ta

an d

an al

ys is

ar e

us ef

ul in

ad m

in is

tr at

iv e

pr ac

tic e.

C an

pr ov

id e

a ra

tio na

le fo

r un

de rt

ak in

g pr

og ra

m /p

ro je

ct

C an

pr ov

id e

a pi

ec e

of or

ig in

al an

al ys

is of

an ob

se rv

ed ph

en om

en on

em pl

oy in

g on

e qu

al ita

tiv e

or qu

an tit

at iv

e m

et ho

do lo

gy ef

fe ct

iv el

y. Po

ss es

se s

ca pa

ci ty

to co

m m

is si

on a

pi ec

e of

or ig

in al

re se

ar ch

.C an

pr ov

id e

a de

ta ile

d ac

co un

tf or

ho w

a

D em

on st

ra te

s th

e ca

pa ci

ty to

un de

rt ak

e an

in de

pe nd

en t

re se

ar ch

ag en

da th

ro ug

h em

pl oy

in g

on e

or m

or e

so ci

al re

se ar

ch m

et ho

ds ar

ou nd

a to

pi c

of st

ud y

of im

po rt

an ce

to pu

bl ic

ad m

in is

tr at

io n.

C an

de m

on st

ra te

th e

su cc

es sf

ul ex

ec ut

io n

of a

pr og

ra m

or

[email protected]

2 Educating Public Managers and Policy Analysts in an Era of Informatics 21

Ta bl

e 2.

1 (c

on tin

ue d)

C ap

ac ity

D oe

s no

tm ee

ts ta

nd ar

d A

pp ro

ac he

s st

an da

rd M

ee ts

st an

da rd

E xc

ee ds

st an

da rd

ev al

ua tio

n an

d ex

pl ai

n w

ha tt

he po

ss ib

le go

al s

an d

ou tc

om es

of su

ch an

ev al

ua tio

n m

ig ht

be

pr og

ra m

or pr

oj ec

te va

lu at

io n

pr oj

ec ts

ho ul

d be

st ru

ct ur

ed w

ith in

th e

co nt

ex to

f a

sp ec

ifi c

pr og

ra m

or pr

oj ec

t

pr oj

ec te

va lu

at io

n or

th e

su cc

es sf

ul ut

ili za

tio n

of a

pr og

ra m

or pr

oj ec

te va

lu at

io n

to im

pr ov

e ad

m in

is tr

at iv

e pr

ac tic

e

C ap

ac it

y to

ap pl

y so

un d

pe rf

or m

an ce

m ea

su re

m en

ta nd

m an

ag em

en tp

ra ct

ic es

C an

pr ov

id e

an ex

pl an

at io

n of

w hy

pe rf

or m

an ce

go al

s an

d m

ea su

re s

ar e

im po

rt an

ti n

pu bl

ic ad

m in

is tr

at io

n, bu

t ca

nn ot

ap pl

y th

is re

as on

in g

to sp

ec ifi

c co

nt ex

ts

C an

id en

tif y

th e

pe rf

or m

an ce

m an

ag em

en tc

on si

de ra

tio ns

fo r

a pa

rt ic

ul ar

si tu

at io

n or

co nt

ex t,

bu th

as lim

ite d

ca pa

ci ty

to ev

al ua

te th

e ef

fe ct

iv en

es s

of pe

rf or

m an

ce m

an ag

em en

t sy

st em

s

C an

id en

tif y

an d

an al

yz e

pe rf

or m

an ce

m an

ag em

en t

sy st

em s,

ne ed

s, an

d em

er gi

ng op

po rt

un iti

es w

ith in

a sp

ec ifi

c or

ga ni

za tio

n or

ne tw

or k

C an

pr ov

id e

ne w

in si

gh ts

in to

th e

pe rf

or m

an ce

m an

ag em

en t

ch al

le ng

es fa

ci ng

an or

ga ni

za tio

n or

ne tw

or k,

an d

su gg

es ta

lte rn

at iv

e de

si gn

an d

m ea

su re

m en

ts ce

na ri

os

C ap

ac it

y to

ap pl

y so

un d

fin an

ci al

pl an

ni ng

an d

fis ca

l re

sp on

si bi

li ty

C an

id en

tif y

w hy

bu dg

et in

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lm an

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en t

pr ac

tic es

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t ca

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an d/

or if

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tic es

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be in

g us

ed w

ith in

sp ec

ifi c

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lp la

nn in

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dg et

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pr ac

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ul ar

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as lim

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to ev

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fin an

ci al

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ag em

en ts

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m

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id en

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an al

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fin an

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m an

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s, an

d em

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un iti

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ith in

a sp

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pr ov

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in si

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th e

fin an

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m an

ag em

en t

ch al

le ng

es fa

ci ng

an or

ga ni

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n or

ne tw

or k,

an d

su gg

es ta

lte rn

at iv

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si gn

an d

bu dg

et in

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en ar

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ac it

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ac hi

ev e

co op

er at

io n

th ro

ug h

pa rt

ic ip

at or

y pr

ac ti

ce s

C an

ex pl

ai n

w hy

it is

im po

rt an

tf or

pu bl

ic ad

m in

is tr

at or

s to

be op

en an

d re

sp on

si ve

pr ac

tit io

ne rs

in a

va gu

e or

ab st

ra ct

w ay

,b ut

ca nn

ot pr

ov id

e sp

ec ifi

c ex

pl an

at io

ns or

ju st

ifi ca

tio ns

ap pl

ie d

to pa

rt ic

ul ar

co nt

ex ts

C an

id en

tif y

in st

an ce

s in

sp ec

ifi c

ca se

s or

co nt

ex ts

w he

re a

pu bl

ic ad

m in

is tr

at or

de m

on st

ra te

d or

fa ile

d to

de m

on st

ra te

in cl

us iv

e pr

ac tic

es

C an

de m

on st

ra te

ho w

in cl

us iv

e pr

ac tic

es an

d co

nfl ic

t m

an ag

em en

tl ea

ds to

co op

er at

io n

fo r

fo rm

in g

co al

iti on

s an

d co

lla bo

ra tiv

e pr

ac tic

es

C an

or ch

es tr

at e

an y

of th

e fo

llo w

in g:

co al

iti on

bu ild

in g

ac ro

ss un

its ,o

rg an

iz at

io ns

,o r

in st

itu tio

ns ,e

ff ec

tiv e

te am

w or

k, an

d/ or

co nfl

ic t

m an

ag em

en t

[email protected]

22 C. Koliba and A. Zia

Ta bl

e 2.

1 (c

on tin

ue d)

C ap

ac ity

D oe

s no

tm ee

ts ta

nd ar

d A

pp ro

ac he

s st

an da

rd M

ee ts

st an

da rd

E xc

ee ds

st an

da rd

C ap

ac it

y to

un de

rt ak

e hi

gh qu

al it

y or

al ,

w ri

tt en

co m

m un

ic at

io n

D em

on st

ra te

s so

m e

ab ili

ty to

ex pr

es s

id ea

s ve

rb al

ly an

d in

w ri

tin g.

L ac

ks co

ns is

te nt

ca pa

ci ty

to pr

es en

ta nd

w ri

te

Po ss

es se

s th

e ca

pa ci

ty to

w ri

te do

cu m

en ts

th at

ar e

fr ee

of gr

am m

at ic

al er

ro rs

an d

ar e

or ga

ni ze

d in

a cl

ea r

an d

ef fic

ie nt

m an

ne r.

Po ss

es se

s th

e ca

pa ci

ty to

pr es

en ti

de as

in a

pr of

es si

on al

m an

ne r.

Su ff

er s

fr om

a la

ck of

co ns

is te

nc y

in th

e pr

es en

ta tio

n of

m at

er ia

la nd

ex pr

es si

on or

or ig

in al

id ea

s an

d co

nc ep

ts

Is ca

pa bl

e of

co ns

is te

nt ly

ex pr

es si

ng id

ea s

ve rb

al ly

an d

in w

ri tin

g in

a pr

of es

si on

al m

an ne

r th

at co

m m

un ic

at es

m es

sa ge

s to

in te

nd ed

au di

en ce

s

C an

de m

on st

ra te

so m

e in

st an

ce s

in w

hi ch

ve rb

al an

d w

ri tte

n co

m m

un ic

at io

n ha

s pe

rs ua

de d

ot he

rs to

ta ke

ac tio

n

C ap

ac it

y to

un de

rt ak

e hi

gh qu

al it

y el

ec tr

on ic

al ly

m ed

ia te

d co

m m

un ic

at io

n an

d ut

il iz

e in

fo rm

at io

n sy

st em

s an

d m

ed ia

to ad

va nc

e ob

je ct

iv es

C an

ex pl

ai n

w hy

in fo

rm at

io n

te ch

no lo

gy is

im po

rt an

tt o

co nt

em po

ra ry

w or

kp la

ce s

an d

pu bl

ic ad

m in

is tr

at io

n en

vi ro

nm en

ts .P

os se

ss es

di re

ct ex

pe ri

en ce

w ith

in fo

rm at

io n

te ch

no lo

gy ,b

ut lit

tle un

de rs

ta nd

in g

fo r

ho w

IT in

fo rm

s pr

of es

si on

al pr

ac tic

e

C an

id en

tif y

in st

an ce

s in

sp ec

ifi c

ca se

s or

co nt

ex tw

he re

a pu

bl ic

ad m

in is

tr at

or su

cc es

sf ul

ly or

un su

cc es

sf ul

ly de

m on

st ra

te d

a ca

pa ci

ty to

us e

IT to

fo st

er in

no va

tio n,

im pr

ov e

se rv

ic es

,o r

de ep

en ac

co un

ta bi

lit y.

A na

ly si

s at

th is

le ve

li s

re le

ga te

d to

de sc

ri pt

io ns

an d

th in

an al

ys is

C an

id en

tif y

ho w

IT im

pa ct

s w

or kp

la ce

s an

d pu

bl ic

po lic

y. C

an di

ag no

se pr

ob le

m s

as so

ci at

ed w

ith IT

to ol

s, pr

oc ed

ur es

,a nd

us es

D em

on st

ra te

s a

ca pa

ci ty

to vi

ew IT

in te

rm s

of sy

st em

s de

si gn

.I s

ca pa

bl e

of w

or ki

ng w

ith IT

pr of

es si

on al

s in

id en

tif yi

ng ar

ea s

of ne

ed fo

r IT

up gr

ad es

,I T

pr oc

ed ur

es ,

an d

IT us

es in

re al

se tti

ng

IT in

fo rm

at io

n te

ch no

lo gy

[email protected]

2 Educating Public Managers and Policy Analysts in an Era of Informatics 23

Competencies in advanced quantitative methods in which students learn to clean and manage large databases, perform advanced statistical tests, develop linear regression models to describe causal relationship, and the like are needed. Capacity to work across software platforms such as Excel, Statistical Package for the Social Sciences (SPSS), Analytica, and the like are important. Increasingly, the capacity to triangu- late different methods, including qualitative approaches such as interviews, focus groups, participant observations is needed.

Data Visualization and Design Not only must analysts be aware of how these meth- ods and decision support platforms may be used by practitioners but also they must know how to design and implement them. Therefore, we suggest that policy infor- matics analysts be exposed to design principles and how they may be applied to decision support systems, big data projects, and the like. Policy informatics analysts will need to understand and appreciate how data visualization techniques are being employed to “tell a story” through data.

Figure 2.1 provides an illustration of one student’s effort to visualize campaign donations to state legislatures from the gas-extraction (fracking) industry undertaken by a masters student, Jeffery Castle for a system analysis and strategic management class taught by Koliba.

Castle’s project demonstrates the power of data visualization to convey a central message drawing from existing databases. With a solid research methods background and exposure to visualization and design principles in class, he was able to develop an insightful policy informatics project.

Basic to Advanced Programming Language Skills Arguably, policy informatics ana- lysts will possess a capacity to visualize and present data in a manner that is accessible. Increasingly, web-based tools are being used to design user interfaces. Knowledge of JAVA and HTML are likely most helpful in these regards. In some instances, original programs and models will need to be written through the use of program- ming languages such as Python, R, C++, etc. The extent to which existing software programs, be they open source or proprietary, provide enough utility to execute pol- icy informatics projects, programs, or platforms is a continuing subject of debate within the policy informatics community. Exactly how much and to what extent spe- cific programming languages and software programs are needing to be mastered is a standing question. For the purposes of writing this chapter, we rely on our current baseline observations and encourage more discussion and debate about the range of competencies needed by successful policy analysts.

Basic to More Advanced Modeling Skills More advanced policy informatics analysts will employ computational modeling approaches that allow for the incorporation of more complex interactions between variables. These models may be used to capture systems as dynamic, emergent, and path dependent. The outputs of these models may allow for scenario testing through simulation (Koliba et al. 2011). With the advancement of modeling software, it is becoming easier for analysts to develop system dynamics models, agent-based models, and dynamic networks designed to simulate the features of complex adaptive systems. In addition, the ability to manage and store data and link or wrap databases is often necessary.

[email protected]

24 C. Koliba and A. Zia

Fig. 2.1 Campaign contributions to the Pennsylvania State Senate and party membership. The goal of this analysis is to develop a visualization tool to translate publically available campaign contribution information into an easily accessible, visually appealing, and interactive format. While campaign contribution data are filed and available to the public through the Pennsylvania Department of State, it is not easily synthesized. This analysis uses a publically available database that has been published on marcellusmoney.org. In order to visualize the data, a tool was used that allows for the creation of a Sankey diagram that is able to be manipulated and interacted within an Internet browser. A Sankey diagram visualizes the magnitude of flow between the nodes of a network (Castle 2014)

The ability of analysts to draw on a diverse array of methods and theoretical frameworks to envision and create models is of critical importance. Any potential policy informatics project, program, or platform will be enabled or constrained by the modeling logic in place. With a plurality of tools at one’s disposal, policy informatics analysts will be better positioned to design relevant and legitimate models.

[email protected]

2 Educating Public Managers and Policy Analysts in an Era of Informatics 25

Fig. 2.2 End-stage renal disease (ESRD) system dynamics population model. To provide clinicians and health care administrators with a greater understanding of the combined costs associated with the many critical care pathways associated with ESRD, a system dynamics model was designed to simulate the total expenses of ESRD treatment for the USA, as well as incidence and mortality rates associated with different critical care pathways: kidney transplant, hemodialysis, peritoneal dialysis, and conservative care. Calibrated to US Renal Data System (USRDS) 2013 Annual and Historical Data Report and the US Census Bureau for the years 2005–2010, encompassing all ESRD patients under treatment in the USA from 2005 to 2010, the ESRD population model predicts the growth and costs of ESRD treatment type populations using historical patterns. The model has been calibrated against the output of the USRDS’s own prediction for the year 2020 and also tested by running his- toric scenarios and comparing the output to existing data. Using a web interface designed to allow users to alter certain combinations of parameters, several scenarios are run to project future spending, incidence, and mortalities if certain combinations of critical care pathways are pursued. These sce- narios include: a doubling of kidney donations and transplant rates, a marked increase in the offering of peritoneal dialysis, and an increase in conservative care routes for patients over 65. The results of these scenario runs are shared, demonstrating sizable cost savings and increased survival rates. Implications of clinical practice, public policy, and further research are drawn (Fernandez 2013)

Figure 2.2 provides an illustration of Luca Fernandez’s system dynamics model of critical care pathways for end-stage renal disease (ESRD). Fernandez took Koliba’s system analysis and strategic management course and Zia’s decision-making model- ing course. This model, constructed using the proprietary software, AnyLogic, was initially constructed as a project in Zia’s course.

Castle and Fernandez’s projects illustrate how master’s-level students with an eye toward becoming policy informatics analysts can build skills and capacities to develop useful informatics projects that can guide policy and public management. They were guided to this point by taking advanced courses designed explicitly with policy informatics outcomes in mind.

[email protected]

26 C. Koliba and A. Zia

Policy Informatics Analyst Informatics-Savvy Public

•Advanced research methods •Data visualization and design techniques •Basic to advanced modeling software skills •Basic to advanced programming language(s) •Systems thinking •Basic understanding of research methods •Knowledge of how to integrate informatics within performance management •Knowledge of how to integrate inofrmatics within financial systems•Effecive written communication •Effective usese of social media / e-governance approaches

Fig. 2.3 The nested capacities of informatics-savvy public managers and policy informatics analysts

Figure 2.3 illustrates how the competencies of the two different ideal types of policy informatics practitioners are nested inside of one another. A more complete list of competencies that are needed for the more advanced forms of policy analy- sis will need to emerge through robust exchanges between the computer sciences, organizational sciences, and policy sciences. These views will likely hinge on as- sumptions about the sophistication of the models to be developed. A key question here concerning the types of models to be built is: Can adequate models be built using existing software or is original programming needed or desired? Ideally, ad- vanced policy analysts undertaking policy informatics projects are “programmers with a public service motivation.”

2.3 Applications to Professional Masters Programs

Professional graduate degree programs have steadily moved toward emphasizing the importance of the mission of particular graduate programs in determining the optimal curriculum to suit the learning needs of it students. As a result, clear definitions of the learning outcomes and the learning needs of particular student communities are defined. Some programs may seek to serve regional or local needs of the government and nonprofit sector, while others may have a broader reach, preparing students to work within federal or international level governments and nonprofits.

In addition to geographic scope, accredited MPA and MPP programs may have specific areas of concentration. Some programs may focus on preparing public man- agers who are charged with managing resources, making operational, tactical, and

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 27

strategic decisions and, overall, administering to the day-to-day needs of a govern- ment or nonprofit organization. Programs may also focus on training policy analysts who are responsible for analyzing policies, policy alternatives, problem definition, and the like. Historically, the differences between public management and policy analysis have distinguished the MPA degree from the MPP degree. However, recent studies of NASPAA-accredited programs have found that the lines between MPA and MPP programs are increasingly blurred (Hur and Hackbart 2009). The relationship between public management and policy analysis matters to those interested in policy informatics because these distinctions drive what policy informatics competencies and capacities are covered within a core curriculum, and what competencies and capacities are covered within a suite of electives or concentrations.

Competency-based assessments are increasingly being used to evaluate and de- sign curriculum. Drawing on the core tenants of adult learning theory and practice, competency-based assessment involves the derivation of specific skills, knowledge, or attitudes that an adult learner must obtain in order to successfully complete a course of study or degree requirement. Effective competency-based graduate pro- grams call on students to demonstrate a mastery of competencies through a variety of means. Portfolio development, test taking, and project completion are common applications. Best practices in competency-based education assert that curriculum be aligned with specific competencies as much as possible.

By way of example, the University ofVermont’s MPA Program has had a “systems thinking” focus since it was first conceived in the middle 1980s. Within the last 10 years, the two chapter coauthors, along with several core faculty who have been associated with the program since its inception, have undertaken an effort to refine its mission based on its original systems-focused orientation.

As of 2010, the program mission was refined to read:

Our MPA program is a professional interdisciplinary degree that prepares pre and in-service leaders, managers and policy analysts by combining the theoretical and practical founda- tions of public administration focusing on the complexity of governance systems and the democratic, collaborative traditions that are a hallmark of Vermont communities.

The mission was revised to include leaders and managers, as well as policy analysts. A theory-practice link was made explicit. The phrase, “complexity of governance systems” was selected to align with a commonly shared view of contemporary gover- nance as a multisectoral and multijurisdictional context. Concepts such as bounded rationality, social complexity, the importance of systems feedback, and path de- pendency are stressed throughout the curriculum. The sense of place found within the State of Vermont was also recognized and used to highlight the high levels of engagement found within the program.

The capacities laid out in Table 2.1 have been mapped to the program’s core curriculum. The program’s current core is a set of five courses: PA 301: Foundations of Public Administration; PA 302: Organizational Behavior and Change; PA 303: Research Methods; PA 305: Public and Nonprofit Budgeting and Finance and PA 306: Policy Systems. In addition, all students are required to undertake a three- credit internship and a three-credit Capstone experience in which they construct a

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28 C. Koliba and A. Zia

final learning portfolio. It is within this final portfolio that students are expected to provide evidence of meeting or exceeding the standard. An expanded rubric of all 18 capacities is used by the students to undertake their own self-assessment. These assessments are judged against the Capstone instructor’s evaluation.

In 2009, the MPA faculty revised the core curriculum to align with the core competencies. Several course titles and content were revised to align with these competencies and the overall systems’ focus of our mission. The two core courses taught by the two coauthors, PA 301 and PA 306, are highlighted here.

2.4 PA 301: Foundations of Public Administration

Designed as a survey of the prevailing public administration literature during the past 200 plus years, Foundations of Public Administration is arranged across a continuum of interconnected themes and topics that are to be addressed in more in-depth in other courses and is described in the syllabus in the following way:

This class is designed to provide you with an overview of the field of public administra- tion. You will explore the historical foundations, the major theoretical, organizational, and political breakthroughs, and the dynamic tensions inherent to public and nonprofit sector administration. Special attention will be given to problems arising from political imperatives generated within a democratic society.

Each week a series of classic and contemporary texts are read and reviewed by the students. In part, to fill a noticeable void in the literature, the authors co-wrote, along with Jack Meek, a book on governance networks called: Governance Networks in Public Administration and Public Policy (Koliba et al. 2010). This book is required reading. Students are also asked to purchase Shafritz and Hyde’s edited volume, Classics of Public Administration.

Current events assignments offered through blog posts are undertaken. Weekly themes include: the science and art of administration; citizens and the administra- tive state; nonprofit, private, and public sector differences; governance networks; accountability; and performance management.

During the 2009 reforms of the core curriculum, discrete units on governance networks and performance management were added to this course. Throughout the entire course, a complex systems lens is employed to describe and analyze gover- nance networks and the particular role that performance management systems play in providing feedback to governance actors. Students are exposed to social network and system dynamics theory, and asked to apply these lenses to several written cases taken from the Electronic Hallway. A unit on performance management systems and their role within fostering organizational learning are provided along with readings and examples of decision support tools and dashboard platforms currently in use by government agencies.

Across many units, including units on trends and reforms, ethical and reflective leadership, citizens and the administrative state, and accountability, the increasing use of social media and other forms of information technology are discussed. Trends

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 29

shaping the “e-governance” and “e-government” movements serve as a major focus on current trends. In addition, students are exposed to current examples of data visualizations and open data platforms and asked to consider their uses.

2.5 PA 306: Policy Systems

Policy Systems is an entry-level graduate policy course designed to give the MPA student an overview of the policy process. In 2009, the course was revised to reflect a more integrated systems focus. The following text provides an overview of the course:

In particular, the emphasis is placed upon meso-, and macro-scale policy system frame- works and theories, such as InstitutionalAnalysis and Development Framework, the Multiple Streams Framework; Social Construction and Policy Design; the Network Approach; Punc- tuated Equilibrium Theory; the Advocacy Coalition Framework; Innovation and Diffusion Models and Large-N Comparative Models. Further, students will apply these micro-, meso- and macro-scale theories to a substantive policy problem that is of interest to a community partner, which could be a government agency or a non-profit organization. These policy problems may span, or even cut across, a broad range of policy domains such as (included but not limited to) economic policy, food policy, environmental policy, defense and foreign policy, space policy, homeland security, disaster and emergency management, social policy, transportation policy, land-use policy and health policy.

The core texts for this class are Elinor Ostrom’s, Understanding Institutional Di- versity, Paul Sabatier’s edited volume, Theories of the Policy Process, and Deborah Stone’s Policy Paradox: The Art of Political Decision-Making. The course itself is staged following a micro, to meso, to macro level scale of policy systems framework. A service-learning element is incorporated. Students are taught to view the policy process through a systems lens. Zia employs examples of policy systems models us- ing system dynamics (SD), agent-based modeling (ABM), social network analysis (SNA), and hybrid approaches throughout the class. By drawing on Ostrom, Sabatier, and other meso level policy processes as a basis, students are exposed to a number of “complexity-friendly” theoretical policy frameworks (Koliba and Zia 2013). Appre- ciating the value of these policy frameworks, students are provided with heuristics for understanding the flow of information across a system. In addition, students are shown examples of simulation models of different policy processes, streams, and systems.

In addition to PA 301 and PA 306, students are also provided an in-depth ex- ploration of organization theory in PA 302 Organizational Behavior and Change that is taught through an organizational psychology lens that emphasizes the role of organizational culture and learning. “Soft systems” approaches are applied. PA 303 Research Methods for Policy Analysis and Program Evaluation exposes students to a variety of research and program evaluation methodologies with a particular focus on quantitative analysis techniques. Within PA 305 Public and Nonprofit Budgeting and Finance, students are taught about evidence-based decision-making and data management.

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30 C. Koliba and A. Zia

By completing the core curriculum, students are exposed to some of the founda- tional competencies needed to use and shape policy informatics projects. However, it is not until students enroll in one of the several electives, that more explicit policy informatics concepts and applications are taught. Two of these elective courses are highlighted here. A third, PA 311 Policy Analysis, also exposes students to policy analyst capacities, but is not highlighted here.

2.6 PA 308: Decision-Making Models

A course designated during the original founding of the University of Vermont (UVM)-MPA Program, PA 308: Decision-Making Models offers students with a more advanced look at decision-making theory and modeling. The course is described by Zia in the following manner:

In this advanced graduate level seminar, we will explore and analyze a wide range of norma- tive, descriptive and prescriptive decision making models. This course focuses on systems level thinking to impart problem-solving skills in complex decision-making contexts. Deci- sion making problems in the real-world public policy, business and management arenas will be analyzed and modeled with different tools developed in the fields of Decision Analysis, Behavioral Sciences, Policy Sciences and Complex Systems. The emphasis will be placed on imparting cutting edge skills to enable students to design and implement multiple criteria decision analysis models, decision making models under risk and uncertainty and computer simulation models such as Monte Carlo simulation, system dynamic models, agent based models, Bayesian decision making models, participatory and deliberative decision making models, and interactive scenario planning approaches. AnyLogic version 6.6 will be made available to the students for working with some of these computer simulation models.

2.7 PA 317: Systems Analysis and Strategic Management

Another course designate during the early inception of the program, systems analysis and strategic management is described by Koliba in the course syllabus as follows:

This course combines systems and network analysis with organizational learning theory and practices to provide students with a heightened capacity to analyze and effectively operate in complex organizations and networks. The architecture for the course is grounded in many of the fundamental conceptual frameworks found in network, systems and complexity analysis, as well as some of the fundamental frameworks employed within the public administration and policy studies fields. In this course, strategic management and systems analysis are linked together through the concept of situational awareness and design principles. Several units focusing on teaching network analysis tools using UCINet have been incorporated.

One of the key challenges to offering these informatics-oriented electives lies in the capacities that the traditional MPA students possess to thrive within them. Increas- ingly, these elective courses are being populated by doctoral and master of science students looking to apply what they are learning to their dissertations or thesis. Our MPA program offers a thesis option and we have had some success with these more

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 31

professionally oriented students undertaking high quality informatics focused thesis. Our experience begs a larger question pertaining to the degree to which the baseline informatics-savvy public manager capacities lead into more complex policy analysts competencies associated with the actual design and construction of policy informatics projects, programs, and platforms.

Table 2.2 provides an overview of where within the curriculum certain policy informatics capacities are covered. When associated with the class, students are exposed to the uses of informatics projects, programs, or platforms or provided opportunities for concrete skill development.

The University of Vermont context is one that can be replicated in other programs. The capacity of the MPA program to offer these courses hinges on the expertise of two faculties who teach in the core and these two electives. With additional re- sources, a more advanced curriculum may be pursued, one that pursues closer ties with the computer science department (Zia has a secondary appointment) around curricular alignment. Examples of more advanced curriculum to support the devel- opment of policy informatics analysts may be found at such institutions as Carnegie Mellon University, Arizona State University, George Mason University, University at Albany, Delft University of Technology, Massachusetts Institute of Technology, among many others. The University of Vermont case suggests, however, that pol- icy informatics education can be integrated into the main stream with relatively low resource investments leveraged by strategic relationships with other disciplines and core faculty with the right skills, training, and vision.

2.8 Conclusion

It is difficult to argue that with the advancement of high speed computing, the dig- itization of data and the increasing collaboration occurring around the development of informatics projects, programs, and platforms, that the educational establishment, particularly at the professional master degree levels, will need to evolve. This chap- ter lays out a preliminary look at some of the core competencies and capacities that public managers and policy analysts will need to lead the next generation of policy informatics integration.

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32 C. Koliba and A. Zia

Table 2.2 Policy informatics capacities covered within the UVM-MPA program curriculum

Course title Policy informatics-savvy public management capacities covered

Policy informatics analysis capacities covered

PA 301: Foundations of public administration

Systems thinking Policy as process Performance management Financial management Basic communication Social media/IT/e-governance Collaborative–cooperative capacity building

Data visualization and design

PA 306: Policy systems Systems thinking Policy as process Basic communication

Basic modeling skills

PA 302: Organizational behavior and change

Systems thinking Basic communication Collaborative–cooperative capacity building

PA 303: Research methods for policy analysis and program evaluation

Research methods Basic communication

Data visualization and design

PA 305: Public and nonprofit budgeting and finance

Financial management Performance management Basic communication

PA 308: Decision-making modeling

Systems thinking Policy as process Research methods Performance management Social media/IT/e-governance

Advanced research methods Data visualization and design techniques Basic modeling skills

PA 311: Policy analysis Systems thinking Policy as process Research methods Performance management Basic communication

Advanced research methods Data visualization and design Basic modeling skills

PA 317: Systems analysis and strategic analysis

Systems thinking Policy as process Research methods Performance management Collaborative–cooperative capacity building Basic communication Social media/IT/e-governance

Data visualization and design Basic modeling skills

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 33

2.9 Appendix A: University of Vermont’s MPA Program Learning Competencies and Capacities

NASPAA core standard UVM-MPA learning capacity

To lead and manage in public governance

Capacity to understand accountability and democratic theory

Capacity to manage the lines of authority for public, private, and nonprofit collaboration, and to address sectorial differences to overcome obstacles

Capacity to apply knowledge of system dynamics and network structures in PA practice

Capacity to carry out effective policy implementation

To participate in and contribute to the policy process

Capacity to apply policy streams, cycles, systems foci upon past, present, and future policy issues, and to understand how problem identification impacts public administration

Capacity to conduct policy analysis/evaluation

Capacity to employ quantitative and qualitative research methods for program evaluation and action research

To analyze, synthesize, think critically, solve problems, and make decisions

Capacity to initiate strategic planning, and apply organizational learning and development principles

Capacity to apply sound performance measurement and management practices

Capacity to apply sound financial planning and fiscal responsibility

Capacity to employ quantitative and qualitative research methods for program evaluation and action research

To articulate and apply a public service perspective

Capacity to understand the value of authentic citizen participation in PA practice

Capacity to understand the value of social and economic equity in PA practices

Capacity to lead in an ethical and reflective manner

Capacity to achieve cooperation through participatory practices

To communicate and interact productively with a diverse and changing workforce and citizenry

Capacity to undertake high quality oral, written, and electronically mediated communication and utilize information systems and media to advance objectives

Capacity to appreciate the value of pluralism, multiculturalism, and cultural diversity

Capacity to carry out effective human resource management

Capacity to undertake high quality oral, written, and electronically mediated communication and utilize information systems and media to advance objectives

NASPAA Network of Schools of Public Affairs and Administration, UVM University of Vermont, MPA Master of Public Administration, PA Public administration

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34 C. Koliba and A. Zia

References

Argyis C, Schön DA (1996) Organizational learning II: theory, method, and practice. Addison- Wesley, Reading

Bryson J (2011) Strategic planning for public and nonprofit organizations: a guide to strengthening and sustaining organizational achievement. Jossey-Bass, San Francisco

Caiden N (1981) Public budgeting and finance. Blackwell, New York Castle J (2014) Visualizing natural gas industry contributions in Pennsylvania Government, PA 317

final class project Desouza KC (2014) Realizing the promise of big data: implementing big data projects. IBM Center

for the Business of Government, Washington, DC Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Fact 37(1):32–

64 Fernandez L (2013) An ESRD system dynamics population model for the United States. Final

project for PA 308 Hur Y, Hackbart M (2009) MPA vs. MPP: a distinction without a difference? J Public Aff Educ

15(4):397–424 Katz D, Khan R (1978) The social psychology of organizations. Wiley, New York Kingdon J (1984) Agendas, alternatives, and public policies. Harper Collins, New York Koliba C, Zia A (2013) Complex systems modeling in public administration and policy studies:

challenges and opportunities for a meta-theoretical research program. In: Gerrits L, Marks PK (eds) COMPACT I: public administration in complexity. Emergent, Litchfield Park

Koliba C, Meek J, Zia A (2010) Governance networks in public administration and public policy. CRC, Boca Raton

Koliba C, Zia A, Lee B (2011) Governance informatics: utilizing computer simulation models to manage complex governance networks. Innov J Innov Publ Sect 16(1):1–26 (Article 3). (http://www.innovation.cc/scholarly-style/koliba_governance_informaticsv16i1a3.pdf)

Korton DC (2001) The management of social transformation. In: Stivers C (ed) Democracy, bureaucracy, and the study of administration. Westview, Boulder, pp 476–497

Loorbach D (2007) Transition management: new modes of governance for sustainable development. International Books, Ultrecht

Mergel I (2013) Social media adoption and resulting tactics in the U.S. federal government. Gov Inf Quart 30(2):123–130

Moynihan DP (2008) The dynamics of performance management: constructing information and reform. Georgetown University Press, Washington, DC

O’Leary R, Bingham L (eds) (2009) The collaborative public manager: new ideas for the twenty-first century. Georgetown University Press, Washington, DC

Patton M (2008) Utilization-focused evaluation. Sage, New York Schick A (1966) The road to PPB: the stages of budget reform. Public Admin Rev 26(4):243–259 Senge PM (1990) The fifth discipline: the art and practice of the learning organization. Doubleday

Currency, New York Stacey RD (2001) Complex responsive processes in organizations: learning and knowledge creation.

Routledge, London Willoughby WF (1918) The movement of budgetary reform in the states. D. Appleton, New York

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Chapter 3 The Quality of Social Simulation: An Example from Research Policy Modelling

Petra Ahrweiler and Nigel Gilbert

Abstract This chapter deals with the assessment of the quality of a simulation. The first section points out the problems of the standard view and the constructivist view in evaluating social simulations. A simulation is good when we get from it what we originally would have liked to get from the target; in this, the evaluation of the simulation is guided by the expectations, anticipations, and experience of the community that uses it. This makes the user community view the most promising mechanism to assess the quality of a policy-modelling exercise. The second section looks at a concrete policy-modelling example to test this idea. It shows that the very first negotiation and discussion with the user community to identify their questions is highly user-driven, interactive, and iterative. It requires communicative skills, patience, willingness to compromise on both sides, and motivation to make the formal world of modellers and the narrative world of practical policy making meet. Often, the user community is involved in providing data for calibrating the model. It is not an easy issue to confirm the existence, quality, and availability of data and check for formats and database requirements. As the quality of the simulation in the eyes of the user will very much depend on the quality of the informing data and the quality of the model calibration, much time and effort need to be spent in coordinating this issue with the user community. Last but not least, the user community has to check the validity of simulation results and has to believe in their quality. Users have to be enabled to understand the model, to agree with its processes and ways to produce results, to judge similarity between empirical and simulated data, etc. Although the user community view might be the most promising, it is the most work-intensive mechanism to assess the quality of a simulation. Summarising, to trust the quality of a simulation means to trust the process that produced its results. This process includes not only the design and construction of the simulation model itself but also the whole interaction between stakeholders, study team, model, and findings.

P. Ahrweiler (�) EA European Academy of Technology and Innovation Assessment GmbH, Bad Neuenahr-Ahrweiler, Germany e-mail: [email protected]

N. Gilbert University of Surrey, Guildford, UK

© Springer International Publishing Switzerland 2015 35 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_3

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36 P. Ahrweiler and N. Gilbert

Table 3.1 Comparing simulations

Caffè Nero simulation Science simulation

Target Venetian Café “Real system”

Goal Getting “the feeling” (customers) and profit (owners) from it

Getting understanding and/or predictions from it

Model By reducing the many features of a Venetian Café to a few parameters

By reducing the many features of the target to a few parameters

Question Is it a good simulation, i.e. do we get from it what we want?

Is it a good simulation, i.e. do we get from it what we want?

This chapter deals with the assessment of the quality of a simulation. After dis- cussing this issue on a general level, we apply and test the assessment mechanisms using an example from policy modelling.

3.1 Quality in Social Simulation

The construction of a scientific social simulation implies the following process: “We wish to acquire something from a target entity T. We cannot get what we want from T directly. So, we proceed indirectly. Instead of T we construct another entity M, the ‘model’, which is sufficiently similar to T that we are confident that M will deliver (or reveal) the acquired something which we want to get from T. [. . .] At a moment in time, the model has structure. With the passage of time the structure changes and that is behaviour. [. . .] Clearly we wish to know the behaviour of the model. How? We may set the model running (possibly in special sets of circumstances of our choice) and watch what it does. It is this that we refer to as‘simulation’ of the target” (quoted with slight modifications from Doran and Gilbert 1994).

We also habitually refer to “simulations” in everyday life, mostly in the sense that a simulation is “an illusory appearance that manages a reality effect” (cf. Norris 1992), or as Baudrillard put it, “to simulate is to feign to have what one hasn’t” while “substituting signs for the real” (Baudrillard 1988). In a previous publication (Ahrweiler and Gilbert 2005), we used the example of the Caffè Nero in Guildford, 50 km southwest of London, as a simulation of a Venetian café—which will serve as the “real” to illustrate this view. The purpose of the café is to “serve the best coffee north of Milan”. It tries to give the impression that you are in a real Italian café—although, most of the time, the weather outside can make the illusion difficult to maintain.

The construction of everyday simulations like Caffè Nero has some resemblance to the construction of scientific social simulations (see Table 3.1):

In both cases, we build models from a target by reducing the characteristics of the latter sufficiently for the purpose at hand; in each case, we want something from the model we cannot achieve easily from the target. In the case of Caffè Nero, we cannot simply go to Venice, drink our coffee, be happy, and return. It is too expensive and

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 37

time-consuming. We have to use the simulation. In the case of a science simulation, we cannot get data from the real system to learn about its behaviour. We have to use the simulation.

The question, whether one or the other is a good simulation, can therefore be reformulated as: Do we get from the simulation what we constructed it for?

Heeding these similarities, we shall now try to apply evaluation methods typically used for everyday simulations to scientific simulation and vice versa. Before doing so, we shall briefly discuss the “ordinary” method of evaluating simulations called the “standard view” and its adversary, a constructivist approach asserting, “anything goes”.

3.1.1 The Standard View

The standard view refers to the well-known questions and methods of verification, namely whether the code does what it is supposed to do and whether there are any bugs, and validation, namely whether the outputs (for given inputs/parameters) resemble observations of the target, although (because the processes being modelled are stochastic and because of unmeasured factors) identical outputs are not to be expected, as discussed in detail in Gilbert and Troitzsch (1997). This standard view relies on a realist perspective because it refers to the observability of reality in order to compare the “real” with artificial data produced by the simulation.

Applying the standard view to the Caffè Nero example, we can find quantitative and sometimes qualitative measures for evaluating the simulation. Using quantitative measures of similarity between it and a “real”Venetian café, we can ask, for example,

• Whether the coffee tastes the same (by measuring, for example, a quality score at blind tasting),

• Whether the Caffè is a cool place (e.g. measuring the relative temperatures inside and outside),

• Whether the noise level is the same (using a dB meter for measuring pur- poses),whether the lighting level is the same (using a light meter), and

• Whether there are the same number of tables and chairs per square metre for the customers (counting them), and so on.

In applying qualitative measures of similarity, we can again ask:

• Whether the coffee tastes the same (while documenting what comes to mind when customers drink the coffee),

• Whether the Caffè is a “cool” place (this time meaning whether it is a fashionable place to hang out),

• Whether it is a vivid, buzzing place, full of life (observing the liveliness of groups of customers),

• Whether there is the same pattern of social relationships (difficult to opera- tionalise: perhaps by observing whether the waiters spend their time talking to the customers or to the other staff), and

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38 P. Ahrweiler and N. Gilbert

• Whether there is a ritual for serving coffee and whether it is felt to be the same as in a Venetian café.

The assumption lying behind these measures is that there is a “real” café and a “simulation” café and that in both of these, we can make observations. Similarly, we generally assume that the theories and models that lie at the base of science simulations are well grounded and can be validated by observation of empirical facts. However, the philosophy of science forces us to be more modest.

3.1.1.1 The Problem of Under-determination

Some philosophers of science argue that theories are under-determined by observa- tional data or experience, that is, the same empirical data may be in accord with many alternative theories. An adherent of the standard view would respond in that one important role of simulations (and of any form of model building) is to derive from theories as many testable implications as possible so that eventually validity can be assessed in a cumulative process1. Simulation is indeed a powerful tool for testing theories in that way if we are followers of the standard view.

However, the problem that theories are under-determined by empirical data can- not be solved by cumulative data gathering: it is more general and therefore more serious. The under-determination problem is not about a missing quantity of data but about the relation between data and theory. As Quine (1977) presents it: If it is possible to construct two or more incompatible theories by relying on the same set of experimental data, the choice between these theories cannot depend on “empirical facts”. Quine showed that there is no procedure to establish a relation of uniqueness between theory and data in a logically exclusive way. This leaves us with an annoying freedom: “sometimes, the same datum is interpreted by such different assumptions and theoretical orientations using different terminologies that one wonders whether the theorists are really thinking of the same datum” (Harbodt 1974, p. 258 f., own translation).

The proposal mentioned above to solve the under-determination problem by sim- ulation does not touch the underlying reference problem at all. It just extends the theory, adding to it its “implications”, hoping them to be more easily testable than the theory’s core theorems. The general reference between theoretical statement— be it implication or core theorem—and observed data has not changed by applying this extension: The point here is that we cannot establish a relation of uniqueness between the observed data and the theoretical statement. This applies to any segment of theorising at the centre or at the periphery of the theory on any level—a matter that cannot be improved by a cumulative strategy.

1 We owe the suggestion that simulation could be a tool to make theories more determined by data to one of the referees of Ahrweiler and Gilbert (2005).

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 39

3.1.1.2 The Theory-Ladenness of Observations

Observations are supposed to validate theories, but in fact theories guide our ob- servations, decide on our set of observables, and prepare our interpretation of the data. Take, for example, the different concepts of two authors concerning Venetian cafés: For one, a Venetian café is a quiet place to read newspapers and relax with a good cup of coffee; for the other, a Venetian café is a lively place to meet and talk to people with a good cup of coffee. The first attribute of these different conceptions of a Venetian café is supported by one and the same observable, namely the noise level, although one author expects a low level, the other a high one. The second attribute is completely different: the first conception is supported by a high number of newspaper readers, the second by a high number of people talking. Accordingly, a “good” simulation would mean a different thing for each of the authors. A good simulation for one would be a poor simulation for the other and vice versa. Here, you can easily see the influence of theory on the observables. This example could just lead to an extensive discussion about the “nature” of a Venetian café between two authors, but the theory-ladenness of observations again leads to more serious difficulties. Our access to data is compromised by involving theory, with the con- sequence that observations are not the “bed rock elements” (Balzer et al. 1987) our theories can safely rely on. At the very base of theory is again theory. The attempt to validate our theories by “pure” theory-neutral observational concepts is mistaken from the beginning.

Balzer et al. summarise the long debate about the standard view on this issue as follows: “First, all criteria of observability proposed up to now are vulnerable to serious objections. Second, these criteria would not contribute to our task because in all advanced theories there will be no observational concepts at all—at least if we take ‘observational’ in the more philosophical sense of not involving any theory. Third, it can be shown that none of the concepts of an advanced theory can be defined in terms of observational concepts” (Balzer et al. 1987, p. 48). Not only can you not verify a theory by empirical observation, but you cannot even be certain about falsifying a theory. A theory is not validated by “observations” but by other theories (observational theories). Because of this reference to other theories, in fact a nested structure, the theory-ladenness of each observation has negative consequences for the completeness and self-sufficiency of scientific theories (cf. Carrier 1994, pp. 1–19). These problems apply equally to simulations that are just theories in process.

We can give examples of these difficulties in the area of social simulation. To compare Axelrod’s The Evolution of Cooperation (Axelrod 1984) and all the subse- quent work on iterated prisoners’ dilemmas with the “real world”, we would need to observe “real” IPDs, but this cannot be done in a theory-neutral way. The same problems arise with the growing body of work on opinion dynamics (e.g. Deffuant et al. 2000; Ben-Naim et al. 2003; Weisbuch 2004). The latter starts with some sim- ple assumptions about how agents’ opinions affect the opinions of other agents, and shows under which circumstances the result is a consensus, polarisation, or fragmen- tation. However, how could these results be validated against observations without involving again a considerable amount of theory?

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40 P. Ahrweiler and N. Gilbert

Important features of the target might not be observable at all. We cannot, for example, observe learning. We can just use some indicators to measure the conse- quences of learning and assume that learning has taken place. In science simulations, the lack of observability of significant features is one of the prime motivations for carrying out a simulation in the first place.

There are also more technical problems. Validity tests should be “exercised over a full range of inputs and the outputs are observed for correctness” (Cole 2000, p. 23). However, the possibility of such testing is rejected: “real life systems have too many inputs, resulting in a combinatorial explosion of test cases”. Therefore, simulations have “too many inputs/outputs to be able to test strictly” (Cole 2000, p. 23).

While this point does not refute the standard view in principle but only emphasises difficulties in execution, the former arguments reveal problems arising from the logic of validity assessment. We can try to marginalise, neglect, or even deny these problems, but this will disclose our position as mere “believers” of the standard view.

3.1.2 The Constructivist View

Validating a simulation against empirical data is not about comparing “the real world” and the simulation output; it is comparing what you observe as the real world with what you observe as the output. Both are constructions of an observer and his/her views concerning relevant agents and their attributes. Constructing reality and simu- lation are just two ways of an observer seeing the world. The issue of object formation is not normally considered by computer scientists relying on the standard view: data is “organized by a human programmer who appropriately fits them into the chosen representational structure. Usually, researchers use their prior knowledge of the na- ture of the problem to hand-code a representation of the data into a near-optimal form. Only after all this hand-coding is completed is the representation allowed to be manipulated by the machine. The problem of representation-formation [. . .] is ignored” (Chalmers et al. 1995, p. 173).

However, what happens if we question the possibility of validating a simulation by comparing it with empirical data from the “real world”? We need to refer to the modellers/observers in order to get at their different constructions. The constructivists reject the possibility of evaluation because there is no common “reality” we might refer to. This observer-oriented opponent of the realist view is a nightmare to most scientists: “Where anything goes, freedom of thought begins. And this freedom of thought consists of all people blabbering around and everybody is right as long as he does not refer to truth. Because truth is divisible like the coat of Saint Martin; everybody gets a piece of it and everybody has a nice feeling” (Droste 1994, p. 50).

Clearly, we can put some central thoughts from this view much more carefully: “In dealing with experience, in trying to explain and control it, we accept as legitimate and appropriate to experiment with different conceptual settings, to combine the flow of experience to different ‘objects”’ (Gellner 1990, p. 75).

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 41

However, this still leads to highly questionable consequences: There seems to be no way to distinguish between different constructions/simulations in terms of “truth”, “objectivity”, “validity”, etc. Science is going coffeehouse: Everything is just construction, rhetoric, and arbitrary talk. Can we so easily dismiss the possibility of evaluation?

3.1.3 The User Community View

We take refuge at the place we started from: What happens if we go back to the Venetian café simulation and ask for an evaluation of its performance? It is probably the case that most customers in the Guildford Caffè Nero have never been to an Italian café. Nevertheless, they manage to “evaluate” its performance—against their concept of an Italian café that is not inspired by any “real” data. However, there is something “real” in this evaluation, namely the customers, their constructions, and a “something” out there, which everybody refers to, relying on some sort of shared meaning and having a “real” discussion about it. The philosopher Searle shows in his work on the Construction of Social Reality (Searle 1997) how conventions are “real”: They are not deficient for the support of a relativistic approach because they are constructed.

Consensus about the “reality observed by us” is generated by an interaction pro- cess that must itself be considered real. At the base of the constructivist view is a strong reference to reality, that is, conventions and expectations that are socially cre- ated and enforced. When evaluating the Caffè Nero simulation, we can refer to the expert community (customers, owners) who use the simulation to get from it what they would expect to get from the target. A good simulation for them would satisfy the customers who want to have the “Venetian feeling” and would satisfy the owners who want to get the “Venetian profit”.

For science, equally, the foundation of every validity discussion is the ordinary everyday interaction that creates an area of shared meanings and expectations. This area takes the place left open by the under-determination of theories and the theo- reticity problem of the standard view.2 Our view comes close to that of empirical epistemology, which points out that the criteria for quality assessment “do not come from some a priori standard but rest on the description of the way research is actually conducted” (Kértesz 1993, p. 32).

2 Thomas Nickles claims new work opportunities for sociology at this point: “the job of phi- losophy is simply to lay out the necessary logico-methodological connections against which the under-determination of scientific claims may be seen; in other words, to reveal the necessity of so- ciological analysis. Philosophy reveals the depths of the under-determination problem, which has always been the central problem of methodology, but is powerless to do anything about it. Under- determination now becomes the province of sociologists, who see the limits of under-determination as the bounds of sociology. Sociology will furnish the contingent connections, the relations, which a priori philosophy cannot” (Nickles 1989, p. 234 f.).

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42 P. Ahrweiler and N. Gilbert

If the target for a social science simulation is itself a construction, then the simu- lation is a second-order construction. In order to evaluate the simulation, we can rely on the ordinary (but sophisticated) institutions of (social) science and their practice. The actual evaluation of science comes from answers to questions such as: Do others accept the results as being coherent with existing knowledge? Do other scientists use it to support their work? Do other scientists use it to inspire their own investigations?

An example of such validity discourse in the area of social simulation is the history of the tipping model first proposed by Schelling, and now rather well known in the social simulation community. The Schelling model purports to demonstrate the reasons for the persistence of urban residential segregation in the USA and elsewhere. It consists of a grid of square cells, on which are placed agents, each either black or white. The agents have a “tolerance” for the number of agents of the other colour in the surrounding eight cells that they are content to have around them. If there are “too many” agents of the other colour, the unhappy agents move to other cells until they find a context in which there are a tolerable number of other-coloured agents. Starting with a random distribution, even with high levels of tolerance, the agents will still congregate into clusters of agents of the same colour. The point Schelling and others have taken from this model is that residential segregation will form and persist even when agents are rather tolerant.

The obvious place to undertake a realist validation of this model is a US city. One could collect data about residential mobility and, perhaps, on “tolerance”. However, the exercise is harder than it looks. Even US city blocks are not all regular and square, so the real city does not look anything like the usual model grid. Residents move into the city from outside, migrate to other cities, are born and die, so the tidy picture of mobility in the model is far from the messy reality. Asking residents how many people of the other colour they would be tolerant of is also an exercise fraught with difficulty: the question is hypothetical and abstract, and answers are likely to be biased by social desirability considerations. Notwithstanding these practical methodological difficulties, some attempts have been made to verify the model. The results have not provided much support. For instance, Benenson (2005) analysed residential distribution for nine Israeli cities using census data and demonstrated that whatever the variable tested—family income, number of children, education level— there was a great deal of ethnic and economic heterogeneity within neighbourhoods, contrary to the model’s predictions.

This apparent lack of empirical support has not, however, dimmed the fame of the model. The difficulty of obtaining reliable data provides a ready answer to doubts about whether the model is “really” a good representation of urban segregation dy- namics. Another response has been to elaborate the model at the theoretical level. For instance, Bruch (2005) demonstrates that clustering only emerges in Schelling’s model for discontinuous functional forms for residents’ opinions, while data from surveys suggest that people’s actual decision functions for race are continuous. She shows that using income instead of race as the sorting factor also does not lead to clustering, but if it is assumed that both race and income are significant, segregation appears. Thus, the model continues to be influential, although it has little or no em- pirical support, because it remains a fruitful source for theorising and for developing

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 43

new models. In short, it satisfies the criterion that it is “valid” because it generates further scientific work.

Summarising the first part of this chapter, we have argued that a simulation is good when we get from it what we originally would have liked to get from the target. It is good if it works. As Glasersfeld (1987, p. 429) puts it: “Anything goes if it works”. The evaluation of the simulation is guided by the expectations, anticipations and experience of the community that uses it—for practical purposes (Caffè Nero), or for intellectual understanding and for building new knowledge (science simulation).

3.2 An Example of Assessing Quality

In this part, we will apply and test the assessment mechanisms outlined using as an example our work with the simulating knowledge dynamics in innovation networks (SKIN) model in its application to research policy modelling.

There are now a number of policy-modelling studies using SKIN (Gilbert et al. 2014). We will here refer to just one recent example, on the impact, assess- ment and ex-ante evaluation of European funding policies in the Information and Communication Technologies (ICT) research domain (Ahrweiler et al. 2014b).

3.2.1 A Policy-Modelling Application of SKIN

The basic SKIN model has been described and discussed in detail elsewhere (e.g. Pyka et al. 2007; Gilbert et al. 2007; Ahrweiler et al. 2011). On its most general level, SKIN is an agent-based model where agents are knowledge-intensive organi- sations, which try to generate new knowledge by research, be it basic or applied, or creating new products and processes by innovation processes. Agents are located in a changing and complex social environment, which evaluates their performance; e.g. the market if the agents target innovation or the scientific community if the agents target publications through their research activities. Agents have various options to act: each agent has an individual knowledge base called its “kene” (cf. Gilbert 1997), which it takes as the source and basis for its research and innovation activities. The agent kene is not static: the agent can learn, either alone by doing incremental or radical research, or from others, by exchanging and improving knowledge in partner- ships and networks. The latter feature is important, because research and innovation happens in networks, both in science and in knowledge-intensive industries. This is why SKIN agents have a variety of strategies and mechanisms for collaborative arrangements, i.e. for choosing partners, forming partnerships, starting knowledge collaborations, creating collaborative outputs, and distributing rewards. Summaris- ing, usually a SKIN application has agents interacting on the knowledge level and on the social level while both levels are interconnected. It is all about knowledge and networks.

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44 P. Ahrweiler and N. Gilbert

This general architecture is quite flexible, which is why the SKIN model has been called a “platform” (cf. Ahrweiler et al. 2014a), and has been used for a variety of applications ranging from the small such as simulating the Vienna biotech cluster (Korber and Paier 2014) to intermediate such as simulating the Norwegian defence industry (Castelacci et al. 2014), to large-scale applications such as the EU-funded ICT research landscape in Europe (Ahrweiler et al. 2014b). We will use the latter study as an example after explaining why the SKIN model is appropriate for realistic policy modelling in particular.

The birth of the SKIN model was inspired by the idea of bringing a theory on innovation networks, stemming mainly from innovation economics and economic so- ciology, onto the computer—a computer theory, which can be instantiated, calibrated, tested, and validated by empirical data. In 1998, the first EU project developing the model “Simulating Self-Organizing Innovation Networks” (SEIN) consisted of a three-step procedure: theory formation, empirical research collecting data both on the quantitative and on the case study level, and agent-based modelling implementing the theory and using the data to inform the model (Pyka et al. 2003).

This is why the SKIN model applications use empirical data and claim to be “realistic simulations” insofar as the aim is to derive conclusions by “inductive the- orising”. The quality of the SKIN simulation derives from an interaction between the theory underlying the simulation and the empirical data used for calibration and validation.

In what way does the SKIN model handle empirical data? We will now turn to our policy-modelling example to explain the data-to-model workflow, which is introduced in greater detail in Schilperoord and Ahrweiler (2014).

3.2.1.1 Policy Modelling for Ex-ante Evaluation of EU Funding Programmes

The INFSO-SKIN application, developed for the Directorate General Information Society and Media of the European Commission (DG INFSO), was intended to help to understand and manage the relationship between research funding and the goals of EU policy. The agents of the INFSO-SKIN application are research institutions such as universities, large diversified firms or small and medium-sized enterprises (SMEs). The model (see Fig. 3.1) simulated real-world activity in which the calls of the commission specify the composition of consortia, the minimum number of partners, and the length of the project; the deadline for submission; a range of capabilities, a sufficient number of which must appear in an eligible proposal; and the number of projects that will be funded. The rules of interaction and decision implemented in the model corresponded to Framework Programme (FP) rules; to increase the usefulness for policy designers, the names of the rules corresponded closely to FP terminology. For the Calls 1–6 that had occurred in FP7, the model used empirical information on the number of participants and the number of funded projects, together with data on project size (as measured by participant numbers), duration and average funding. Analysis of this information produced data on the functioning of, and relationships within, actual collaborative networks within the

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 45

Fig. 3.1 Flowchart of INFSO-SKIN

context of the FP. Using this data in the model provided a good match with the empirical data from EU-funded ICT networks in FP7: the model accurately reflected what actually happened and could be used as a test bed for potential policy choices (cf. Ahrweiler et al. 2014b).

Altering elements of the model that equate to policy interventions, such as the amount of funding, the size of consortia, or encouraging specific sections of the research community, enabled the use of INFSO-SKIN as a tool for modelling and evaluating the results of specific interactions between policies, funding strategies and agents. Because changing parameters within the model is analogous to applying different policy options in the real world, the model could be used to examine the likely real-world effects of different policy options before they were implemented.

3.2.1.2 The Data-to-Model Workflow

The first contact with “the real world” occurred in the definition phase of the project. What do the stakeholders want to know in terms of policies for a certain research or innovation network? Identifying relevant issues, discussing interesting aspects about them, forming questions and suggesting hypotheses for potential answers formed a first important step. This step was intended to conclude with a set of questions and a corresponding set of designs for experiments using the model that could answer those questions. This was an interactive and participative process between the study team,

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46 P. Ahrweiler and N. Gilbert

which knew about the possibilities and limitations of the model, and the stakeholders, who could be assumed to know what are the relevant issues in their day-to-day practice of policy making.

After discussing the evaluative questions for the ex-ante evaluation part of this study with the stakeholders from DG INFSO, the following questions were singled out for experiments:

1. What if there are no changes, and funding policies of DG INFSO continued in Horizon 2020 as they were in FP7?

2. What if there are changes to the currently eight thematic areas funded in the ICT domain prioritising certain areas in Horizon 2020?

3. What if there are changes to the instruments of funding and fund larger/smaller consortia in Horizon 2020 than in FP7?

4. What if there are interventions concerning the scope or outreach of funding providing much more/much less resource to more/fewer actors?

5. What if there are interventions concerning the participation of certain actors in the network (e.g. SMEs)?

The next step (see Fig. 3.2) was to collect relevant data to address these questions and hypotheses. The issues were not different from the ones every empirical researcher is confronted with. To identify relevant variables for operationalising hypotheses, to be as simple as possible but as detailed as necessary for description and explana- tion, is in line with the requirements of all empirical social research. For SKIN, the most important data are about knowledge dynamics (e.g. knowledge flows, amount of knowledge, and diversity of knowledge) and their indicators (e.g. publications, patents, and innovative ideas), and about dynamics concerning actors, networks, their measures, and their performance (e.g. descriptive statistics about actors, network analysis measures, and aggregate performance data).

These data were used to calibrate the initial knowledge bases of the agents, the social configurations of agents (“starting networks”), and the configuration of an environment at a given point in time. DG INFSO provided the data needed to calibrate the knowledge bases of the agents (in this case the research organisations in the European research area), the descriptive statistics on agents and networks and their interactions (in this case data on funded organisations and projects in ICT under FP7).

The time series data were used to validate the simulations by comparing the empirical data with the simulation outputs. Once we were satisfied with the model performance in that respect, experiments were conducted and the artificially produced data analysed and interpreted. The stakeholders were again invited to provide their feedback and suggestions about how to finetune and adapt the study to their changing user requirements as the study proceeded.

The last step was again stakeholder-centred as it involved visualisation and com- munication of data and results. We had to prove the credibility of the work and the commitment of the stakeholders to the policy-modelling activity.

We worked from an already existing application of the SKIN model adapted to the European research area (Scholz et al. 2010), implemented the scenarios according

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 47

Baseline

Thematic change

Instruments change

Funding level change

Participants chenge

Evaluative questions Horizon 2020

INFSO FP7 Database

Calls, Themes, Participants, Projects

21

Network Vis. & Statistics

Gephi

Scenario Development Tool

K"5"(Java 3 4

Participants impacts

Proposals impacts

Projects impacts

Knowlwdge impacts

Network impacts

SKIN model

Netlogo & lava

Simulation Database

CSV

Computational Policy Lab

MySQL

Calls, Themes, Participants, Proposals, Projects, Knowlwdge flows

Fig. 3.2 Horizon 2020 study workflow (Schilperoord and Ahrweiler 2014). First (on the left), a set of issues was isolated, in discussion with stakeholders. Data describing the network of FP7 projects and participants, by theme and Call, obtained from DG INFSO were entered into a database. These data were used to calibrate the INFSO-SKIN model. This model was then used to generate simulated data under various policy options. The simulated data were fed into a second database and visualised using additional network visualisation and statistical software in order to assess the expected impacts of those policy options

to the evaluative questions, and produced artificial data as output of the simulations. The results are reported in the final report presented to the European Cabinet, and were communicated to the stakeholders at DG INFSO.

3.2.2 The INFSO-SKIN Example as Seen by the Standard View

The standard view refers to verification, namely whether the code does what it is sup- posed to do, and validation, namely whether the outputs (for given inputs/parameters) sufficiently resemble observations of the target. To aid in verifying the model, it was completely recoded in another programming language and the two implementations cross-checked to ensure that they generated the same outputs given the same inputs.

To enable validation of the model, we needed to create a simulation resembling the stakeholders’ own world as they perceived it. The simulation needed to create the effect of similar complexity, similar structures and processes, and similar objects and options for interventions. To be under this similarity threshold would have led to the rejection of the model as a “toy model” that is not realistic and is under-determined by empirical data.

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In the eyes of these stakeholders, the more features of the model that can be validated against empirical data points, the better. Of course, there will always be an empirical “under-determination” of the model due to the necessary selection and abstraction process of model construction, empirical unobservables, missing data for observables, random features of the model, and so on. However, to find the “right” trade-off between empirical under-determination and model credibility was a crucial issue in the discussions between the study team and the stakeholders.

3.2.3 The INFSO-SKIN Example as Seen by the Constructivist View

The strength of a modelling methodology lies in the opportunity to ask what-if questions (ex-ante evaluation), an option that is normally not easily available in the policy-making world. INFSO-SKIN uses scenario modelling as a worksite for “reality constructions”, in line with Gellner’s statement quoted above about the constructivist approach: “In dealing with experience, in trying to explain and control it, we accept as legitimate and appropriate to experiment with different conceptual settings, to combine the flow of experience to different ‘objects”’ (Gellner 1990, p. 75). Scenario modelling was employed in the study both for the impact assessment of existing funding policies, where we measured the impact of policy measures by experimenting with different scenarios where these policies are absent, changed or meet different conditions, and for ex-ante evaluation, where we developed a range of potential futures for the European Research Area in ICT by asking what-if questions.

These are in-silico experiments that construct potential futures. Is this then a relativist approach where “anything goes”, because everything is just a construction? For the general aspects of this question, we refer to Part I of this article. There we talk about the “reality requirements” of the constructivist approach, which mediates its claims. For the limits of constructivist ideas applied to SKIN, we refer to Sect. 2.1.

3.2.4 The INFSO-SKIN Example as Seen by the User Community View

The user community view is the most promising, although the most work-intensive mechanism to assess the quality of this policy-modelling exercise.

3.2.4.1 Identifying User Questions

In our example, SKIN was applied to a tender study with a clear client demand behind it, where the questions the simulation needs to answer were more or less predefined

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from the onset of the project. Enough time should, however, be dedicated to identi- fying and discussing the exact set of questions the stakeholders of the work want to see addressed. We found that the best way to do this is applying an iterative process of communication between study team and clients, where stakeholders learn about the scope and applicability of the methods, and where researchers get acquainted with the problems policy makers have to solve and with the kind of decisions for which sound background information is needed. This iterative process should result in an agreed set of questions for the simulation, which will very often decisively differ from the set proposed at the start of the study. In our example, the so-called “steering committee” was assigned to us by the European Commission consisting of policy makers and evaluation experts of DG INFSO.

There are various difficulties and limitations to overcome in identifying user ques- tions. In the case of the DG INFSO study, although the questions under study were outlined in the Tender Specifications in great detail, this was a complicated negotiation process where the stakeholder group:

• Had to find out about the exact nature and direction of their questions while they talked to the study team;

• Had questioned the original set of the Tender Specifications in the meantime and negotiated among each other for an alternative set;

• Did not share the same opinion about what questions should be in the final sample, and how potential questions should be ranked in importance;

• Did not share the same hypotheses about questions in the final sample.

The specification of evaluative questions might be the first time stakeholders talk to each other and discuss their viewpoints.

What is the process for identifying user questions for policy modelling? In the INFSO-SKIN application, the following mechanism was used by the study team and proved to be valuable:

• Scan written project specification by client (in this case the Tender Specifications of DG INFSO) and identify the original set of questions;

• Do a literature review and context analysis for each question (policy background, scope, meaning, etc.) to inform the study team;

• Meet stakeholders to get their views on written project specifications and their view on the context of questions; inform the stakeholders about what the model is about, what it can and cannot do; discuss until stakeholder group and study team is “on the same page”;

• Evaluate the meeting and revise original set of questions if necessary (probably an iterative process between study team and different stakeholders individually where study team acts as coordinator and mediator of the process);

• Meet stakeholders to discuss the final set of questions, get their written consent on this, and get their hypotheses concerning potential answers and potential ways to address the questions;

• Evaluate the meeting and develop experiments that are able to operationalise the hypotheses and address the questions;

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• Meet stakeholders and get their feedback and consent that the experiments meet questions/hypotheses;

• Evaluate the meeting and refine the experimental setup concerning the final set of questions.

This negotiation and discussion process is highly user-driven, interactive, and itera- tive. It requires communicative skills, patience, willingness to compromise on both sides, and motivation to make both ends meet—the formal world of modellers and the narrative world of policy making in practice. The process is highly time-consuming. In our example, we needed about 6 months of a 12-month-contract research study to get to satisfactory results on this first step.

3.2.4.2 Getting Their Best: Users Need to Provide Data

The study team will know best what types of empirical data are needed to inform the policy modelling. In SKIN, data availability is an important issue, because the findings need to be evidence-based and realistic. This is in the best interest of the stakeholders, who need to trust the findings. This will be the more likely to the extent that the simulated data resembles the empirical data known to the user (see Sect. 2.1). However, the study team might discover that the desired data is not available, either because it does not exist or because it is not willingly released by the stakeholders or whoever holds it.

In our example, the stakeholders were data collectors on a big scale themselves. The evaluation unit of DG INFSO employs a data collection group, which provides information about funded projects and organisations at a detailed level. Furthermore, the DG is used to provide data to the study teams of the projects they contract for their evaluation projects. Consequently we benefitted from having a large and clean database concerning all issues the study team was interested in. However, it was still an issue to confirm the existence, quality and availability of the data and check for formats and database requirements. Even if the data is there in principal, enough time should be reserved for data management issues. The quality of the simulation in the eyes of the user will very much depend on the quality of the informing data and the quality of the model calibration.

What would have been the more common process if the study team had not struck lucky as in our example? In other SKIN applications, the following mechanism was used by the study team and proved to be valuable (the ones with asterisks apply to our INFSO-SKIN example as well):

• Identify the rough type of data required for the study from the project specifications • Estimate financial resources for data access in the proposal of project to

stakeholders (this can sometimes happen in interaction with the funding body); • After the second meeting with stakeholders (see Sect. 2.3.1), identify relevant

data concerning variables to answer study questions and address/test hypotheses of Sect. 2.3.1*;

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 51

• Communicate exact data requirements to those stakeholders who are experts on their own empirical data environment*;

• Review existing data bases including the ones stakeholders might hold or can get access to*;

• Meet stakeholders to discuss data issues; help them understand and agree on the scope and limitations of data access*;

• If needed and required by stakeholders, collect data; • Meet stakeholders to discuss the final database; • Evaluate the meeting and develop data-to-model procedures*.

3.2.4.3 Interacting with Users to Check the Validity of Simulation Results

The stakeholders put heavy demands on the study team concerning understanding and trusting the simulation findings. The first and most important is that the clients want to understand the model. To trust results means to trust the process that produced them. Here, the advantage of the adapted SKIN model is that it relies on a narrative that tells the story of the users’ every-day world of decision-making (see Sect. 2.1.1). In the SKIN model, a good example for “reality” requirements is the necessity to model the knowledge and behaviour of agents. Blackboxing knowledge of agents or creating merely reactive simple agents would not have been an option, because stakeholders do not think the world works that way.

The SKIN model is based on empirical quantitative and qualitative research in innovation economics, sociology, science and technology studies, and business stud- ies. Agents and behaviours are informed by what we know about them; the model is calibrated by data from this research. We found that there is a big advantage in having a model where stakeholders can recognise the relevant features they see at work in their social contexts. In setting up and adapting the model to study needs, stakeholders can actively intervene and ask for additional agent characteristics or behavioural rules; they can refine the model and inform blackbox areas where they have information on the underlying processes.

However, here again, we encountered the diversity of stakeholder preferences. Different members of the DG INFSO Steering Committee opted for different changes and modifications of the model. Some were manageable with given time constraints and financial resources; some would have outlived the duration of the project if realised. The final course of action for adapting the model to study needs was the result of discussions between stakeholders about model credibility and increasing complexity and of discussions between stakeholders and the study team concerning feasibility and reducing complexity.

Once the stakeholders were familiar with the features of the model and had con- tributed to its adaptation to study requirements, there was an initial willingness to trust model findings. This was strengthened by letting the model reproduce FP7 data as the baseline scenario that all policy experiments would be benchmarked against. If the networks created by real life and those created by the agent-based model cor- respond closely, the simulation experiments can be characterized as history-friendly

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52 P. Ahrweiler and N. Gilbert

experiments, which reproduce the empirical data and cover the decisive mechanisms and resulting dynamics of the real networks (see standard view).

In presenting the results of the INFSO-SKIN study, however, it became clear that there were, again, certain caveats coming from the user community. The policy analysts did not want to look at a multitude of tables and scan through endless numbers of simulation results for interesting parameters; nor did they expect to watch the running model producing its results, because a typical run lasted 48 hours. Presenting results in an appealing and convincing way required visualisations and interactive methods where users could intuitively understand what they see, had access to more detailed information if wanted, e.g. in a hyperlink structure, and could decide themselves in which format, in which order and in which detail they want to go through findings. This part of the process still needs further work: new visualisation and interactive technologies can help to make simulation results more accessible to stakeholders.

This leads to the last issue to be discussed in this section. What happens after the credibility of simulation results is established? In the INFSO-SKIN study, the objective was policy advice for Horizon 2020. The stakeholders wanted the study team to communicate the results as “recommendations” rather than as “findings”. They required a so-called “utility summary” that included statements about what they should do in their policy domain justified according to the results of the study. Here the study team proved to be hesitant—not due to a lack of confidence in their model, but due to the recognition of its predictive limitations and a reluctance to formulate normative statements, which were seen as a matter of political opinion and not a responsibility of a scientific advisor. The negotiation of the wording in the Utility Summary was another instance of an intense dialogue between stakeholders and study team. Nevertheless, the extent to which the results influenced or were somehow useful in the actual political process of finalising Horizon 2020 policies was not part of the stakeholder feedback after the study ended and is still not known to us. The feedback consisted merely of a formal approval that we had fulfilled the project contract.

3.3 Conclusions

To trust the quality of a simulation means to trust the process that produced its results. This process is not only the one incorporated in the simulation model itself. It is the whole interaction between stakeholders, study team, model, and findings.

The first section of this contribution pointed out the problems of the Standard View and the constructivist view in evaluating social simulations. We argued that a simulation is good when we get from it what we originally would have liked to get from the target; in this, the evaluation of the simulation would be guided by the expectations, anticipations, and experience of the community that uses it. This makes the user community view the most promising mechanism to assess the quality of a policy-modelling exercise.

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3 The Quality of Social Simulation: An Example from Research Policy Modelling 53

The second section looked at a concrete policy-modelling example to test this idea. It showed that the very first negotiation and discussion with the user commu- nity to identify their questions were highly user-driven, interactive, and iterative. It required communicative skills, patience, willingness to compromise on both sides, and motivation to link the formal world of modellers and the narrative world of policy making in practice.

Often, the user community is involved in providing data for calibrating the model. It is not an easy issue to confirm the existence, quality, and availability of the data and check for formats and database requirements. Because the quality of the simulation in the eyes of the user will depend on the quality of the informing data and the quality of the model calibration, much time and effort need to be spent in coordinating this issue with the user community.

Last but not least, the user community has to check the validity of simulation results and has to believe in their quality. Users have to be helped to understand the model, to agree with its processes and ways to produce results, to judge similarity between empirical and simulated data, etc.

The standard view is epistemologically questionable due to the two problems of under-determination of theory and of theory-ladenness of observations; the con- structivist view is difficult due to its inherent relativism, which annihilates its own validity claims. The user community view relies on social model building and model assessment practices and, in a way, bridges the two other views, because it rests on the realism of these practices. This is why we advocate its quality assessment mechanisms.

Summarising, in our eyes, the user community view might be the most promis- ing, but is definitely the most work-intensive mechanism to assess the quality of a simulation. It all depends on who the user community is and whom it consists of: if there is more than one member, the user community will never be homogenous. It is difficult to refer to a “community”, if people have radically different opinions.

Furthermore, there are all sorts of practical contingencies to deal with. People might not be interested, or they might not be willing or able to dedicate as much of their time and attention to the study as needed. There is also the time dimension: the users at the end of a simulation project might not be the same as those who initiated it, as a result of job changes, resignations, promotions, and organisational restructuring. Moreover, the user community and the simulation modellers may affect each other, with the modellers helping in some ways to construct a user community in order to solve the practical contingencies that get in the way of assessing the quality of the simulation, while the user community may in turn have an effect on the modellers (not least in terms of influencing the financial and recognition rewards the modellers receive).

If trusting the quality of a simulation indeed means trusting the process that pro- duced its results, then we need to address the entire interaction process between user community, researchers, data, model, and findings as the relevant assessment mech- anism. Researchers have to be aware that they are codesigners of the mechanisms they need to participate in with the user community for assessing the quality of a social simulation.

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54 P. Ahrweiler and N. Gilbert

References

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Ahrweiler P, Pyka A, Gilbert N (2011) A New model for university-industry links in knowledge- based economies. J Prod Innov Manag 28:218–235

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Ahrweiler P, PykaA, Gilbert N (2014b, forthcoming): Simulating knowledge dynamics in innovation networks: an introduction. In: Gilbert N, Ahrweiler P, Pyka A (eds) Simulating knowledge dynamics in innovation networks. Springer, Heidelberg

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(eds) Innovation networks—theory and practice. Edward Elgar, Cheltenham, pp 169–198 Pyka A, Gilbert N, Ahrweiler P (2007) Simulating knowledge generation and distribution processes

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Chapter 4 Policy Making and Modelling in a Complex World

Wander Jager and Bruce Edmonds

Abstract In this chapter, we discuss the consequences of complexity in the real world together with some meaningful ways of understanding and managing such situations. The implications of such complexity are that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions). We shortly discuss the problems arising from a too-narrow focus on quantification in managing complex systems. We criticise some of the approaches that ignore these difficulties and pretend to predict using simplistic models. However, lack of pre- dictability does not automatically imply a lack of managerial possibilities. We will discuss how some insights and tools from “complexity science” can help with such management. Managing a complex system requires a good understanding of the dynamics of the system in question—to know, before they occur, some of the real possibilities that might occur and be ready so they can be reacted to as responsively as possible. Agent-based simulation will be discussed as a tool that is suitable for this task, and its particular strengths and weaknesses for this are discussed.

4.1 Introduction

Some time ago, one of us (WJ) attended a meeting of specialists in the energy sector. A former minister was talking about the energy transition, advocating for directing this transition; I sighed, because I realized that the energy transition, involving a multitude of interdependent actors and many unforeseen developments, would make a planned direction of such a process a fundamental impossibility.Yet I decided not to interfere, since my comment would have required a mini lecture on the management of complex systems, and in the setting of this meeting this would have required too much time. So the speaker went on, and one of the listeners stood up and asked, “But

W. Jager (�) Groningen Center of Social Complexity Studies, University Groningen, Groningen, The Netherlands e-mail: [email protected]

B. Edmonds Manchester Metropolitan University, Manchester, UK

© Springer International Publishing Switzerland 2015 57 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_4

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58 W. Jager and B. Edmonds

Fig. 4.1 Double pendulum. (Source: Wikipedia)

sir, what if the storage capacity of batteries will drastically improve?” The speakers answered, “this is an uncertainty we cannot include in our models, so in our transition scenarios we don’t include such events”. This remark made clear that, in many cases, policymakers are not aware of the complexities in the systems they operate in, and are not prepared to deal with surprises in systems. Because the transitional idea is being used very frequently to explain wide-ranging changes related to the transformation of our energy system, and the change towards a sustainable society, it seems relevant to address the issue of complexity in this chapter, and discuss the implications for policy making in complex behaving system. After explaining what complexity is, we will discuss the common mistakes being made in managing complex systems. Following that, we will discuss the use of models in policy making, specifically addressing agent-based models because of their capacity to model social complex systems that are often being addressed by policy.

4.2 What is Complexity?

The word “complexity” can be used to indicate a variety of kinds of difficulties. However, the kind of complexity we are specifically dealing with in this chapter is where a system is composed of multiple interacting elements whose possible behavioural states can combine in ways that are hard to predict or characterise. One of the simplest examples is that of a double pendulum (Fig 4.1).

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4 Policy Making and Modelling in a Complex World 59

Although only consisting of a few parts connected by joints, it has complex and un- predictable behaviour when set swinging under gravity. If this pendulum is released, it will move chaotically due to the interactions between the upper (θ1) and lower (θ2) joint. Whereas it is possible to formally represent this simple system in detail, e.g. including aspects such as air pressure, friction in the hinge, the exact behaviour of the double pendulum is unpredictable.1 This is due to the fundamental uncertainty of the precise position of its parts2and the unsolvability of the three-body problem as proven by Bruns and Poincaré in 1887. Just after release, its motion is predictable to a considerable degree of accuracy, but then starts to deviate from any prediction until it is moving in a different manner. Whereas the precise motion at these stages is not predictable, we know that after a while, the swinging motion will become less erratic, and ultimately it will hang still (due to friction). This demonstrates that even in very simple physical systems, interactions may give rise to complex behaviour, expressed in different types of behaviour, ranging from very stable to chaotic. Obviously, many physical systems are much more complicated, such as our atmospheric system. As can be expected, biological or social systems also display complex behaviour be- cause they are composed of large numbers of interacting agents. Also, when such systems are described by a simple set of equations, complex behaviour may arise. This is nicely illustrated by the “logistic equation”, which was originally introduced as a simple model of biological populations in a situation of limited resources (May 1976). Here the population, x, in the next year (expressed as a proportion of its max- imum possible) is determined based on the corresponding value in the last year as rx(1-x), where r is a parameter (the rate of unrestrained population increase). Again, this apparently simple model leads to some complex behaviour. Figure 4.2 shows the possible long-term values of x for different values of r, showing that increasing r creates more possible long-term states for x. Where on the left hand side (r< 3.0) the state of x is fixed, at higher levels the number of possible states increases with the number of states increasing rapidly until, for levels of r above 3.6, almost any state can occur, indicating a chaotic situation. In this case, although the system may be predictable under some circumstances (low r), in others it will not be (higher r).

What is remarkable is that, despite the inherent unpredictability of their environ- ment, organisms have survived and developed intricate webs of interdependence in terms of their ecologies. This is due to the adaptive capacity of organisms, allowing them to self-organise. It is exactly this capacity of organisms to adapt to changing cir- cumstances (learning) that differentiates ‘regular’ complex systems from ‘complex adaptive systems’ (CAS). Hence complex adaptive systems have a strong capacity to self-organise, which can be seen in, i.e. plant growth, the structure of ant nests and the organisation of human society. Yet these very systems have been observed to exist in both stable and unstable stages, with notable transitions between these

1 Obviously predictions can always be made, but it has been proved analytically that the predictive value of models is zero in these cases. 2 Even if one could measure them with extreme accuracy, there would never be complete accuracy due to the uncertainty theorem of Heisenberg (1927).

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60 W. Jager and B. Edmonds

Fig. 4.2 Bifurcation diagram. (Source: Wikipedia)

stages. Ecological science has observed that major transitions in ecological systems towards a different regime (transition) are often preceded by increased variances, slower recovery from small perturbations (critical slowing down) and increased re- turn times (Boettiger and Hastings 2012; Dai and Vorselen et al. 2012; Dakos and Carpenter et al. 2012). A classic example here is that of the transition from a clear lake to a turbid state due to eutrophication. Here an increase in mineral and organic nutrients in the water gives rise to the growth of plants, in particular algae. In the stage preceding to a transition, short periods of increased algal blooms may occur, decreasing visibility and oxygen levels, causing the population of top predating fish hunting on eyesight to decrease, causing a growth in populations of other species, etc. The increased variance (e.g. in population levels of different species in the lake) indicates that a regime shift is near, and that the lake may radically shift from a clear state to a turbid state with a complete different ecosystem, with an attendant loss of local species.

The hope is that for other complex systems, such indicators may also identify the approach of a tipping point and a regime shift or transition (Scheffer et al. 2009). For policy making, this is a relevant perspective, as it helps in understanding what a transition or regime shift is, and has implications for policy development. A transition implies a large-scale restructuring of a system that is composed of many interacting parts. As such, the energy system and our economy at large are examples of complex systems where billions of actors are involved, and a large number of stakeholders such as companies and countries are influencing each other. The transformation from, for example, a fossil fuel-based economy towards a sustainable energy system requires that many actors that depend on each other have to simultaneously change their behaviour. An analogy with the logistic process illustrated in Fig. 4.2 can be made.

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4 Policy Making and Modelling in a Complex World 61

Imagine a move from the lower stable situation x = 0.5 at r = 3.3 to the upper stable situation x = 0.8. This could be achieved by increasing the value of r, moving towards the more turbulent regime of the system and then reducing r again, allowing the new state to be settled into. This implies that moving from one stable regime towards another stable regime may require a period of turbulence where the transition can happen. Something like a period of turbulence demarcating regime shifts is what seems to have occurred during many transitions in the history of the world.

4.3 Two Common Mistakes in Managing Complex Systems

Turbulent stages in social systems are usually experienced as gruesome by policy- makers and managers. Most of them prefer to have grip on a situation, and try to develop and communicate a clear perspective on how their actions will affect future outcomes. Especially in communicating the rationale of their decisions to the out- side world, the complex nature of social systems is often lost. It is neither possible nor particularly useful to try and list all of the “mistakes” that policymakers might make in the face of complex systems, but two of the ways in which systems are oversimplified are quantification and compartmentalisation.

Quantification implies that policy is biased towards those attributes of a system that are easy to quantify. Hence, it comes as no surprise that economic outcomes, in terms of money, are often the dominating criteria in evaluating policy. Often, this results in choosing a solution that will result in the best financial economic outcome. Whereas non-quantifiable outcomes are often acknowledged, usually the bottom line is that “we obviously have to select the most economical viable option” because “money can be spent only once”. In such a case, many other complex and qualitative outcomes might be undervalued or even ignored since the complex system has been reduced to easily measurable quantities. In many situations, this causes resistance to policies, because the non-quantifiable outcomes often have an important impact on the quality of life of people. An example would be the recent earthquakes in the north of the Netherlands due to the extraction of natural gas, where the policy perspective was mainly focussing on compensating the costs of damage to housing, whereas the population experienced a loss of quality of life due to fear and feelings of unfair treatment by the government, qualities that are hard to quantify and were undervalued in the discussion. The more complex a system is, the more appealing it seems to be to get a grip on the decision context by quantifying the problem, often in economical terms. Hence, in many complex problems, e.g. related to investments in sustainable energy, the discussion revolves around returns on investment, whereas other relevant qualities, whereas being acknowledged, lose importance because they cannot be included in the complicated calculations. Further, the ability to encapsulate and manipulate number-based representations in mathematics may give such exercises an appearance of being scientific and hence reinforce the impression that the situation is under control. However, what has happened here is a conflation of indicators with the overall quality of the goals and outcomes themselves. Indicators may well be

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62 W. Jager and B. Edmonds

useful to help judge goals and outcomes; but in complex situations, it is rare that such a judgement can be reduced to such simple dimensions.

Compartimentalization is a second response of many policymakers in trying to simplify complex social systems. This is a strategy whereby a system or organi- sation is split into different parts that act (to a large extent) independently of each other as separated entities, with their own goals and internal structures. As a conse- quence, the policy/management organization will follow the structure of its division into parts. Being responsible for one part of the system implies that a bias emerges towards optimizing the performance of the own part. This is further stimulated by rewarding managers for the performance of the subsystem they are responsible for, independently of the others. However, this approach makes it difficult to account for spillover effects towards other parts of the system, particularly when the outcomes in related parts of the system are more difficult to quantify. An example would be the savings on health care concerning psychiatric care. Reducing the number of maxi- mum number of consults being covered by health insurance resulted in a significant financial savings in health care nationally. However, as a result, more people in need of psychiatric help could not afford this help, and, as a consequence, may have contributed to an increase in problems such as street crime, annoyance, and deviant behaviour. Because these developments are often qualitative in nature, hard num- bers are not available, and hence these effects are more being debated than actually being included in policy development. Interestingly, due to this compartmentalisa- tion, the direct financial savings due to the reduction of the insurance conditions may be surpassed by the additional costs made in various other parts as the system such as policing, costs of crime, and increased need for crisis intervention. Thus, the problems of quantification and compartmentalisation can exacerbate each other: A quantitative approach may facilitate compartmentalisation since it makes measure- ment of each compartment easier and if one takes simple indicates as one’s goals, then it is tempting to reduce institutional structures to separate compartments that can concentrate on these narrow targets. We coin the term “Excellification”—after Microsoft Excel—to express the tendency to use quantitative measurements and compartmentalise systems in getting a grip on systems.

Whereas we are absolutely convinced of the value of using measurements in developing and evaluating policy/management, it is our stance that policy making in complex systems is requiring a deeper level of understanding the processes that guide the developments in the system at hand. When trying to steer policy in the face of a complex and dynamic situation, there are essentially two kinds of strategies being used in developing this understanding: instrumental and representational. We look at these next, before we discuss how agent-based modelling may contribute to understanding and policy making in complex systems.

4.4 Complexity and Policy Making

An instrumental approach is where one chooses between a set of possible policies and then evaluates them according to some assessment of their past effectiveness.

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4 Policy Making and Modelling in a Complex World 63

Fig. 4.3 An illustration of the instrumental approach

Choose one and put it into effect (work out what to do)

actionindicators

Strategy 1

Strategy 2

etc.

Strategy 3

Evaluate how successful strategy was

In future iterations, one then adapts and/or changes the chosen policy in the light of its track record. The idea is illustrated in Fig. 4.3. This can be a highly adaptive approach, reacting rapidly in the light of the current effectiveness of different strate- gies. No initial knowledge is needed for this approach, but rather the better strategies develop over time, given feedback from the environment. Maybe, the purest form of this is the “blind variation and selective retention” of Campbell (1960), where new variants of strategies are produced (essentially) at random, and those that work badly are eliminated, as in biological evolution. The instrumental approach works better when: there is a sufficient range of strategies to choose between, there is an effective assessment of their efficacy, and the iterative cycle of trial and assessment is rapid and repeated over a substantial period of time. The instrumental approach is often used by practitioners who might develop a sophisticated “menu” of what strategies seem to work under different sets of circumstances.

An example of this might be adjusting the level of some policy instrument such as the level of tolls that are designed to reduce congestion on certain roads. If there is still too much congestion, the toll might be raised; if there is too little usage, the toll might be progressively lowered.

The representational approach is a little more complicated. One has a series of “models” of the environment. The models are assessed by their ability to pre- dict/mirror observed aspects of the environment. The best model is then used to evaluate possible actions in terms of an evaluation of the predicted outcomes from those actions and the one with the best outcome chosen to enact. Thus, there are two “loops” involved: One in terms of working out predictions of the models and seeing which best predicts what is observed, and the second is a loop of evaluating possible actions using the best model to determine which action to deploy. Figure 4.4 illus- trates this approach. The task of developing, evaluating, and changing the models is an expensive one, so the predictive power of these models needs to be weighed against this cost. Also, the time taken to develop the models means that this approach is often slower to adapt to changes in the environment than a corresponding instru- mental approach. However, one significant advantage of this approach is that, as a result of the models, one might have a good idea of why certain things were hap- pening in the environment, and hence know which models might be more helpful,

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64 W. Jager and B. Edmonds

Fig. 4.4 An illustration of the representational approach

Choose one, work out predictions of effects of possible actions

actionperception

Model 1

Model 2

etc.

Model 3

Evaluate whether predicitons were accurate

as well as allowing for the development of longer term strategies addressing the root causes of such change. The representational approach is the one generally followed by scientists because they are interested in understanding what is happening.

An example of the representational approach might be the use of epidemiolog- ical models to predict the spread of an animal disease, given different contain- ment/mitigation strategies to deal with the crisis. The models are used to predict the outcomes of various strategies, which can inform the choice of strategy. This prediction can be useful even if the models are being improved, at the same time, due to the new data coming in because of the events.

Of course, these two approaches are frequently mixed. For example, representa- tional models might be used to constrain which strategies are considered within an otherwise instrumental approach (even if the representational models themselves are not very good at prediction). If a central bank is considering what interest rate to set, there is a certain amount of trial and error: thus, exactly how low one has to drop the interest rates to get an economy going might be impossible to predict, and one just has to progressively lower them until the desired effect achieved. However, some theory will also be useful: thus, one would know that dropping interest rates would not be the way to cool an over-heating economy. Thus, even very rough models with relatively poor predictive ability (such as “raising interest rates tends to reduce the volume of economic activity and lowering them increases it”) can be useful.

Complexity theory is useful for the consideration of policy in two different ways. First, it can help provide representational models that might be used to constrain the range of strategies under consideration and, second, can help inform second- order considerations concerning the ways in which policy might be developed and/or adopted—the policy adaption process itself. In the following section, we first look at the nature and kinds of models so as to inform their best use within the policy modelling, and later look at how second-order considerations may inform how we might use such models.

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4 Policy Making and Modelling in a Complex World 65

Fig. 4.5 An illustration in some of the opposing desiderata of models

simplicity

generality

validity

formality

4.4.1 Using Formal Models in Policy Making

The use of models in policy making starts with the question—what the appropriate policy models are? Many models are often available because (1) improving mod- els following the representational approach will yield series of models that further improve the representation of the process in terms of cause–effect relations, and (2) sometimes more extended models are required for explaining a process, whereas often simpler models are used to represent a particular behaviour.

Realising that many models are often available, we still have to keep in mind that any model is an abstraction. A useful model is necessarily simpler than what it represents, so that much is left out—abstracted away. However, the decision as to what needs to be represented in a model and what can be safely left out is a difficult one. Some models will be useful in some circumstances and useless in others. Also, a model that is useful for one purpose may well be useless for another. Many of the problems associated with the use of models to aid the formulation and steering of policy derive from an assumption that a model will have value per se, independent of context and purpose.

One of the things that affect the uses to which models can be put is the compromise that went into the formulation of the models. Figure 4.5 illustrates some of these tensions in a simple way.

These illustrated desiderata refer to a model that is being used. Simplicity is how simple the model is, the extent to which the model itself can be completely understood. Analytically solvable mathematical models, most statistical models, and abstract simulation models are at the relatively simple end of the spectrum. Clearly, a simple model has many advantages in terms of using the model, checking it for bugs and mistakes (Galán et al. 2009), and communicating it. However, when modelling complex systems, such as what policymakers face, such simplicity may not be worth it if gaining it means a loss of other desirable properties. Generality is the extent of the model scope: How many different kinds of situations could the model be usefully applied. Clearly, some level of generality is desirable; otherwise one could only apply

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66 W. Jager and B. Edmonds

the model in a single situation. However, all policy models will not be completely general—there will always be assumptions used in their construction, which limit their generality. Authors are often rather lax about making the scope of their models clear—often implying a greater level of generality that can be substantiated. Finally, validity means the extent to which the model outcomes match what is observed to occur—it is what is established in the process of model validation. This might be as close a match as a point forecast, or as loose as projecting qualitative aspects of possible outcomes.

What policymakers want, above all, is validity, with generality (so they do not have to keep going back to the modellers) and simplicity (so there is an accessible narrative to build support for any associated policy) coming after this. Simplicity and generality are nice if you can get them, but one cannot assume that these are achiev- able (Edmonds 2013). Validity should be an overwhelming priority for modellers; otherwise, they are not doing any sort of empirical science. However, they often put this off into the future, preferring the attractions of the apparent generality offered by analogical models (Edmonds 2001, 2010).

Formality is the degree to which a model is built in a precise language or system. A system of equations or a computer simulation is formal, vague, but intuitive ideas expressed in natural language are informal. It must be remembered that formality for those in the policy world is not a virtue but more of a problem. They may be convinced it is necessary (to provide the backing of “science”), but it means that the model is inevitably somewhat opaque and not entirely under their control. This is the nub of the relationship between modellers and the policy world—if the policy side did not feel any need for the formality, then they would have no need of modellers—they are already skilled at making decisions using informal methods. For the modellers, the situation is reverse. Formality is at the root of modelling, so that they can replicate their results and so that the model can be unambiguously passed to other researchers for examination, critique, and further development (Edmonds 2000). For this reason, we will discuss formality a bit and analyse its nature and consequences.

Two dimensions of formality can be usefully distinguished here, these are:

a. The extent to which the referents of the representation are constrained (“specificity of reference”).

b. The extent to which the ways in which instantiations of the representation can be manipulated are constrained (“specificity of manipulation”).

For example, an analogy expressed in natural language has a low specificity of reference since, what its parts refer to are reconstructed by each hearer in each situation. For example, the phrase “a tidal wave of crime” implies that concerted and highly coordinated action is needed in order to prevent people being engulfed, but the level of danger and what (if anything) is necessary to do must be determined by each listener. In contrast to this is a detailed description where what it refers to is severely limited by its content, e.g. “Recorded burglaries in London rose by 15 % compared to the previous year”. Data are characterised by a high specificity of reference, since what it refers to is very precise, but has a low specificity of manipulation because there are few constraints in what one can do with it.

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A system of mathematics or computer code has a high specificity of manipulation since the ways these can be manipulated are determined by precise rules—what one person infers from them can be exactly replicated by another. Thus, all formal models (the ones we are mostly concentrating on here) have a high specificity of manipulation, but not necessarily a high specificity of representation. A piece of natural language that can be used to draw inferences in many different ways, only limited by the manipulators’ imagination and linguistic ability, has a low specificity of manipulation. One might get the impression that any “scientific” model expressed in mathematics must be formal in both ways. However, just because a representation has high specificity of manipulation, it does not mean that the meaning of its parts in terms of what it represents is well determined.

Many simulations, for example, do not represent anything we observe directly, but are rather explorations of ideas. We, as intelligent interpreters, may mentally fill in what it might refer to in any particular context but these “mappings” to reality are not well defined. Such models are more in the nature of an analogy, albeit one in formal form—they are not testable in a scientific manner since it is not clear as to precisely what they represent. Whilst it may be obvious when a system of mathematics is very abstract and not directly connected with what is observed, simulations (especially agent-based simulations) can give a false impression of their applicability because they are readily interpretable (but informally). This does not mean they are useless for all purposes. For example, Schelling’s abstract simulation of racial segregation did not have any direct referents in terms of anything measurable,3 but it was an effective counterexample that can show that an assumption that segregation must be caused by strong racial prejudice was unsound. Thus, such “analogical models” (those with low specificity of reference) can give useful insights—they can inform thought, but cannot give reliable forecasts or explanations as to what is observed.

In practice, a variety of models are used by modellers in the consideration of any issue, including: informal analogies or stories that summarise understanding and are used as a rough guide to formal manipulation, data models that abstract and represent the situation being modelled via observation and measurement, the simulation or mathematical model that is used to infer something about outcomes from initial situations, representations of the outcomes in terms of summary measures and graphs, and the interpretations of the results in terms of the target situation. When considering very complex situations, it is inevitable that more models will become involved, abstracting different aspects of the target situation in different ways and “staging” abstraction so that the meaning and reference can be maintained. However, good practice in terms of maintaining “clusters” of highly related models has yet to be established in the modelling community, so that a policymaker might well be bewildered by different models (using different assumptions) giving apparently conflicting results. However, the response to this should not be to reject this variety, and enforce comforting (but ultimately illusory) consistency of outcomes, but accept

3 Subsequent elaborations of this model have tried to make the relationship to what is observed more direct, but the original model, however visually suggestive, was not related to any data.

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that it is useful to have different viewpoints from models as much as it is to have different viewpoints from experts. It is the job of policymakers to use their experience and judgement in assessing and combining these views of reality. Of course, equally it is the job of the modellers to understand and explain why models appear to contradict each other and the significance of this as much as they can.

A model that looks scientific (e.g. is composed of equations, hence quantified) might well inspire more confidence than one that does not. In fact, the formality of models is very much a two-edged sword, giving advantages and disadvantages in ways that are not immediately obvious to a nonmodeller. We will start with the disadvantages and then consider the advantages.

Most formal models will be able to output series of numbers composed of mea- sures on the outcomes of the model. However, just because numbers are by their nature precise,4 does not mean that this precision is representative of the certainty to which these outcomes will map to observed outcomes. Thus, numerical outcomes can give a very false sense of security, and lead those involved in policy to falsely think that prediction of such values is possible. Although many forecasters now will add indications of uncertainty “around” forecasts, this can still be deeply misleading as it still implies that there is a central tendency about which future outcomes will gravitate.5

Many modellers are now reluctant to make such predictions because they know how misleading these can be. This is, understandably, frustrating for those involved in policy, whose response might be, “I know its complex, but we do not have the time/money to develop a more sophisticated model so just give me your ‘best guess”’. This attitude implies that some prediction is better than none, and that the reliability of a prediction is monotonic to the amount of effort one puts in. It seems that many imagine that the reliability of a prediction increases with effort, albeit unevenly—so a prediction with a small amount of effort will be better than none at all. Unfortunately, this is far from the case, and a prediction based on a “quick and dirty” method may be more misleading than helpful and merely give a false sense of security.

One of the consequences of the complexity of social phenomena is that the pre- diction of policy matters is hard, rare, and only obtained as a result of the most specific and pragmatic kind of modelling developed over relatively long periods of time.6 It is more likely that a model is appropriate for establishing and understanding candidate explanations of what is happening, which will inform policy making in a less exact manner than prediction, being part of the mix of factors that a policymaker will take into account when deciding action. It is common for policy people to want a prediction of the impact of possible interventions “however rough”, rather than settle for some level of understanding of what is happening. However, this can be

4 Even if, as in statistics, they are being precise about variation and levels of uncertainty of other numbers. 5 This apparent central tendency might be merely the result of the way data are extracted from the model and the assumptions built into the model rather than anything that represents the fundamental behaviour being modelled. 6 For an account of actual forecasting and its reality, see Silver (2012).

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illusory—if one really wanted a prediction “however rough”, one would settle for a random prediction7 dressed up as a complicated “black box” model. If we are wiser, we should accept the complexity of what we are dealing and reject models that give us ill-founded predictions.

Maybe a better approach is to use the modelling to inform the researchers about the kinds of process that might emerge from a situation—showing them possible “trajectories” that they would not otherwise have imagined. Using visualisations of these trajectories and the critical indicators clarifies the complex decision context for policymakers. In this way, the burden of uncertainty and decision making remains with the policymakers and not the researchers, but they will be more intelligently informed about the complexity of what is currently happening, allowing them to “drive” decision making better.

As we have discussed above, one feature of complex systems is that they can result in completely unexpected outcomes, where due to the relevant interactions in the system, a new kind of process has developed resulting in qualitatively different results. It is for this reason that complex models of these systems do not give prob- abilities (since these may be meaningless, or worse be downright misleading) but rather trace some (but not all) of the possible outcomes. This is useful as one can then be as prepared as possible for such outcomes, which otherwise would not have been thought of.

On the positive side, the use of formal modelling techniques can be very helpful for integrating different kinds of understanding and evidence into a more “well- rounded” assessment of options. The formality of the models means that it can be shared without ambiguity or misunderstanding between experts in different domains. This contrasts with communication using natural language where, inevitably, people have different assumptions, different meanings, and different inferences for key terms and systems. This ability to integrate different kinds of expertise turns out to be especially useful in the technique we will discuss next—agent-based simulation.

4.4.2 The Use of Agent-Based Models to Aid Policy Formation

In recent years, agent-based simulation has gained momentum as a tool allowing the computer to simulate the interactions between a great number of agents. An agent- based simulation implies that individuals can be represented as separate computer models that capture their motives and behaviour. Letting these so-called agents in- teract though a network, and confront them with changing circumstances, creates an artificial environment where complex and highly dynamic processes can be stud- ied. Because agent-based models address the interactions between many different agents, they offer a very suitable tool to represent and recreate the complexities in so- cial systems. Hence, agent-based modeling has become an influential methodology

7 Or other null model, such as “what happened last time” or “no change”.

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to study a variety of social systems, ranging from ant colonies to aspects of human society. In the context of agent-based simulation of human behaviour, one of the challenges is connecting the knowledge from behavioural sciences in agent-based models that can be used to model behaviour in some kind of environment. These mod- elled environments may differ largely, and may reflect different (inter)disciplinary fields. Examples of environments where agents can operate in are, e.g. financial markets, agricultural settings, the introduction of new technologies in markets, and transportation systems, just to name a few. A key advantage here is that a model creates a common formal language for different disciplines to communicate. This is important, as it allows for speaking the same language in targeting issues that are interdisciplinary by nature. Rather than taking information from social scientists as an interesting qualitative advice, it becomes possible to actually simulate what the behaviour dynamical effects of policies are. This is, in our view, an important step in addressing interdisciplinary policy issues in an effective way. An additional advantage of social simulation is that formalizing theory and empirical data in mod- els requires researchers to be exact in the assumptions, which, in turn, may result in specific research questions for field and/or lab experiments. Hence, social simulation is a tool that both stimulates the interaction between scientific disciplines, and may stimulate theory development/specification within the behavioural sciences.

An increasing number of agent-based models is being used in a policy context. A recent inventory on the SIMSOC mailing list by Nigel Gilbert8 resulted in a list of modelling projects that in some way were related to actual policy making. Topics included energy systems, littering, water management, crowd dynamics, financial crisis, health management, deforestation, industrial clustering, biogas use, military interventions, diffusion of electric cars, organization of an emergency centre, natural park management, postal service organization, urban design, introduction of renew- able technology, and vaccination programmes. Whereas some models were actually being used by policymakers, in most instances, the models were being used to in- form policy makers about the complexities in the system they were interacting with. The basic idea is that a better understanding of the complex dynamics of the system contributes to understanding how to manage these systems, even if they are unpre- dictable by nature. Here, a comparison can be made with sailing as a managerial process.

Sailing can be seen as a managerial challenge in using different forces that con- stantly change and interact in order to move the ship to a certain destination. In stable and calm weather conditions, it is quite well possible to set the sails in a certain posi- tion and fix the rudder, and make an accurate prediction where of the course the boat will follow. The situation becomes different when you enter more turbulent stages in the system, and strong and variable winds, in combination with bigger waves and streams, requiring the sailor to be very adaptive to the circumstances. A small deviation from the course, due to a gush or a wave, may alter the angle of the wind

8 See mailing list [email protected] Mail distributed by Nigel Gilbert on December 14, 2013, subject: ABMs in action: second summary.

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4 Policy Making and Modelling in a Complex World 71

in the sail, which may give rise to further deviations of the course. This is typically a feedback process, and obviously an experienced sailor is well aware of all these dynamics, and, as a consequence, the sailor responds very adaptive to these small disturbances, yet keeps the long-term outcome—the destination port—also in mind.

The social systems that we are dealing with, in transitions, are way more com- plex than the sailing example. Yet, the underlying rational is the same: the better we learn to understand the dynamics of change, the better we will be capable of coping with turbulences in the process, whilst keeping the long-term goals in focus. Hence, policy aims such that the transition towards a sustainable energy future provides a reasonably clear picture of the direction we are aiming for, but the turbulences in the process towards this future are not well known. Where the sailor has a deep under- standing of the dynamics that govern the behaviour of his boat, for policymakers, this understanding is often limited, as the opening example demonstrated.

Using agent-based models for policy would contribute to a better understand- ing and management of social complex phenomena. First, agent-based models will be useful in identifying under what conditions a social system will behave rela- tively stable (predictable) versus turbulent (unpredictable). This is critical for policy making, because in relatively stable situations, predictions can be made concern- ing the effects of policy, whereas in turbulent regimes, a more adaptive policy is recommended. Adaptive policy implies that the turbulent developments are being followed closely, and that policymakers try to block developments to grow in an undesired direction, and benefit and support beneficial developments. Second, if simulated agents are more realistic in the sense that they are equipped with differ- ent utilities/needs/preferences, the simulations will not only show what the possible behavioural developments are but also reveal the impact on a more psychologi- cal quality-of-life level. Whereas currently many policy models assess behavioural change from a more financial/economical drivers, agent-based models open a pos- sibility to strengthen policy models by including additional outcomes. Examples would be outcomes relating to the stability and support in social networks, and general satisfaction levels.

Agent-based models, thus, can provide a richer and more complex representation of what may be happening within complex and highly dynamic situations, allowing for some of the real possibilities within the system to be explored. This exploration of possibilities can inform the risk analysis of policy, and help ensure that policymakers are ready for more of what the world may throw at them, for example, by having put in place custom-designed indicators that give them the soonest-possible indication that certain kinds of processes or structural changes are underway.

4.5 Conclusions

The bad news for policymakers is that predictive models perform worst exactly at the moment policymakers need them most—during turbulent stages.Yet, we observe that many policymakers, not being aware of the complex nature of the system they

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72 W. Jager and B. Edmonds

are interfering with, still have a mechanistic worldview, and base their decisions on classical predictions. This may be one of the reasons for scepticism by policymakers of any modelling approaches (see e.g. Waldherr and Wijermans 2013). Even nowa- days, when complexity has turned into a buzzword, many policymakers still confuse this concept with “complicatedness”, not embracing the essence and meaning of what complexity means for understanding social systems. As a consequence, still many policymakers are “Cartesian9” in their demand for better predictive models. On the other side, still many modellers working from a mechanistic perspective (e.g. linear and/or generic models), holding out the false hope of “scientifically” predic- tive models, look for more resources to incrementally improve their models, e.g. covering more variables. However, whereas it is sometimes justified to argue for the inclusion of more variables in a model, this will not contribute to a better predictive capacity of the model. As Scott Moss reports in his paper (Moss 2002), there are no reported correct real-time forecasts of the volatile clusters or the post-cluster levels in financial market indices or macroeconomic trade cycles, despite their incremental “refinement” over many years. Characteristically, they predict well in periods where nothing much changes, but miss all the “turning points” where structural change occurs.

Even if policymakers have some understanding of the complex nature of the systems they are managing, they still often respond with “I know it is complex, but how else can I decide policy except by using the numbers I have?”, indicating that the numbers are often an important justification of decisions, even if people are aware of the uncertainties behind them. The example of the former minister in the introduction is a prototypical example of this decision making.

The challenge, hence, is not in trying to convince policymakers of the value of simulation models, but providing them with a deeper level understanding of complex systems. Here, simulation models can provide an important role by creating learning experiences. But before going to simulation models, it might be important to use a strong metaphor in anchoring the core idea of managing complex systems. Sailing offers an excellent metaphor here, because many people know the basics of sailing, and understand that it deals with the management of a ship in sometimes turbulent circumstances. What is critical in this metaphor is that in more turbulent conditions, the crew should become more adaptive to the developments in the system.

Agent-based simulation is increasingly being used as a modelling tool to explore the possibilities and potential impacts of policy making in complex systems. They are inherently possibilistic rather than probabilistic. However, the models being used are usually not very accessible for policymakers. Also, in the context of education, not many models are available that allow for an easy access to experiencing policy making in complex systems. In Chap. 13 of this book, Jager and Van der Vegt suggest using based gaming as a promising venue to make agent-based models more

9 Descartes’mechanistic worldview implies that the universe works like a clockwork, and prediction is possible when one has knowledge of all the wheels, gears, and levers of the clockwork. In policy this translates as the viable society.

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4 Policy Making and Modelling in a Complex World 73

accessible in education and practical policy settings. A setting where valid games are being used to increase our understanding of the processes in complex management issues is expected to contribute to an improvement of the policy-making process in complex systems.

Acknowledgments This chapter has been written in the context of the eGovPoliNet project. More information can be found on http://www.policy-community.eu/.

References

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Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380–400

Dai L, Vorselen D et al (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336(6085):1175–1177

Dakos V, Carpenter RA et al (2012) Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7(7) e41010

Edmonds B (2000) The purpose and place of formal systems in the development of science. CPM report 00–75, MMU, UK (http://cfpm.org/cpmrep75.html)

Edmonds B (2001) The use of models—making MABS actually work. In: Moss S, Davidsson P (eds) Multi agent based simulation. Lecture Notes in Artificial Intelligence 1979. Springer, Berlin, pp 15–32

Edmonds B (2010) Bootstrapping knowledge about social phenomena using simulation models. J Artif Soc Soc Simul 13(1):8 (http://jasss.soc.surrey.ac.uk/13/1/8.html)

Edmonds B (2013) Complexity and context-dependency. Found Sci 18(4):745–755. doi:10.1007/s10699-012-9303-x

Galán JM, Izquierdo LR, Izquierdo SS, Santos JI, del Olmo R, López-Paredes A, Edmonds B (2009) Errors and artefacts in agent-based modelling. J Artif Soc Soc Simul 12(1):1 (http://jasss.soc.surrey.ac.uk/12/1/1.html)

Heisenberg W (1927) Ueber den anschaulichenInhalt der quantentheoretischen. Kinematik and Mechanik Zeitschriftfür Physik 43:172–198. English translation in (Wheeler and Zurek, 1983), pp 62–84

May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261(5560):459–467

Moss S (2002) Policy analysis from first principles. Proc US Natl Acad Sci 99(Suppl 3):7267–7274 Scheffer et al (2009) Early warnings of critical transitions. Nature 461:53–59 Silver N (2012) The signal and the noise: why so many predictions fail-but some don’t. Penguin,

New York Waldherr A, Wijermans N (2013) Communicating social simulation models to sceptical minds.

J Artif Soc Soc Simul 16(4):13 (http://jasss.soc.surrey.ac.uk/16/4/13.html)

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Chapter 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling

Erik Pruyt

Abstract Starting from the state-of-the-art and recent evolutions in the field of system dynamics modeling and simulation, this chapter sketches a plausible near term future of the broader field of systems modeling and simulation. In the near term future, different systems modeling schools are expected to further integrate and accelerate the adoption of methods and techniques from related fields like policy analysis, data science, machine learning, and computer science. The resulting future state of the art of the modeling field is illustrated by three recent pilot projects. Each of these projects required further integration of different modeling and simulation approaches and related disciplines as discussed in this chapter. These examples also illustrate which gaps need to be filled in order to meet the expectations of real decision makers facing complex uncertain issues.

5.1 Introduction

Many systems, issues, and grand challenges are characterized by dynamic com- plexity, i.e., intricate time evolutionary behavior, often on multiple dimensions of interest. Many dynamically complex systems and issues are relatively well known, but have persisted for a long time due to the fact that their dynamic complexity makes them hard to understand and properly manage or solve. Other complex systems and issues—especially rapidly changing systems and future grand challenges—are largely unknown and unpredictable. Most unaided human beings are notoriously bad at dealing with dynamically complex issues—whether the issues dealt with are persistent or unknown. That is, without the help of computational approaches, most human beings are unable to assess potential dynamics of complex systems and issues, and are unable to assess the appropriateness of policies to manage or address them.

E. Pruyt (�) Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands e-mail: [email protected]

Netherlands Institute for Advanced Study, Wassenaar, The Netherlands

© Springer International Publishing Switzerland 2015 75 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_5

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Modeling and simulation is a field that develops and applies computational meth- ods to study complex systems and solve problems related to complex issues. Over the past half century, multiple modeling methods for simulating such issues and for advising decision makers facing them have emerged or have been further devel- oped. Examples include system dynamics (SD) modeling, discrete event simulation (DES), multi-actor systems modeling (MAS), agent-based modeling (ABM), and complex adaptive systems modeling (CAS). All too often, these developments have taken place in distinct fields, such as the SD field or the ABM field, developing into separate “schools,” each ascribing dynamic complexity to the complex underlying mechanisms they focus on, such as feedback effects and accumulation effects in SD or heterogenous actor-specific (inter)actions in ABM. The isolated development within separate traditions has limited the potential to learn across fields and advance faster and more effectively towards the shared goal of understanding complex systems and supporting decision makers facing complex issues.

Recent evolutions in modeling and simulation together with the recent explosive growth in computational power, data, social media, and other evolutions in computer science have created new opportunities for model-based analysis and decision mak- ing. These internal and external evolutions are likely to break through silos of old, open up new opportunities for social simulation and model-based decision making, and stir up the broader field of systems modeling and simulation. Today, different modeling approaches are already used in parallel, in series, and in mixed form, and several hybrid approaches are emerging. But not only are different modeling tradi- tions being mixed and matched in multiple ways, modeling and simulation fields have also started to adopt—or have accelerated their adoption of—useful methods and techniques from other disciplines including operations research, policy analysis, data analytics, machine learning, and computer science. The field of modeling and simulation is consequently turning into an interdisciplinary field in which various modeling schools and related disciplines are gradually being integrated. In prac- tice, the blending process and the adoption of methodological innovations have just started. Although some ways to integrate systems modeling methods and many in- novations have been demonstrated, further integration and massive adoption are still awaited. Moreover, other multi-methods and potential innovations are still in an experimental phase or are yet to be demonstrated and adopted.

In this chapter, some of these developments will be discussed, a picture of the near future state of the art of modeling and simulation is drawn, and a few examples of integrated systems modeling are briefly discussed. The SD method is used to illustrate these developments. Starting with a short introduction to the traditional SD method in Sect. 5.2, some recent and current innovations in SD are discussed in Sect. 5.3, resulting in a picture of the state of modeling and simulation in Sect. 5.4. A few examples are then briefly discussed in Sect. 5.5 to illustrate what these developments could result in and what the future state-of-the-art of systems modeling and simulation could look like. Finally, conclusions are drawn in Sect. 5.6.

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 77

5.2 System Dynamics Modeling and Simulation of Old

System dynamics was first developed in the second half of the 1950s by Jay W. Forrester and was further developed into a consistent method built on specific method- ological choices1. It is a method for modeling and simulating dynamically complex systems or issues characterized by feedback effects and accumulation effects. Feed- back means that the present and future of issues or systems, depend—through a chain of causal relations—on their own past. In SD models, system boundaries are set broadly enough to include all important feedback effects and generative mecha- nisms. Accumulation relates not only to building up real stocks—of people, items, (infra)structures, etc.,—but also to building up mental or other states. In SD mod- els, stock variables and the underlying integral equations are used to group largely homogenous persons/items/. . . and keep track of their aggregated dynamics over time. Together, feedback and accumulation effects generate dynamically complex behavior both inside SD models and—so it is assumed in SD—in real systems.

Other important characteristic of SD are (i) the reliance on relatively enduring conceptual systems representations in people’s minds, aka mental models (Doyle and Ford 1999, p. 414), as prime source of “rich” information (Forrester 1961; Doyle and Ford 1998); (ii) the use of causal loop diagrams and stock-flow diagrams to represent feedback and accumulation effects (Lane 2000); (iii) the use of credibility and fitness for purpose as main criteria for model validation (Barlas 1996); and (iv) the interpretation of simulation runs in terms of general behavior patterns, aka modes of behavior (Meadows and Robinson 1985).

In SD, the behavior of a system is to be explained by a dynamic hypothesis, i.e., a causal theory for the behavior (Lane 2000; Sterman 2000). This causal theory is formalized as a model that can be simulated to generate dynamic behavior. Simulating the model thus allows one to explore the link between the hypothesized system structure and the time evolutionary behavior arising out of it (Lane 2000).

Not surprisingly, these characteristics make SD particularly useful for dealing with complex systems or issues that are characterized by important system feedback effects and accumulation effects. SD modeling is mostly used to model core system structures or core structures underlying issues, to simulate their resulting behavior, and to study the link between the underlying causal structure of issues and models and the resulting behavior. SD models, which are mostly relatively small and manageable, thus allow for experimentation in a virtual laboratory. As a consequence, SD models are also extremely useful for model-based policy analysis, for designing adaptive policies (i.e., policies that automatically adapt to the circumstances), and for testing their policy robustness (i.e., whether they perform well enough across a large variety of circumstances).

1 See Forrester (1991, 2007), Sterman (2007) for accounts of the inception of the SD field. See Sterman (2000), Pruyt (2013) for introductions to SD. And see Forrester (1961, 1969), Homer (2012) for well-known examples of traditional SD.

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In terms of application domains, SD is used for studying many complex social– technical systems and solving policy problems in many application domains, for example, in health policy, resource policy, energy policy, environmental policy, housing policy, education policy, innovation policy, social–economic policy, and other public policy domains. But it is also used for studying all sorts of business dynamics problems, for strategic planning, for solving supply chain problems, etc.

At the inception of the SD method, SD models were almost entirely continuous, i.e., systems of differential equations, but over time more and more discrete and other noncontinuous elements crept in. Other evolutionary adaptations in line with ideas from the earliest days of the field, like the use of Group Model Building to elicit mental models of groups of stakeholders (Vennix 1996) or the use of SD models as engines for serious games, were also readily adopted by almost the entire field. But slightly more revolutionary innovations were not as easily and massively adopted. In other words, the identity and appearance of traditional SD was well established by the mid-1980s and does—at first sight—not seem to have changed fundamentally since then.

5.3 Recent Innovations and Expected Evolutions

5.3.1 Recent and Current Innovations

Looking in somewhat more detail at innovations within the SD field and its adop- tion of innovations from other fields shows that many—often seemingly more revolutionary—innovations have been introduced and demonstrated, but that they have not been massively adopted yet.

For instance, in terms of quantitative modeling, system dynamicists have invested in spatially specific SD modeling (Ruth and Pieper 1994; Struben 2005; BenDor and Kaza 2012), individual agent-based SD modeling as well as mixed and hybrid ABM- SD modeling (Castillo and Saysal 2005; Osgood 2009; Feola et al. 2012; Rahmandad and Sterman 2008), and micro–macro modeling (Fallah-Fini et al. 2014). Examples of recent developments in simulation setup and execution include model calibration and bootstrapping (Oliva 2003; Dogan 2007), different types of sampling (Fiddaman 2002; Ford 1990; Clemson et al. 1995; Islam and Pruyt 2014), multi-model and multi- method simulation (Pruyt and Kwakkel 2014; Moorlag 2014), and different types of optimization approaches used for a variety of purposes (Coyle 1985; Miller 1998; Coyle 1999; Graham andAriza 1998; Hamarat et al. 2013, 2014). Recent innovations in model testing, analysis, and visualization of model outputs in SD include the development and application of new methods for sensitivity and uncertainty analysis (Hearne 2010; Eker et al. 2014), formal model analysis methods to study the link between structure and behavior (Kampmann and Oliva 2008, 2009; Saleh et al. 2010), methods for testing policy robustness across wide ranges of uncertainties (Lempert et al. 2003), statistical packages and screening techniques (Ford and Flynn 2005; Taylor et al. 2010), pattern testing and time series classification techniques

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 79

(Yücel and Barlas 2011;Yücel 2012; Sucullu andYücel 2014; Islam and Pruyt 2014), and machine learning techniques (Pruyt et al. 2013; Kwakkel et al. 2014; Pruyt et al. 2014c). These methods and techniques can be used together with SD models to identify root causes of problems, to identify adaptive policies that properly address these root causes, to test and optimize the effectiveness of policies across wide ranges of assumptions (i.e., policy robustness), etc. From this perspective, these methods and techniques are actually just evolutionary innovations in line with early SD ideas. And large-scale adoption of the aforementioned innovations would allow the SD field, and by extension the larger systems modeling field, to move from “experiential art” to “computational science.”

Most of the aforementioned innovations are actually integrated in particular SD approaches like in exploratory system dynamics modelling and analysis (ESDMA), which is an SD approach for studying dynamic complexity under deep uncertainty. Deep uncertainty could be defined as a situation in which analysts do not know or cannot agree on (i) an underlying model, (ii) probability distributions of key variables and parameters, and/or (iii) the value of alternative outcomes (Lempert et al. 2003). It is often encountered in situations characterized by either too little information or too much information (e.g., conflicting information or different worldviews). ESDMA is the combination of exploratory modeling and analysis (EMA), aka robust decision making, developed during the past two decades (Bankes 1993; Lempert et al. 2000; Bankes 2002; Lempert et al. 2006) and SD modeling. EMA is a research methodology for developing and using models to support decision making under deep uncertainty. It is not a modeling method, in spite of the fact that it requires computational models. EMA can be useful when relevant information that can be exploited by building computational models exists, but this information is insufficient to specify a single model that accurately describes system behavior (Kwakkel and Pruyt 2013a). In such situations, it is better to construct and use ensembles of plausible models since ensembles of models can capture more of the un/available information than any individual model (Bankes 2002). Ensembles of models can then be used to deal with model uncertainty, different perspectives, value diversity, inconsistent information, etc.—in short, with deep uncertainty.2

In EMA (and thus in ESDMA), the influence of a plethora of uncertainties, includ- ing method and model uncertainty, are systematically assessed and used to design policies: sampling and multi-model/multi-method simulation are used to generate ensembles of simulation runs to which time series classification and machine learning techniques are applied for generating insights. Multi-objective robust optimization (Hamarat et al. 2013, 2014) is used to identify policy levers and define policy triggers, and by doing so, support the design of adaptive robust policies. And regret-based approaches are used to test policy robustness across large ensembles of plausible runs (Lempert et al. 2003). EMA and ESDMA can be performed with TU Delft’s

2 For ESDMA, see among else Pruyt and Hamarat (2010), Logtens et al. (2012), Pruyt et al. (2013), Kwakkel and Pruyt (2013a, b), Kwakkel et al. (2013), Pruyt and Kwakkel (2014).

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EMA workbench software, which is an open source tool3 that integrates multi- method, multi-model, multi-policy simulation with data management, visualization, and analysis.

The latter is just one of the recent innovations in modeling and simulation software and platforms: online modeling and simulation platforms, online flight simulator and gaming platforms, and packages for making hybrid models have been developed too. And modeling and simulation across platforms will also become reality soon: the eXtensible Model Interchange LanguagE (XMILE) project (Diker and Allen 2005; Eberlein and Chichakly 2013) aims at facilitating the storage, sharing, and combination of simulation models and parts thereof across software packages and across modeling schools and may ease the interconnection with (real-time) databases, statistical and analytical software packages, and organizational information and com- munication technology (ICT) infrastructures. Note that this is already possible today with scripting languages and software packages with scripting capabilities like the aforementioned EMA workbench.

5.3.2 Current and Expected Evolutions

Three current evolutions are expected to further reinforce this shift from “experiential art” to “computational science.”

The first evolution relates to the development of “smarter” methods, techniques, and tools (i.e., methods, techniques, and tools that provide more insights and deeper understanding at reduced computational cost). Similar to the development of formal model analysis techniques that smartened the traditional SD approach, new meth- ods, techniques, and tools are currently being developed to smarten modeling and simulation approaches that rely on “brute force” sampling, for example, adaptive output-oriented sampling to span the space of possible dynamics (Islam and Pruyt 2014) or smarter machine learning techniques (Pruyt et al. 2013; Kwakkel et al. 2014; Pruyt et al. 2014c) and time series classification techniques (Yücel and Barlas 2011; Yücel 2012; Sucullu and Yücel 2014; Islam and Pruyt 2014), and (multi-objective) robust optimization techniques (Hamarat et al. 2013, 2014).

Partly related to the previous evolution are developments relates to “big data,” data management, and data science. Although traditional SD modeling is sometimes called data-poor modeling, it does not mean it is, nor should be. SD software packages allow one to get data from, and write simulation runs to, databases. Moreover, data are also used in SD to calibrate parameters or bootstrap parameter ranges. But more could be done, especially in the era of “big data.” Big data simply refers here to much more data than was until recently manageable. Big data requires data science techniques to make it manageable and useful. Data science may be used in

3 The EMA workbench can be downloaded for free from http://simulation.tbm.tudelft.nl/ ema-workbench/contents.html

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 81

modeling and simulation (i) to obtain useful inputs from data (e.g., from real-time big data sources), (ii) to analyze and interpret model-generated data (i.e., big artificial data), (iii) to compare simulated and real dynamics (i.e., for monitoring and control), and (iv) to infer parts of models from data (Pruyt et al. 2014c). Interestingly, data science techniques that are useful for obtaining useful inputs from data may also be made useful for analyzing and interpreting model-generated data, and vice versa. Online social media are interesting sources of real-world big data for modeling and simulation, both as inputs to models, to compare simulated and real dynamics, and to inform model development or model selection. There are many application domains in which the combination of data science and modeling and simulation would be beneficial. Examples, some of which are elaborated below, include policy making with regard to crime fighting, infectious diseases, cybersecurity, national safety and security, financial stress testing, energy transitions, and marketing.

Another urgently needed innovation relates to model-based empowerment of de- cision makers. Although existing flight simulator and gaming platforms are useful for developing and distributing educational flight simulators and games, and interfaces can be built in SD packages, using them to develop interfaces for real-world real-time decision making and integrating them into existing ICT systems is difficult and time consuming. In many cases, companies and organizations want these capabilities in- house, even in their boardroom, instead of being dependent on analyses by external or internal analysts. The latter requires user-friendly interfaces on top of (sets of) models possibly connected to real-time data sources. These interfaces should allow for experimentation, simulation, thoroughly analysis of simulation results, adaptive robust policy design, and policy robustness testing.

5.4 Future State of Practice of Systems Modeling and Simulation

These recent evolutions in modeling and simulation together with the recent explosive growth in computational power, data, social media, and other evolutions in computer science may herald the beginning of a new wave of innovation and adoption, moving the modeling and simulation field from building a single model to simultaneously simulating multiple models and uncertainties; from single method to multi-method and hybrid modeling and simulation; from modeling and simulation with sparse data to modeling and simulation with (near real-time) big data; from simulating and analyzing a few simulation runs to simulating and simultaneously analyzing well- selected ensembles of runs; from using models for intuitive policy testing to using models as instruments for designing adaptive robust policies; and from developing educational flight simulators to fully integrated decision support.

For each of the modeling schools, additional adaptations could be foreseen too. In case of SD, it may for example involve a shift from developing purely endoge- nous to largely endogenous models; from fully aggregated models to sufficiently spatially explicit and heterogenous models; from qualitative participatory modeling

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Fig. 5.1 Picture of the state of science/future state of the art of modeling and simulation

to quantitative participatory simulation; and from using SD to combining problem structuring and policy analysis tools, modeling and simulation, machine learning techniques, and (multi-objective) robust optimization.

Adoption of these recent, current, and expected innovations could result in the future state of the art4 of systems modeling as displayed in Fig. 5.1. As indicated by (I) in Fig. 5.1, it will be possible to simultaneously use multiple hypotheses (i.e., simulation models from the same or different traditions or hybrids), for different goals including the search for deeper understanding and policy insights, experimentation in a virtual laboratory, future-oriented exploration, robust policy design, and robustness testing under deep uncertainty. Sets of simulation models may be used to represent different perspectives or plausible theories, to deal with methodological uncertainty, or to deal with a plethora of important characteristics (e.g., agent characteristics, feedback and accumulation effects, spatial and network effects) without necessarily having to integrate them in a single simulation model. The main advantages of using multiple models for doing so are that each of the models in the ensemble of models remains manageable and that the ensemble of simulation runs generated with the

4 Given the fact that it takes a while before innovations are adopted by software developers and practitioners, this picture of the current state of science is at the same time a plausible picture of the medium term future of the field of modeling and simulation.

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 83

ensemble of models is likely to be more diverse which allows for testing policy robustness across a wider range of plausible futures.

Some of these models may be connected to real-time or near real-time data streams, and some models may even be inferred in part with smart data science tools from data sources (see (II) in Fig. 5.1). Storing the outputs of these simulation models in databases and applying data science techniques may enhance our under- standing, may generate policy insights, and may allow for testing policy robustness across large multidimensional uncertainty spaces (see (III) in Fig. 5.1). And user- friendly interfaces on top of these interconnected models may eventually empower policy makers, enabling them to really do model-based policy making.

Note, however, that the integrated systems modeling approach sketched in Fig. 5.1 may only suit a limited set of goals, decision makers, and issues. Single model simulation properly serves many goals, decision makers, and issues well enough for multi-model/multi-method, data-rich, exploratory, policy-oriented approaches not to be required. However, there are most certainly goals, decision makers, and issues that do.

5.5 Examples

Although all of the above is possible today, it should be noted that this is the current state of science, not the state of common practice yet. Applying all these methods and techniques to real issues is still challenging, and shows where innovations are most needed. The following examples illustrate what is possible today as well as what the most important gaps are that remain to be filled.

The first example shows that relatively simple systems models simulated under deep uncertainty allow for generating useful ensembles of many simulation runs. Using methods and techniques from related disciplines to analyze the resulting arti- ficial data sets helps to generate important policy insights. And simulation of policies across the ensembles allows to test for policy robustness. This first case nevertheless shows that there are opportunities for multi-method and hybrid approaches as well as for connecting systems models to real-time data streams.

The second example extends the first example towards a system-of-systems ap- proach with many simulation models generating even larger ensembles of simulation runs. Smart sampling and scenario discovery techniques are then required to reduce the resulting data sets to manageable proportions.

The third example shows a recent attempt to develop a smart model-based decision-support system for dealing with another deeply uncertain issue. This ex- ample shows that it is almost possible to empower decision makers. Interfaces with advanced analytical capabilities as well as easier and better integration with existing ICT systems are required though. This example also illustrates the need for more advanced hybrid systems models as well as the need to connect systems models to real-time geo-spatial data.

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5.5.1 Assessing the Risk, and Monitoring, of New Infectious Diseases

The first case, which is described in more detail in (Pruyt and Hamarat 2010; Pruyt et al. 2013), relates to assessing outbreaks of new flu variants. Outbreaks of new (vari- ants of) infectious diseases are deeply uncertain. For example, in the first months after the first reports about the outbreak of a new flu variant in Mexico and the USA, much remained unknown about the possible dynamics and consequences of this pos- sible epidemic/pandemic of the new flu variant, referred to today as new influenza A(H1N1)v. Table 5.1 shows that more and better information became available over time, but also that many uncertainties remained. However, even with these remaining uncertainties, it is possible to model and simulate this flu variant under deep uncer- tainty, for example with the simplistic simulation model displayed in Fig. 5.2, since flu outbreaks can be modeled.

Simulating this model thousands of times over very wide uncertainty ranges for each of the uncertain variables generates the 3D cloud of potential outbreaks dis- played in Fig. 5.3a. In this figure, the worst flu peak (0–50 months) is displayed on the X-axis, the infected fraction during the worst flu peak (0–50 %) is displayed on the Y -axis, and the cumulative number of fatal cases in the Western world (0– 50.000.000) is displayed on the Z-axis. This 3D plot shows that the most catastrophic outbreaks are likely to happen within the first year or during the first winter season following the outbreak. Using machine learning algorithms to explore this ensemble of simulation runs helps to generate important policy insights (e.g., which policy levers to address). Testing different variants of the same policy shows that adaptive policies outperform their static counterparts (compare Fig. 5.3b and c). Figure 5.3d finally shows that adaptive policies can be further improved using multi-objective robust optimization.

However, taking deep uncertainty seriously into account would require simulating more than a single model from a single modeling method: it would be better to simultaneously simulate CAS, ABM, SD, and hybrid models under deep uncertainty and use the resulting ensemble of simulation runs. Moreover, near real-time geo- spatial data (from twitter, medical records, etc.) may also be used in combination with simulation models, for example, to gradually reduce the ensemble of model- generated data. Both suggested improvements would be possible today.

5.5.2 Integrated Risk-Capability Analysis under Deep Uncertainty

The second example relates to risk assessment and capability planning for National Safety and Security. Since 2001, many nations have invested in the development of all-hazard integrated risk-capability assessment (IRCA) approaches. All-hazard IRCAs integrate scenario-based risk assessment, capability analysis, and capability- based planning approaches to reduce all sorts of risks—from natural hazards, over technical failures to malicious threats—by enhancing capabilities for dealing with

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 85

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86 E. Pruyt

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 87

Fig. 5.3 3D scatter plots of 20,000 Latin-Hypercube samples for region 1 with X-axis: worst flu peak (0–50 months); Y -axis: infected fraction during the worst flu peak (0–50 %); Z-axis: fatal cases (0–5 × 107)

them. Current IRCAs mainly allow dealing with one or a few specific scenarios for a limited set of relatively simple event-based and relatively certain risks, but not for dealing with a plethora of risks that are highly uncertain and complex, combina- tions of measures and capabilities with uncertain and dynamic effects, and divergent opinions about degrees of (un)desirability of risks and capability investments.

The next generation model-based IRCAs may solve many of the shortcomings of the IRCAs that are currently being used. Figure 5.4 displays a next generation IRCA for dealing with all sorts of highly uncertain dynamic risks. This IRCA approach, described in more detail in Pruyt et al. (2012), combines EMA and modeling and simulation, both for the risk assessment and the capability analysis phases. First, risks—like outbreaks of new flu variants—are modeled and simulated many times across their multidimensional uncertainty spaces to generate an ensemble of plausible risk scenarios for each of the risks. Time series classification and machine learning techniques are then used to identify much smaller ensembles of exemplars that are representative for the larger ensembles. These ensembles of exemplars are then used as inputs to a generic capability analysis model. The capability analysis model is subsequently simulated for different capabilities strategies under deep uncertainty (i.e., simulating the uncertainty pertaining to their effectiveness) over all ensembles of exemplars to calculate the potential of capabilities strategies to reduce these risks.

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Fig. 5.4 Model-based integrated risk-capability analysis (IRCA)

Finally, multi-objective robust optimization helps to identify capabilities strategies that are robust.

Not only does this systems-of-systems approach allow to generate thousands of variants per risk type over many types of risks and to perform capability analy- ses across all sorts of risk and under uncertainty, it also allows one to find sets of capabilities that are effective across many uncertain risks. Hence, this integrated model-based approach allows for dealing with capabilities in an all-hazard way under deep uncertainty.

This approach is currently being smartened using adaptive output-oriented sam- pling techniques and new time-series classification methods that together help to identify the largest variety of dynamics with the minimal amount of simulations. Covering the largest variety of dynamics with the minimal amount of exemplars is desirable, for performing automated multi-hazard capability analysis over many risks is—due to the nature of the multi-objective robust optimization techniques used— computationally very expensive. This approach is also being changed from a multi- model approach into a multi-method approach. Whereas, until recently, sets of SD models were used; there are good reasons to extend this approach to other types of systems modeling approaches that may be better suited for particular risks or—using multiple approaches—help to deal with methodological uncertainty. Finally, settings of some of the risks and capabilities, as well as exogenous uncertainties, may also be fed with (near) real-world data.

5.5.3 Policing Under Deep Uncertainty

The third example relates to another deeply uncertain issue, high-impact crimes (HIC). An SD model and related tools (see Fig. 5.5) were developed some years ago in view of increasing the effectiveness of the fight against HIC, more specifically the fight against robbery and burglary. HICs require a systemic perspective and approach:

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5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 89

Fig. 5.5 (I) Exploratory system dynamics modelling and analysis (ESDMA) model, (II) interface for policy makers, (III) analytical module for analyzing the high-impact crimes (HIC)system under deep uncertainty, (IV) real-world pilots based on analyses, and (V) monitoring of real-world data from the pilots and the HIC system

These crimes are characterized by important systemic effects in time and space, such as learning and specialization effects, “waterbed effects” between different HICs and precincts, accumulations (prison time) and delays (in policing and jurisdiction), preventive effects, and other causal effects (ex-post preventive measures). HICs are also characterized by deep uncertainty: Most perpetrators are unknown and even though their archetypal crime-related habits may be known to some extent at some point in time, accurate time and geographically specific predictions cannot be made. At the same time, is part of the HIC system well known and is a lot of real-world information related to these crimes available.

Important players in the HIC system besides the police and (potential) perpetrators are potential victims (households and shopkeepers), partners in the judicial system (the public prosecution service, the prison system, etc.). Hence, the HIC system is dynamically complex, deeply uncertain, but also data rich, and contingent upon external conditions.

The main goals of this pilot project were to support strategic policy making under deep uncertainty and to test and monitor the effectiveness of policies to fight HIC. The SD model (I) was used as an engine behind the interface for policy makers (II) to explore plausible effects of policies under deep uncertainty and identify real- world pilots that could possibly increase the understanding about the system and effectiveness of interventions (III), to implement these pilots (IV), and monitor their outcomes (V). Real-world data from the pilots and improved understanding about the functioning of the real system allow for improving the model.

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Today, a lot of real-world geo-spatial information related to HICs is available online and in (near) real time which allows to automatically update the data and model, and hence, increase its value for the policy makers. The model used in this project was an ESDMA model. That is, uncertainties were included by means of sets of plausible assumptions and uncertainty ranges. Although this could already be argued to be a multi-model approach, hybrid models or a multi-method approach would really be needed to deal more properly with systems, agents, and spatial characteristics. Moreover, better interfaces and connectors to existing ICT systems and databases would also be needed to turn this pilot into a real decision-support system that would allow chiefs of police to experiment in a virtual world connected to the real world, and to develop and test adaptive robust policies on the spot.

5.6 Conclusions

Recent and current evolutions in modeling and simulation together with the recent explosive growth in computational power, data, social media, and other evolutions in computer science have created new opportunities for model-based analysis and decision making.

Multi-method and hybrid modeling and simulation approaches are being devel- oped to make existing modeling and simulation approaches appropriate for dealing with agent system characteristics, spatial and network aspects, deep uncertainty, and other important aspects. Data science and machine learning techniques are currently being developed into techniques that can provide useful inputs for simulation models as well as for building models. Machine learning algorithms, formal model analysis methods, analytical approaches, and new visualization techniques are being devel- oped to make sense of models and generate useful policy insights. And methods and tools are being developed to turn intuitive policy making into model-based policy design. Some of these evolutions were discussed and illustrated in this chapter.

It was also argued and shown that easier connectors to databases, to social media, to other computer programs, and to ICT systems, as well as better interfacing software need to be developed to allow any systems modeler to turn systems models into real decision-support systems. Doing so would turn the art of modeling into the computational science of simulation. It would most likely also shift the focus of attention from building a model to using ensembles of systems models for adaptive robust decision making.

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Chapter 6 Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making: A Comparative Analysis

Dragana Majstorovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis and Petra Ahrweiler

Abstract Using computer simulations in examining, explaining and predicting so- cial processes and relationships as well as measuring the possible impact of policies has become an important part of policy-making. This chapter presents a compara- tive analysis of simulation models utilised in the field of policy-making. Different models and modelling theories and approaches are examined and compared to each other with respect to their role in public decision-making processes. The analysis has shown that none of the theories alone is able to address all aspects of complex policy interactions, which indicates the need for the development of hybrid simula- tion models consisting of a combinatory set of models built on different modelling theories. Building such hybrid simulation models will also demand the development of new and more comprehensive simulation modelling platforms.

D. Majstorovic (�) · M. A. Wimmer University of Koblenz-Landau, Koblenz, Germany e-mail: [email protected]

M. A. Wimmer e-mail: [email protected]

R. Lay-Yee · P. Davis Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Private Bag 92019, 1142 Auckland, New Zealand e-mail: [email protected]

P. Davis e-mail: [email protected]

P. Ahrweiler EA European Academy of Technology and Innovation Assessment GmbH, Bad Neuenahr-Ahrweiler, Germany e-mail: [email protected]

© Springer International Publishing Switzerland 2015 95 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_6

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96 D. Majstorovic et al.

6.1 Introduction

Using computer simulation as a tool in examining, explaining and predicting social processes and relationships started intensively during 1990s (Gilbert and Troitzsch 2005). Since 2000s, a growing recognition of simulation models playing a role in public decision modelling processes can be noted (van Egmond and Zeiss 2010). One reason for this increased attention is that simulation models enable the examination of complex social processes and interactions between different entities and the potential impact of policies. For example, simulation models can be used to examine the impact of measures such as school closure and vaccination in stopping the spread of influenza as the cases described in Sect. 3.1 and 3.2 demonstrate; or to examine the influence of different policies in the early years of life as the case outlined in Sect. 3.3 evidences.

This chapter presents a comparative analysis of different simulation models with respect to their role in public decision-making processes. The focus is on investigating the differences between simulation models and their underlying modelling theories in order to find variables that impact the effectiveness of the usage of simulation models in policy-making. The ultimate goal is to provide an understanding of the peculiarities and the added value of different kinds of simulation models generated on the basis of particular modelling approaches. The chapter also aims at giving indications of how existing approaches to policy simulation can and should be combined to effectively support public policy-making in a comprehensive way.

This comparative analysis was performed as part of the eGovPoliNet1 initia- tive, which aims at developing an international multidisciplinary policy community in information and communication (ICT) solutions for governance and policy modelling. eGovPoliNet brings researchers from different disciplines and com- munities together for sharing ideas, discussing knowledge assets and developing joint research findings. The project fosters a multidisciplinary approach to in- vestigate different concepts in policy modelling. In investigating these concepts, researchers from different disciplines (such as information systems, e-government and e-participation, computer science, social sciences, sociology, psychology, or- ganisational sciences, administrative sciences, etc.) collaborate to study the—so far mostly mono-disciplinary—approaches towards policy modelling. With this ap- proach, eGovPoliNet aims at contributing to overcoming the existing fragmentation of research in policy modelling across different disciplines.

The research carried out in this paper was based on the literature study of policy modelling approaches whereby the authors collaborated with expertise from their own academic background. On the other hand, a comparative analysis of five differ- ent simulation models was performed using a framework of comparison developed along the eGovPoliNet initiative. The selection of the cases was based on the authors’

1 eGovPoliNet—Building a global multidisciplinary digital governance and policy modelling re- search.and practice community. See http://www.policy-community.eu/ (last access: 28th July 2014).

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 97

access to and involvement in generating the particular models. Accordingly, the re- spective authors also elaborated the individual descriptions of the simulation models. The subsequent comparison and synthesis of simulation models based on different modelling approaches was done in a collaborative way. Findings were developed jointly and present views of different disciplines concerning the role of simulation models in policy modelling as well as possible ways of advancement of the usage of combinations of simulation models and joint elaborations thereof.

The key research questions guiding the comparative investigation of different simulation models are twofold: (1) What particular modelling theories, frameworks and/or methods build the theoretical and methodical foundations of simulation mod- els in policy modelling? (2) In what way do simulation models developed on the basis of different foundations (cf. question (1)) differ and what lessons can be drawn from using different simulation models in policy modelling? To answer research question (1), the different theoretical and methodical grounds of simulation approaches will be studied, while for research question (2), five different simulation models will be compared and analysed.

The chapter is organised as follows: Sect. 6.2 first provides an understanding of the key terms and subsequently examines three different and widely used theories and approaches to simulation modelling (system dynamics, micro-simulation and agent- based modelling (ABM)) in order to establish common grounds for the research context. Subsequently, a framework for the comparative analysis of simulation mod- els based on these modelling paradigms is introduced and five different simulation models are analysed in Sect. 6.3 (VirSim, MicroSim, MEL-C, OCOPOMO’s Kosice model and SKIN). Then in Sect. 6.4, these models are compared and discussed to extract features of usage, benefits and the main characteristics of specific approaches to simulation modelling in policy-making. Some reflections on the research and practice implications as well as further research needs are also drawn from the com- parative analysis. We conclude with a reflection on the results and insights gathered in Sect. 6.5.

6.2 Foundations of Simulation Modelling

Gilbert and Troitzsch define a simulation model as ‘a simplification—smaller, less detailed, less complex, or all of these together—of some other structure or system’ (Gilbert and Troitzsch 2005). A simulation model is a computer program that captures the behaviour of a real-world system and its input and possible output processes. It relies on data from the real world to create an artificial one that mimics the original but upon which experiments can be performed (Gilbert and Troitzsch 2005. According to Gilbert and Troitzsch, simulation models are useful for many reasons—it is easier, less expensive and in many cases the only appropriate way (e.g. spreading of a disease) or only feasible way (e.g. the consequences of some policy decisions can be seen only many years ahead as is the case with urbanisation) of examining possible impacts of policies. The output of a simulation is a set of measurements describing

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the observable reactions and a performance of a real-world system. For example, simulation models may produce forecasts or projections as output into the future, hence supporting policy-making processes, while stakeholders could use simulation models as support tools in examining possible impacts of different policies(Gilbert and Troitzsch 2005. Simulation models can also be used for a better understanding of real-world processes and relationships (Gilbert and Troitzsch 2005, for entertainment (e.g. the simulation game MoPoS2, where a player is a central bank governor), as well as for education and training purposes (e.g. the simulation model GAIM3). On a higher abstraction level, simulation models can even be used for the formalisation of social theories producing social science specifications (Gilbert and Troitzsch 2005).

Different paradigms (i.e. approaches and theories) to simulation modelling ex- ist in the literature. They vary in aspects of the reality they model as well as in methods they use to produce a simulation model (Gilbert and Troitzsch 2005. Three approaches are considered in this chapter: system dynamics as a representative type of macro-simulation, ABM as a representative type of modelling social behaviour of groups, and micro-simulation focusing on modelling individual’s evolution. These approaches have been selected as they are well-known and widely used.

The system dynamics approach models a situation at a global level to describe a real-world system using analytical means via systems of differential equations (Gilbert and Troitzsch 2005. A real-world system is described and analysed as a whole at the macro-level (Forrester 1961) and represented using flow diagrams and internal feedback loops (Harrison et al. 2007). Such a model does not require much data and the output of the model consists of plots describing behaviour and the changes of the initial values of the variables and parameters of the model over time. To describe behaviour of the real-world process accurately, a model needs to be run many times with different parameter values (Maria 1997). A typical use of system dynamics models is macro-economic modelling as well as for describing the impact of policies during, for example, a spread of a disease. According to (Astolfi et al. 2012), system dynamics models are well suited for predicting short-term policy impacts.

Complex policy issues require approaches that enable research synthesis and the use of systems thinking (Milne et al. 2014). Micro-simulation modelling has the potential to represent systems and processes in various social domains and to test their functioning for policy purposes (Anderson and Hicks 2011; Zaidi et al. 2009). A micro-simulation model is based on empirical individual-level data and it can account for social complexity, heterogeneity, and change (Orcutt 1957; Spielauer 2011). Micro-simulation operates at the level of individual units, each with a set of associated attributes as a starting point. A set of rules, for example equations derived from statistical analysis of (often multiple) survey data sets, is then applied in a stochastic manner to the starting sample to simulate changes in state or behaviour.

2 MoPoS—A monetary Policy Simulation Game (Lengwiler 2004). 3 GAIM—Gestione Accoglienza IMmigrati (Sedehi 2006) is used for the training of foreign intercultural mediators in the immigration housing management courses.

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Modifications of influential factors can then be carried out to test hypothetical ‘what if’scenarios on a key outcome of policy interest (Davis et al. 2010). Micro-simulation can integrate, and accommodate the manipulation of, and the effects of variables across multiple model equations (often derived from multiple data sources) in a single simulation run. Thus, each otherwise separate equation is given its social context and influence among the other equations, representing a system of interdependent social processes.

Gilbert defines ABM as ‘a computational method that enables a researcher to cre- ate, analyse and experiment with models composed of agents that interact within an environment’ (Gilbert 2007). In artificial intelligence, agents are ‘self-contained pro- grams that can control their own actions based on their perceptions of the operating environment’ (Gilbert and Troitzsch 2005). Applied to social processes, agents are individuals or groups of individuals aware of their environment and at the same time proactive in interactions with each other and their surroundings. Agent-based simu- lation models capture and explain the behaviour of agents and the dynamics of their social interactions, and they usually do not assume future predictions (Srbljanovic and Skunca 2003, Gilbert and Troitzsch 2005). ABM is considered a powerful tool for developing, testing and formalising social theories and examining complex social interactions (Gilbert and Troitzsch 2005). For example, agent-based simulations are able to describe complex social phenomena at a global macro-level emerging from simple micro-level interactions between the agents (Srbljanovic and Skunca 2003). The application of ABM offers two major advantages (Gilbert and Troitzsch 2005): a capability to show from where collective phenomena come based on isolation of crit- ical behaviour and the main agents, and a possibility to explore various alternatives of development.

Building a simulation model means developing a computer program either from scratch or from adapting existing models. To achieve this, tools such as AnyLogic4, NetLogo5, etc.6 are being used. Prior to programming the simulation model, ap- propriate knowledge about the policy context that should be explored needs to be collected. Different literature provide indications of the steps in an ordered process as shown in Fig. 6.1. It is to be noted that not necessarily every step is carried out by a simulation modeller. Depending on the expertise of programmers or policy analysts, steps 1 and 2 are in some cases merged, or steps 3 and 4 are not differentiated. In the simplest case, an expert policy modeller might even just perform steps 1, 4 and 5. However, to support a wider understanding of policy modelling, the sharing of the overall concept of analysis and programming, and a higher quality of simulation models, the performance of all five steps is highly recommended.

The first three steps to generate a simulation model are to (1) collect source data, (2) to develop a conceptual model and (3) to design the simulation model. These

4 http://www.anylogic.com/ (last access: 28th July 2014). 5 http://ccl.northwestern.edu/netlogo/ (last access: 28th July 2014). 6 A more detailed overview of tools and technologies supporting policy making is provided in (Kamateri et al. 2014).

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Fig. 6.1 Generic steps for developing simulation models

Program the simula on model

Conceptual modelling

Simula on model design

Analysis of source data

Verifica on and Valida on

steps form the analytical work of policy modelling and can also be labelled ‘policy analysis’ and ‘conceptual modelling’. The ways and methods to collect and analyse source data (step 1) depend on the type of simulation model to be generated and its underlying modelling paradigm. For example, micro-simulation is based on large amounts of representative data gathered on individuals; it considers characteristics of individuals and is able to reproduce social reality (Martini and Trivellato 1997). Micro-simulation is beneficial in predicting both short-term as well as long-term impacts of policies (Gilbert and Troitzsch 2005). System dynamics models the real- world system as a whole, i.e. at the macro-level (Forrester 1961) by using (a small set of) aggregated data. ABM is valuable for describing and explaining complex social interactions and behaviour, thus contributing to the understanding of a real- world social system and to a better management of different social processes (Gilbert and Troitzsch 2005). The focus is on groups and individuals interacting in a social system and the amount of data needed for developing the simulation model can be considered moderate. The particular approach of simulation modelling determines the complexity of data analysis (i.e. highly complex and intense for micro-simulation, moderate to high complexity for ABM (depending on the number of agents and the aggregation concept), and rather low for system dynamics). Inputs for data analysis are features, descriptions, relationships and specifications of the observed real-world system (Gilbert and Troitzsch 2005). Data analysis can be performed through many different ways ranging from qualitative and quantitative data analysis methods of the social sciences (Mayring 2011) to action research (Greenwood and Levin 2006), design research (Collins et al. 2004) and active stakeholder engagement using, e.g. scenario-building and online citizen participation methods (Wimmer et al. 2012). The second step—conceptual modelling—is not always explicitly implemented as already mentioned. It is a step that is widely used in action research and in design

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 101

research. Newer approaches to policy modelling are based on the value of concep- tualising a policy context and accordingly building conceptual models from the data analysed, as is, e.g. described in (Scherer et al. 2013). Since simulation models are simplifications of reality (Zeigler 1976), conceptual modelling, in practical terms, means to decide on which characteristics of the real-world system are to be included in a simulation model and which ones are not (Gilbert and Troitzsch 2005). In the third step—design of the actual simulation model—the programming of the simulation model is prepared by building a construct of the simulation model (again dependent on the modelling paradigm). The fourth step—programming the simulation model— involves putting hands on writing the code using a particular tool for programming. The fifth step—verification and validation—aims to check if a simulation model behaves as desired (i.e. verification) and whether the model describes the intended real-world system in a satisfactory way and gives reliable outputs (i.e. validation). Validation can be conducted by comparing known behaviour and parameters of the real-world system with the outputs of a simulation model (Maria 1997).

Based on the insights of different paradigms of simulation models, the next sections analyse and compare different models built on these theories and approaches.

6.3 Analysis of Simulation Models of Different Modelling Approaches

In this section, the analysis of five simulation models is presented with respect to their contribution to policy modelling in different public domains. The main goal is to describe and compare different simulation models in order to identify similarities and differences that suggest approaches, tools and techniques that are useful and effective in different policy modelling contexts. For the comparative analysis, eGov- PoliNet developed a framework which serves as a template to ensure comparability across particular aspects of study and to simplify understanding. The framework is divided into three parts: (1) abstract, which gives a brief summary of the model under investigation, and its context; (2) metadata, providing general information such as name of the model, developer, the publication date, background documents used in developing the model, references, tools needed to run the simulation model (for an ordinary user), and a reference to the source of the model; and (3) conceptual aspects of interest in the comparison such as disciplines involved in the model development, underlying theory, particular methods applied to develop the model, technical frame- works and tools used to develop the simulation model7, application domain of the model, constraints of using the model in a particular way, examples of (re)use of the formal model (i.e. giving reference to policy cases and projects where the model

7 A comparative analysis of tools and technical frameworks is provided in (Kamateri et al. 2014).

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Table 6.1 Simulation models examined in the comparative analysis

Based on theory Simulation model

System dynamics VirSim—a model to support pandemic policy-making (cf. Sect. 6.3.1)

Micro-simulation MicroSim—micro-simulation model: modelling the Swedish population (cf. Sect. 6.3.2)

MEL-C—Modelling the early life-course (cf. Sect. 6.3.3)

Agent-based modelling (ABM) OCOPOMO’s Kosice case (cf. Sect. 6.3.4)

SKIN—simulating knowledge dynamics in innovation networks (cf. Sect. 6.3.5)

is/was used), transferability of the simulation model in other domains or disciplinary contexts, and concluding recommendations on the model development and use).8

The simulation models studied in this chapter are based on the modelling paradigms presented in Sect. 6.2, namely system dynamics, micro-simulation, and ABM. Table 6.1 indicates the five simulation models examined in the comparative analysis and presented in the subsections below. It was not the aim of the authors to present an exhaustive list of models but rather a collection that is an informative choice of specific simulation models corresponding to different modelling theories. The models were chosen because the authors had the access to, and knowledge about these simulation models, and they were directly involved in the development of the simulation models analysed in Sect. 3.3, 3.4 and 3.5. The two models described in Sect. 3.1 and 3.2 are interesting for the comparison since they represent the same policy domain and use the same data but represent implementations of different modelling paradigms and methods.

The five simulation models are presented in the following subsections. The de- scriptions follow the structure suggested by the framework proposed by eGovPoliNet, i.e. abstract, metadata and conceptual aspects of interest. While the abstract is provided as a narrative paragraph, the metadata and conceptual aspects are each elab- orated in a tabular form. The subsequent Sect. 6.4 provides a comparative discussion of the different models and their added value to policy modelling.

6.3.1 VirSim—A Model to Support Pandemic Policy-Making

VirSim simulates the spread of pandemic influenza and enables evaluating the effect of different policy measures (Fasth et al. 2010). The main goal is to find the most optimal policies connected to the starting time and the duration of school closure as

8 The framework is published in Annex I to technical report D 4.2 of eGovPoliNet: Maria A. Wimmer and Dragana Majstorovic (Eds.): Synthesis Report of Knowledge Assets, including Visions (D 4.2). eGovPoliNet consortium, 2014, report available under http://www.policy- community.eu/results/public-deliverables/ (last access: 28th July 2014).

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well as the pace and the vaccination coverage. Using the model, it is also possible to estimate public costs due to the absence of staff from work during sick leave. The model considers real population data in Sweden at both national and regional levels (Fasth et al. 2010). In VirSim, the population is divided into three age groups: individuals less than 20 years old, those between 20 and 59 years, and people aged 60 years and more. It is initially assumed that influenza spreads within and between groups with different probabilities. For each age group, a SEIR model (Susceptible, Exposed, Infected, and Recovered) is constructed, which represents the dynamics of spread of the disease. This means that a healthy person starts as a susceptible (S), becomes exposed (E), then infected (I) and, after some time recovered (R) (or dead). VirSim supports scenario analysis (i.e. ‘what-if’ analysis), which means that a user can combine a number of different parameters producing ‘real’ scenarios and examine the impact of policies while asking ‘what could happen if we apply policy XY’ (Fasth et al. 2010) (Table 6.2).

Table 6.2 Analysis of metadata and conceptual data of the simulation model VirSim

Metadata

Name VirSim

Developer Tobias Fasth, Marcus Ihlar, Lisa Brouwers

Publication date 2010

Background documents To segregate the Swedish population into three age groups: (Statistics Sweden (Statistiska centralbyrån, SCB 2009) To estimate frequency of social contacts between and within age groups: (Wallinga 2006) To decide on the duration of a latent period: (Carrat et al. 2008, Fraser et al. 2009)

Reference(s) (Fasth et al. 2010)

Tools needed to run the model Web browser, Internet

Source of the model http://www.anylogic.com/articles/virsim-a-model-to- support-pandemic-policy-making (last access: 28th July 2014) http://people.dsv.su.se/∼maih4743/VirSim/VirSim.html (needs Java Platform SE 7 U activated; last access: 28th July 2014)

Conceptual aspects

Discipline(s) Health science, Information technology/E-Government

Based on theory System dynamics

Developed through method SEIR model (susceptible, exposed, infected, recovered)

Technical framework/tools used for development

AnyLogic

Application domain(s) Policy-making under pandemic influenza

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Table 6.2 (continued)

Metadata

Constraints of using the model in a particular way

The VirSim model does not take into account parameters that are also important for the transmission and spreading of the influenza virus, such as effect of weather and temperature conditions, geographical differences between regions as well as diverse social structures including travelling frequency, gender and hygiene habits. It is not possible to analyse many of the missing parameters since the underlying SEIR model and system dynamics method do not take into account social differences. Hence, the same infection probability was assigned to all people within the three age groups

Examples of use (projects/cases) Policy-making under pandemic influenza in Sweden in 2009. Tested policies were vaccination and school closure (Fasth et al. 2010)

Transferability of formal model in other domains or disciplinary contexts

The initial values for all parameters are provided, for example, starting time of vaccination and the infection risk for different age groups, based on the documents and the data available in the time of the development of the model. However, VirSim allows for the change of all parameter values, including those initially assumed. This assures that the model is re-usable when other data become available. To our knowledge, the model is not transferable to other domains and contexts since it does not allow for a change of the parameters as such, their number, and the underlying differential equations

Concluding remarks on simulation model development and/or use

VirSim runs fast and a user can easily manipulate different parameters. However, the user interface does not include descriptions of the parameters; a user has to guess their meaning and a range of values, based on their names. In some cases, this is difficult, for example, for the parameter ‘vaccination . . . starts after’ with the initial value of 147—it is not clear for what the given initial value stands. Apart from this issue, the model is intuitive and easy to work with. The model is based on the scenario analysis—a user posts a question (‘what could happen if we apply certain policy under certain conditions . . . ’) and gets the answer in a form of suitable plots. This allows policy-making officials to discuss policies further towards finding the most suitable ones. To provide accurate and significant results, VirSim uses real population data in Sweden, at the national and regional level (Fasth et al. 2010). To use the simulation model in similar contexts, the model development should become flexible to support the definition of custom variables and at least some classes of differential equations that are suitable for modelling similar phenomena. Also, based on supported types of processes, the description of possible domains of application to which the model could be transferred would be helpful

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6.3.2 MicroSim—Micro-simulation Model: Modelling the Swedish Population

According to Brouwers et al, MicroSim addresses the problem of the spread of influenza in Sweden. It is an event-driven micro-simulation model with discrete time steps of an hour, developed for exploring the impact of different intervention policies based on vaccination, isolation and social distancing. Each person living in Sweden was modelled in many details, including age, family status, employment details, and important geographical data, such as home and workplace coordinates. Such a modelling strategy provided a fine-grained differentiation between age groups, people’s daily routines and their educational level. This enabled examining the spread of influenza through different social contacts as well as analysing the spatial spread of the disease within the time range of 1 h (Brouwers et al. 2009a, 2009b) (Table 6.3)

Table 6.3 Analysis of metadata and conceptual data of the simulation model MicroSim

Metadata

Name MicroSim—micro-simulation model: modelling the Swedish population

Developer Lisa Brouwers, Martin Camitz, Baki Cakici, Kalle Mäkilä, Paul Saretok

Publication date 2009

Background documents MicroSim uses registry data obtained from Statistics Sweden (Statistiska centralbyrån, SCB)a to generate the simulated population, in particular: National Population Register (2002) to describe age, marital status, children, employment, IDs father and mother; Employment Register (2002) to describe company, workplace, branch, municipality of the workplace for each individual; Employment Register (2002) to obtain family household coordinates, workplace coordinates, and school coordinates

Reference(s) (Brouwers et al. 2009a, 2009b)

Tools needed to run the model

Executable that runs within C++ environment

Source of the model Available on demand from http://arxiv.org/abs/0902.0901 (last access: 28th July 2014)

Conceptual aspects

Discipline(s) Health science, Information technology/E-Government

Based on theory Micro-simulation

Developed through method Population analysis

Framework/tools used for development

C++

Application domain(s) Policy-making under pandemic influenza

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Table 6.3 (continued)

Metadata

Constraints of using the model in a particular way

The model relies on particular data from Sweden and therefore cannot be used for other countries. Also, due to possible migrations and changes in structure of Swedish population, the model has to be validated against new available data

Examples of use (projects/cases)

Policy-making under pandemic influenza in Sweden in Autumn of 2009

Transferability of formal model in other domains or disciplinary contexts

The model can be used as a basis for examining effects of different policies as well as dependencies in the real-world systems and processes based on social and geographical distributions

Concluding remarks on simulation model development and/or use

While micro-simulation models in general use only sample data of the population, MicroSim uses personal, employment and geographic data of the complete Swedish population (approximately 9 million people), which provides an explicit enhancement of the model’s accuracy and reliability. Such detailed representation provides conditions suitable for realistic simulations of influenza outbreaks in Sweden. However, micro-simulation models based on the ontology of the population is not robust towards demographic changes in the social structure of a population

a http://www.scb.se (last access: 28th July 2014).

6.3.3 MEL-C—Modelling the Early Life-Course

MEL-C is a Knowledge-based Inquiry tool With Intervention modelling (KIWI) developed on the early life-course as a decision support aid to policy analysts and advisors in New Zealand. Underlying the tool is a dynamic discrete-time micro- simulation model using a social determinants framework to predict child outcomes, for which the key parameters have been estimated from existing longitudinal cohort studies in New Zealand, initially the Christchurch Health and Development Study. These parameters were applied to a starting sample synthesised from a combination of data from the national census and from the longitudinal studies. Thus a set of synthetic representative early life histories was created that reproduced patterns found in the original data. The tool can be interrogated with realistic policy scenarios by changing baseline features or parameters in the model and observing the effect on outcomes, for example, ‘what if’ the initial social determinants were different and what would be their impact. The model content, tool interface and inquiry system have been developed in cooperation with central government policy advisors drawn from the agencies with a special interest in the early life-course (Mannion et al. 2012) (Table 6.4).

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Table 6.4 Analysis of metadata and conceptual data of the simulation model MEL-C

Metadata

Name MEL-C—modelling the early life-course

Developer COMPASSa

Publication date 2014

Background documents (Fergusson et al. 1989; Solar and Irwin 2010)

Reference(s) (Mannion et al. 2012; Milne, et al. 2014; McLay et al. 2014; Lay-Yee et al. 2014)

Tools needed to run the model

The MEL-C executable, which includes JAMSIM (consisting of ASCAPE, JAVA and R) and simulation code run with R and tailored functions from the R Simario package developed by COMPASS

Source of the model See http://code.google.com/p/jamsim/ for JASMIM (last access: 28th July 2014) See http://code.google.com/p/simario/ for R SIMARIO package (last access: 28th July 2014) MEL-C simulation model accessible on request from the COMPASS research centre

Conceptual aspects

Discipline(s) Social and health sciences (sociology, psychology, epidemiology), statistics, computer science, policy sciences

Based on theory Child development, Social determinants of health, Micro-simulation

Developed through method Regression analysis R and JAVA programming Micro-simulation modelling End-user engagement Cluster matching and data imputation

Framework/tools used for development

MEL-C as a single executable software application in which users can interrogate the model from the ‘front end’ and not need to deal with the ‘behind-the-scenes’ computer programs and statistical models. The tools used are: Eclipse, StatEt, Git control, Ivy. ASCAPEb and Jamsimc (JAVA) for front end Simario (R)d

for execution of models

Application domain(s) Early life-course, Health, Justice, Education, Social Policy, Policy scenarios, User interface

Constraints of using the model in a particular way

Limited by variables available in the source data sets. Relationships between variables are un-directional with no feedback. Scenarios tested involve changing the distribution of variables not the effects (e.g. the effect of X on Y). Potential geographical and period limits of data sources. Discrete time only

Examples of use (projects/cases)

Illustrative application to social determinants of health and end-user engagement

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Table 6.4 (continued)

Metadata

Transferability of formal model in other domains or disciplinary contexts

The model is of generic applicability in early life-course analysis. Subject to data availability and funding, it is possible to extend the model to later periods in the life-course and other domains. There may be other dynamic socio-demographic processes where this approach can be applied

Concluding remarks on simulation model development and/or use

The model is restricted to a notional ‘evidence-based’/ science-informed approach to policy development. The model is conceptually predicated on the primacy of social determinants. The role of stakeholders is limited to the rather formal role of a policy advisor or analyst seeking to weigh different options within a prescribed range. The model is able to reproduce actualities and to produce plausible substantive results in scenario testing. The model has the great potential of combining a realistic data framework with estimates derived from meta-analyses, systematic reviews and other research sources. The model is a simplification of reality but is nevertheless a powerful source of information that can be interrogated by end-users and can be considered alongside other evidence for policy

a http://www.arts.auckland.ac.nz/en/about/ourresearch-1/research-centres-and-archives/centre-of- methods-and-policy-application-in-the-social-sciences-compass/about-compass.html (last access: 28th July 2014). b http://ascape.sourceforge.net/ (last access: 28th July 2014). c http://code.google.com/p/jamsim/ (last access: 28th July 2014). d http://code.google.com/p/simario/ (last access: 28th July 2014).

6.3.4 Ocopomo’s Kosice Case

Energy policy is increasingly receiving attention, especially in exploring renewable energy sources, energy saving and to raise awareness about energy policy among citizens. The aim of the simulation model developed for the Kosice self-governing region was to capture the behaviour of key stakeholders and decision-makers towards a new energy policy moving to better house insulation and to using renewable en- ergy sources. The renewable energy policy case combined the scenario-method with ABM to explore social behaviour and interrelations between stakeholders, economic conditions of the region and realistic social dynamics. Based on the stakeholder in- puts gathered through scenarios developed via an online e-participation platform, a conceptual model was developed which informed the agent-based simulation model. The simulation model helped to test the effectiveness of various policy options (e.g. to support better insulation of houses, to invest in renewable energy sources such as gas, coal, and biomass, etc.) (Wimmer et al. 2012). A particularity of the model is the possibility to trace evidence data provided in stakeholders’ scenarios or background documents via a conceptual model to inform the simulation model. Accordingly it is possible for stakeholders to navigate from the simulation outputs back to the evidence input (Lotzmann and Wimmer 2013) (Table 6.5).

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 109

Table 6.5 Analysis of metadata and conceptual data of the simulation model developed in OCOPOMO’s Kosice case

Metadata

Name OCOPOMO’s Kosice case

Developer Partners of the OCOPOMO consortium, involving University of Koblenz-Landau, Scott Moss Associates, Technical University of Kosice, Intersoft and Kosice Self-Governing Region (KSR)

Publication date 2013

Background documents Based on the OCOPOMO approach (Wimmer et al. 2012) a number of background documents was used, such as: Analysis of structural funds (2007–2013) and Projects Approved in 2009 in KSR Energy policy of KSR (2007) Strategy of Renewable Energy Sources Utilization in KSR (2006) Demographic composition of the households (1996) Annual report 2009, Regulatory Office for Network Industries Regional Statistics Database (2010) Interviews with experts from KSR and local energy providers

Reference(s) (Wimmer et al. 2012; Wimmer 2011; Lotzmann and Meyer 2011; Butka et al. 2011; Lotzmann and Wimmer 2013)

Tools needed to run the model

Collaborative e-participation platform for scenario generation and stakeholder engagement using the ALFRESCOa Web content management system (wiki for scenario generation, discussion, polling) DRAMS (Lotzmann and Meyer 2011)—the Declarative Rule-based Agent Modelling system, Consistent Conceptual Modelling Tool (CCD) (Scherer et al. 2013)

Source of the model http://www.ocopomo.eu/results/software-and-models/software- and-model-artefacts/eclipse-based-tools-and-simulation-models (last access: 28th July 2014)

Conceptual aspects

Discipline(s) Social Science, Information Systems/E-Government

Based on theory Agent-based modelling, Model-driven Architecture, Design Research, Stakeholder theory

Developed through method Stakeholder engagement through online deliberation, qualitative analysis methods such as workshops and interviews, conceptual modelling using Consistent Conceptual Modelling (CCD), ontology development

Framework/tools used for development

Eclipse Modelling Framework (EMF)b, Eclipse Graphical Modelling Framework (GMF)c, Graphical Editing Framework (GEF)d , Collaborative participation platform for scenario generation and stakeholder interaction ALFRESCO (wiki, discussion, voting), DRAMS—Declarative Rule-based Agent Modelling System, Consistent Conceptual Modelling (CCD) Tool

Application domain(s) The simulation model is used for policy development in the field of energy with the focus on: • Energy efficiency • Decrease of energy consumption (heating) and improved

insulation as well as wise spending of energy • Utilisation of renewable energy sources

Constraints of using the model in a particular way

Agent-based modelling is particularly applicable for examining social behaviour but cannot be the only source for policy-making.

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110 D. Majstorovic et al.

Table 6.5 (continued)

Metadata

Examples of use (projects/cases)

Renewable energy and heating in Kosice Self-Governing Region (KSG), Slovakia Housing policy in London, UK Knowledge transfer in Campania Region, Italy Parts of the OCOPOMO simulation environment are also used in the GLODERS projecte

Transferability of formal model in other domains or disciplinary contexts

Natural conditions of the Kosice region, such as terrain, location of and distance from the renewable energy sources, concentration of housing, available infrastructure, influence the output of the model. Therefore, transferability is restricted, and the use of the model demands for updating the local and natural conditions of a region

Concluding remarks on simulation model development and/or use

The simulation model is evidence-based and built around the descriptions, expectations, interactions and beliefs of stakeholders in the policy-making process. The modelling process involved stakeholders who expressed their views and concerns on a policy via collaborative scenarios and e-participation tools. They acted as partners and researchers in the modelling process. A key feature of the OCOPOMO policy modelling approach is to engage stakeholders and to ensure traceability from evidence-based input of stakeholders in narrative text to simulation outputs generated through agent-based simulation. A lesson from using a declarative rule programming paradigm as implemented in DRAMS vs. the imperative paradigm in most ABM tools is that the declarative way is more difficult to program and less intuitive for programmers

a http://www.alfresco.com/?pi_ad_id=39517088287 (last access: 28th July 2014). b https://www.eclipse.org/modeling/emf/ (last access: 28th July 2014). c http://www.eclipse.org/modeling/gmp/ (last access: 28th July 2014). d http://www.eclipse.org/gef/ (last access: 28th July 2014). e http://www.gloders.eu/ (last access: 28th July 2014).

6.3.5 SKIN—Simulating Knowledge Dynamics in Innovation Networks

Simulating Knowledge Dynamics in Innovation Networks (SKIN) is an agent-based model used to understand innovation policy initiatives, which contain heterogeneous agents, who act and interact in a large-scale complex and changing social environ- ment. The agents represent innovative actors who try to sell their innovations to other agents and end users but who also have to buy raw materials or more sophisticated inputs from other agents (or material suppliers) to produce their outputs. This basic model of a market is extended with a representation of the knowledge dynamics in and between the agents. Each agent tries to improve its innovation performance and its sales by improving its knowledge base through adaptation to user needs, incremental or radical learning, and co-operation and networking with other agents (Ahrweiler et al. 2004) (Table 6.6)

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 111

Table 6.6 Analysis of metadata and conceptual data of the simulation model SKIN

Metadata

Name Simulating knowledge dynamics in innovation networks (SKIN)

Developer Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

Publication date 2001, with continuous updates since

Background documents Literature from Evolutionary Economics, Economic Sociology, and Science and Technology Studies (see body of literature cited in the references as listed next)

Reference(s) (Gilbert et al. 2001; Ahrweiler et al. 2004; Gilbert et al. 2007; Pyka et al. 2007; Scholz et al. 2010; Ahrweiler et al. 2011a, 2011b; Gilbert et al. 2014)

Tools needed to run the model

NetLogo (versions available in alternative languages such as Java) http://ccl.northwestern.edu/netlogo/ (last access: 28th July 2014)

Source of the model http://cress.soc.surrey.ac.uk/SKIN/ (last access: 28th July 2014)

Conceptual aspects

Discipline(s) Economics, sociology, science and technology studies, policy research, business studies

Based on theory Evolutionary Economics, Organisational Theory, Organisational Learning, Field Theory, Complex Systems Theory, Agent-based modelling

Developed through method Theory formation, empirical research, implementing theoretical concepts and empirical insights, consistent conceptual modelling, agent-based modelling

Framework/tools used for development

NetLogo

Application domain(s) Knowledge-intensive industries, EU framework programmes, national innovation policies, role of specific actors in innovation networks

Constraints of using the model in a particular way

SKIN is about knowledge and agent networks embedded in a dynamic environment. Not applicable if domain has nothing to do with it.

Examples of use (projects/cases)

EU projects: Simulating self-organizing innovation networks (SEIN)a , 1998–2001 Network models, governance, and R&D collaboration networks (NEMO)b, 2006–2009 Managing emerging technologies for economic impact (ManETEI)c, 2010–2014 Using network analysis to monitor and track effects resulting from Changes in policy intervention and instruments, (SMART 2010/0025) 2010–2011 Governance of responsible research and innovation (GREAT)d , 2013–2016

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112 D. Majstorovic et al.

Table 6.6 (continued)

Metadata

Transferability of formal model in other domains or disciplinary contexts

SKIN is a multi-disciplinary initiative (see above Discipline(s)) and is therefore used in various disciplinary contexts. However, as it is about knowledge and agent networks embedded in a dynamic environment, it is not applicable if the policy domain is not working with knowledge, innovation and agent networks

Concluding remarks on simulation model development and/or use

The advantages of using SKIN for policy modelling include: The experiments can be run many times to find statistically average behaviour. Experiments can be used to give an indication of the likely effects of a wide variety of policy measures Empirical ‘Un-observables’ such as the amount of knowledge generated, and the number of proposals started but abandoned before submission, can be measured by instrumenting the simulation The problems include determining: What are the ultimate policy objectives for the support of Research and Development? When were the policies being formulated and by whom? How can the research be presented so that it is interesting and comprehensible to a policy-making audience?

a http://ec.europa.eu/research/social-sciences/projects/097_en.html. b http://cress.soc.surrey.ac.uk/SKIN/research/projects/nemo. c http://lubswww.leeds.ac.uk/manetei/home/. d http://www.great-project.eu/ (last access 28th July 2014).

6.4 Comparison of Simulation Models and Discussion of Added Value and Limitations of Particular Simulation Models

In Table 6.7, the key elements of the five simulation models introduced in Sect. 3 are compared using the comparison framework above, and summed up on the following aspects: publication date of the model, key aspects of the model, tools needed to run the model, discipline(s), simulation paradigm on which the model is based, method through which the model is developed, framework and/or tools used for the devel- opment of the model, application domain, constraints of using the model, examples of use, model’s transferability to other domains, and limitations and suggestions.

As elaborated in Sect. 3.1, the VirSim simulation model examines the effect of different policies to the problem of influenza spread by using the SEIR model for modelling the population on a global (macro-) level. Over time, a person changes between the categories and this flow is described with a set of differential equations (Fasth et al. 2010). The model applies a system dynamics paradigm. The other exam- ple of the same policy domain, the micro-simulation model MicroSim as introduced in Sect. 3.2, applies a different modelling approach to the same problem, where each person is modelled in many details. The spread of influenza is therefore determined by many ‘micro’-level factors.

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 113

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[email protected]

114 D. Majstorovic et al.

Ta bl

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[email protected]

6 Features and Added Value of Simulation Models Using Different Modelling. . . 115

Ta bl

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116 D. Majstorovic et al.

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 117

The main advantage of system dynamics models is that they are fast to run and technologically not demanding while providing useful information about the real- world processes and insights into possible impacts of different macro-level policies. However, these models face a number of restrictions. For example, VirSim initially assumes infection probabilities where elderly people (age group 60 and more) have considerably fewer chances of being infected with influenza compared to the other two age groups (Fasth et al. 2010). However, the SEIR model that was applied cannot predict this and cannot explain why this occurs. The authors of VirSim used this result from the micro-simulation model MicroSim and assumed this phenomenon happens because of less social contacts of elderly people or some prior immunity. VirSim cannot explain this phenomenon because system dynamics does not include modelling of various social interactions and other similar dependencies between actors since all variables are averaged over particular groups or the population in general - in the case of VirSim within the members of a particular age group. Apart from the categories of people based on their age, VirSim cannot identify fine-grain groups that have higher probability to be infected. For example, a student has more chances to be exposed and therefore infected than a researcher working in the same university but more in the closed environment of an office while students usually have more frequent social interactions among their groups and communities. It is important to identify closed environments that have high risk of spreading influenza, for example boarding and nursing homes. From the policy modelling point of view, it is important to identify high-risk groups to start the vaccination from there. One could define refined categories of actors by defining more variables, but in general, it would not be possible to represent relations between subcategories, such as taxonomies or ontologies needed to represent social contacts or interactions among actors, due to the lack of representation apparatus in system dynamics models.

As Gilbert and Troitzsch argue, due to social complexity and non-linearity, it is difficult to describe processes and systems analytically. To be able to examine interactions between simulation units, other modelling techniques such as ABM or micro-simulation models need to be applied for exploring the social heterogeneity and structures (Gilbert and Troitzsch 2005).

Micro-simulation models, usually based on a weighted sum of a representative sample of the population, consider characteristics of individuals and are able to re- produce social reality (Martini and Trivellato 1997). They are beneficial in predicting both, short-term as well as long-term impact of policies (Gilbert and Troitzsch 2005). However, micro-simulation models are costly to build and complex, especially at the level of data analysis requirements. In the case of MicroSim, the Swedish population of approximately nine million people was modelled in many details (Brouwers et al. 2009). In addition, in ‘simple’cases, especially in demographics, a micro-simulation model produces similar results as a system dynamics-based model (Gilbert and Troitzsch, 2005). This proved true in the case of MicroSim and VirSim: The lat- ter confirmed the results of the former, although with a greater difference between vaccination and non-vaccination results (The National Board of Health and Wel- fare 2011). According to Spielauer, micro-simulation is best to use when population heterogeneity matters; when there are too many possible combinations to split the

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118 D. Majstorovic et al.

population into a manageable number of groups; in situations when the micro level explains complex macro-behaviours, or when individual history is important for the model’s outcomes (Spielauer 2011).

Although agent-based models lack clear predictive possibilities, they are consid- ered a highly valuable tool for describing and explaining complex social interactions and behaviours, contributing to the understanding of real-world social systems and to a better management of different social processes. Schindler argues that agent-based simulations are capable of representing real-world systems, where small changes in parameter values induce big changes in the model’s outputs. This property shifts attention from the importance of predictions of the system’s future behaviour to the management of critical (social) processes responsible for the changes. However, agent-based simulations alone are not sufficient to model reality. Another possi- ble problem is a high degree of freedom in modelling agents, which amplifies the importance of a proper validation of a simulation model (Schindler 2013).

While agent-based and micro-simulation models would be able to show that an elderly person has less infection probability, it is questionable whether they would be able to answer why an elderly person is less infected by influenza. Knowing ‘why’ can help in building a successful strategy for protection against the disease. It might happen that hidden variables and parameters influence this age group. For this reason, in order to model correct probabilities for different age groups, several authors suggest that uncertainty models, such as (dynamic) Bayesian models or Markov chains could be used. In addition, if the past should be also considered (for example, a person has less chances to be infected now because he/she was infected in the recent past), then we have to use more complex probability models, such as the Dempster–Shafer model (Ronald and Halpern 1991, Jameson 1996). Gilbert and Troitzsch argue that statistical models can also be used to predict values of some dependent variables. However, statistical models assume linear relationships between parameters, which becomes a restrictive assumption in the case of (complex) social systems (Gilbert and Troitzsch 2005).

Comparing the three different paradigms of social and policy modelling explored in this chapter, the three approaches can be examined according to the level of gran- ularity they are focussing on, the complexity of the models, the demand for the amount of data needed to generate a valuable simulation model and whether social behaviour is modelled. Table 6.8 provides this comparison, which is adapted from (Gilbert and Troitzsch 2005). Micro-simulation models represent particular ontolo- gies of the population or its representative subset based on individuals and are most demanding regarding data needed for developing a model. Agent-based models are less data demanding, less complex and well suited for representing groups of actors (which can represent individuals, groups as well as a system as a whole) and their social behaviour. ABM is the only one of the three paradigms studied which models social behaviour. However, social behaviour cannot be the only source for policy- making (Gilbert and Troitzsch 2005) Macro-models, in this chapter represented by system dynamics, are the least demanding—they model a situation at a global level and require the least data. Nevertheless, they are better for the analysis of short-term policy impacts than for longer-term perspectives (Astolfi et al. 2012).

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 119

Table 6.8 Comparison of simulation modelling theories along level of granularity, the complexity of the models, the amount of data needed to generate simulation models, and the modelling of social behaviour of agents

Simulation paradigm

Granularity Complexity Data needed Behavioural

System dynamics Macro-focusing on the system as a whole

Low Aggregated data No

Micro-simulation Micro-focusing on individuals

High High amount at individual level

No

Agent-based modelling

Micro-macro— focusing on interaction of agents (which can be individuals as well as a system)

Medium-high Low to moderate (depending on the number of agents and the policy context)

Yes

The analysis of three different modelling paradigms with the comparison of five different simulation models has shown that each of the modelling approaches has strengths and weaknesses that constrain their usage in policy-making. For example, micro-simulation can be used for representing social structures whileABM examines interactions between the agents. Astolfi et al argue that none of the theories alone is able to address complex policy interactions (Astolfi et al. 2012). In consequence, a necessary step in the development of simulation modelling is to build and explore ways of maintaining complex simulation models consisting of a few sub-models built on different modelling theories, which communicate with each other by set- ting up and propagating particular parameters after each reasoning iteration (Astolfi et al. 2012). These hybrid models can be considered as modelling platforms or com- plex systems consisting of sub-models. Yet, it is necessary to study methodologies and possibilities of combining different modelling paradigms in order to provide reliable simulation platforms. Current research indicates this trend, as an example of micro-macro combination in a Chronic Disease Prevention Model developed in Australia shows (Brown et al. 2009). However, more research is needed to better understand the implications of combined modelling paradigms, to develop innova- tive simulation platforms that support easy adjustment and development of different models based on different modelling paradigms and to bring evangelists of particular modelling paradigms closer to each other to support mutual understanding and the exploration of the added value and benefits of particular simulation models. Further recommendations and indications of research needs include, but are not exhaustively listed:

• Providing guidelines for how to best choose and arrange a collection of smaller (sub) models each describing certain aspects of a given domain of modelling;

• Finding the junction points of those models of distinct modelling paradigms with each other by defining input and output parameters for each of the sub-models;

• Developing meta-models that reflect the combinatory use of distinct modelling approaches;

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120 D. Majstorovic et al.

• Determining the workflow of a simulation process by means of, e.g. a sequence and timing of exchanging the input and output parameters between sub-models in a combined hybrid meta-model;

• Exploring more extensive engagement of stakeholders in the policy development9; • Developing more comprehensive simulation platforms that enable the combina-

tion of different simulation paradigms in an easy way.

6.5 Conclusions

In this chapter, we have examined and compared five different simulation mod- els, which were built on three different modelling paradigms: system dynamics, micro-simulation, andABM. The chapter first provided an overview of the main char- acteristics of each of the modelling paradigms and then described the five simulation models by outlining them according to a framework elaborated by eGovPoliNet for comparative analysis of knowledge assets. The simulation models are each suitable for representing different aspects of socio-political and/or socio-economic phenom- ena, such as demographic processes (education, social contacts, spread of diseases, etc.), innovation processes or natural resource consumptions (e.g. energy consump- tion). The comparison has revealed the major differences as well as added value and limitations of the different approaches and simulation models. Some lessons from the comparative analysis are that the main strengths of using simulation models in policy- making are the possibilities of exploring and creating understanding of real-world systems and relationships, of experimenting with new situations and of forecasting outputs of alternative policy options or situations based on the given values of pa- rameters. Another key added value of simulation models in policy-making is that simulation models enable the exploration of social processes to evaluate potential impacts of alternative policy options on real-world situations and thus to identify the most suitable policy option.

Current paradigms of policy modelling using simulation models are however constrained by their particular focus.Yet, our real-world systems and social processes are complex and require the consideration of parameters at different levels: macro- level, micro-level as well as social behaviour and interconnections between actors. Accordingly, applying one singular approach to modelling a real-world problem is constrained by the particular modelling approach it focuses on: A simulation model of system dynamics may therefore lack precision and social interactions because the missing factors are not accounted for. While the demand for meeting the appropriate level of details included in a model’s description, being not too complex and also not too simple, determines the success of a simulation model, there is a rising need for integrating and combining different modelling paradigms to accommodate the diverse aspects to be considered in complex social world policy contexts. Unifying

9 A more detailed discussion of stakeholder engagement in policy making is given in (Helbig et al. 2014).

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6 Features and Added Value of Simulation Models Using Different Modelling. . . 121

different modelling theories under an umbrella of comprehensive policy modelling platforms is a research need identified in this chapter. Such research should put forward a meta-model of how individual simulation paradigms can be combined, and suggestions of ‘clever’junctions of individual smaller (and self-contained) simulation models dedicated to individual aspects to be modelled.

While this chapter selected three widely used simulation paradigms for the study, it does not claim to be exhaustive nor comprehensive. Further research is needed to ex- tend the study to involve other important modelling approaches such as theory-based macro-economic forecasting for instance Dynamic Stochastic General Equilibrium (DSGE) modelling. DSGE is exemplified by the Global Economy Model (GEM) which provides support in policy analysis to central banks and the International Monetary Fund (IMF) (Bayoumi 2004). This will further add to understanding the scope and limitations of different modelling paradigms, as for example Farmer and Foley argue, too, that instead of DSGE models, agent-based models should be used to model the world economy (Farmer and Foley 2009). Thus, the authors of this chapter recognise the need to continue comparative analysis as carried out in this contribution and to expand the research to incorporate further modelling paradigms as well as other public policy domains. Insights gained will help build up better hybrid models of social simulation paradigms that are better able to cope with the complexity of our social and dynamic world systems and that are more reliable as they are covering the various social, policy and economic aspects at various levels of abstraction and giving consideration in a more comprehensive way. Accordingly, better social simulation modelling platforms will emerge.

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Chapter 7 A Comparative Analysis of Tools and Technologies for Policy Making

Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee and David Price

Abstract Latest advancements in information and communication technologies of- fer great opportunities for modernising policy making, i.e. increasing its efficiency, bringing it closer to all relevant actors, and enhancing its transparency and acceptance levels. In this context, this chapter aims to present, analyse, and discuss emerging information and communication technologies (ICT) tools and technologies present- ing the potential to enhance policy making. The methodological approach includes the searching and identification of relevant tools and technologies, their system- atic analysis and categorisation, and finally a discussion of potential usage and recommendations for enhancing policy making.

E. Kamateri (�) · E. Panopoulou · E. Tambouris · K. Tarabanis Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece e-mail: [email protected]

E. Panopoulou e-mail: [email protected]

E. Tambouris · K. Tarabanis University of Macedonia, Thessaloniki, Greece e-mail: [email protected], [email protected]

K. Tarabanis e-mail: [email protected], [email protected]

A. Ojo · D. Lee INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland e-mail: [email protected]

D. Lee e-mail: [email protected]

D. Price Thoughtgraph Ltd, Somerset, UK e-mail: [email protected] © Springer International Publishing Switzerland 2015 125 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_7

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126 E. Kamateri et al.

7.1 Introduction

Policy making may be defined as “the process by which governments translate their political vision into programmes and actions to deliver ‘outcomes’ desired changes in the real world” (UK Government 1999). Policy making encompasses any activity relevant to discussing political issues, identifying areas of improvement or solutions, creating and implementing laws and regulations, monitoring and evaluating current policies, etc.

Policy making is a multidisciplinary scientific field referring mainly to politi- cal science, but it may also refer to social, economics, statistics, information, and computer sciences. These diverse scientific fields are essential in order to perform policy making in a more effective and informed manner. Information and communi- cation technologies (ICTs), specifically, have supported decision-making processes for many years. However, the current ICT advancements and good practices of- fer even greater opportunities for modernising policy making, i.e. increasing its efficiency, bringing it closer to all relevant actors and increasing participation, facil- itating its internal processes (e.g. decision making), and enhancing its transparency and acceptance levels.

In this context, this chapter aims to present, analyse, and discuss emerging ICT tools and technologies presenting the potential to enhance policy making. Our ap- proach includes searching and identification of relevant tools and technologies, their systematic analysis and categorisation, and finally a discussion of potential usage and recommendations for enhancing policy making. The chapter is structured in the following way: Sect. 7.2 describes our methodological approach, Sect. 7.3 provides the comparative analysis, and Sect. 7.4 discusses the findings and concludes the chapter.

Before proceeding to the rest of the chapter, we should provide further clarifica- tions with regard to its scope. First, for work presented in this chapter, policy making is considered as a broad and continuous process that commences from the need to create a policy and ends when a policy is abandoned or replaced. In this context, the policy-making process is usually described with a circular-staged model called “the policy cycle”. There are differences in the number, names, and boundaries of the stages adopted in each proposed policy cycle (e.g. Jann and Wegrich 2006; Northern Ireland Government 2013); however, every policy cycle includes an initiation stage, a drafting stage, an implementation stage, and an evaluation stage. The scope of our work refers to all these stages of the policy cycle.

Second, we consider all stakeholders relevant to policy making within the scope of work presented in this chapter. Obviously, the main actor involved in policy making is the government with its different roles, bodies, and institutions. However, noninstitutional actors are also involved such as political parties, political consultants and lobbyists, the media, nongovernmental organisations, civil organisations, and other interested parties depending also on the policy topic at hand. Last but not least, individual citizens are also actors of policy making; as the final policy recipients and beneficiaries, they should actively participate in policy making. Hence, in this

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chapter, we do not consider policy making as a close, internal government process, but rather as an open, deliberative process relevant to the whole society.

7.2 Methodology

In order to analyse the existing ICT tools and technologies that can be used to enhance the policy-making process, we adopted a simple methodology consisting of four main steps.

Before introducing the adopted methodology, we provide a short description with regard to the difference between ICT tools and technologies. ICT tools normally in- clude software applications, web-based environments, and devices that facilitate the way we work, communicate, and solve problems. These are developed by individual software developers, big software providers, researchers, and scientists (Phang and Kankanhalli 2008). Technology, on the other hand, refers to knowledge and know- how, skills, processes, tools and/or practices.1 Therefore, technology not only refers to tools but also the way we employ them to build new things. In the current survey, we organise the findings of our literature analysis based on tool categories.

Step 1: Identification During this step, we surveyed the current state of the art to identify ICT tools and technologies that have been (or have a clear potential to be) used to reinforce the policy-making process. These tools have been collected mainly from project deliverables, posts, electronic articles, conference papers, scientific journals, and own contacts and expertise.

In particular, we searched for tools and technologies that have been highlighted, used, or created by existing research and coordination projects in the area of e-government and policy modelling, i.e. CROSSOVER2, e-Policy3, FuturICT4, OCOPOMO5, COCKPIT6 and UbiPol7, OurSpace8, PuzzledbyPolicy9, etc. This investigation resulted in a collection of more than 30 ICT tools and technolo- gies mainly coming from project deliverables, posts, electronic articles, conference papers, scientific journals, and own contacts and expertise.

Thereafter, we expanded our research on the web to include additional tools that were not previously identified. To this end, we tried multiple searches in the major research databases of computer science, e.g. Association for Computing Machin- ery (ACM) Digital Library and Google Scholar using a combination of different

1 http://en.wikipedia.org/wiki/Technology. 2 http://crossover-project.eu. 3 http://www.epolicy-project.eu/node. 4 http://www.futurict.eu. 5 http://www.ocopomo.eu. 6 http://www.cockpit-project.eu. 7 http://www.ubipol.eu/. 8 http://www.ep-ourspace.eu/. 9 http://www.puzzledbypolicy.eu/.

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keywords such as tools, technologies, policy modelling, online participation, en- gagement, government, policy making, decision making, policy formulation, etc. The references of the selected papers were checked and additional papers were found. Some of the journals that have been reviewed include Government Infor- mation Quarterly, International Journal of Electronic Government Research. In addition, we surveyed similar initiatives that summarise tools or/and methods, i.e. the Participation Compass10 launched by Involve11 (not-for-profit organisation in public participation), the ParticipateDB12 by Intellitics13, and the ReformCompass by Bertelsmann Stiftung14 (providers of digital engagement solutions). The final result of this exercise was a list of 75 tools and technologies.

Step 2: Categorisation Analysing the identified tools and technologies, it was ev- ident that most of them fall under a number of categories. We defined, therefore, 11 categories of tools and technologies for policy making. Each category has a spe- cific application focus, e.g. opinion mining, serious games, etc., and may be further divided into one or more subcategories.

We then organised tools and technologies’ analysis according to the defined cate- gories. There are few cases, however, where the same tool could be classified under more than one category, i.e. in the case of visualisation and argumentation tools and in the case of serious games and simulation tools. In the first case, argumentation tools represent and structure arguments and debates, and usually exploit visual means in order to clearly represent the arguments. However, the main focus remains the representation of arguments. On the other hand, the visualisation tools present, in a graphical form, any type of input data. Thus, it was selected for the sake of simplicity to analyse each tool in one category according to its most prominent feature. Similar difficulties in categorisation have also arisen in the case of simulation tools and seri- ous games. Serious games are created for educational and entertainment purposes, or for helping citizens to further understand some processes by playing the role of a key stakeholder. On the other hand, simulation tools are usually created on a more serious context (e.g. within a research project, taking into account accurate real-world data) in order to help real policy makers or governments to simulate long-term impacts of their actions. Therefore, the categorisation of tools in these two categories was made based on the context and the goal of the tool.

Step 3: Comparative Analysis A comparative analysis of identified ICT tools and technologies per category was then performed. Initially, we analysed tools’ function- ality to identify core capabilities per category. Then, we examined the key features for each tool. The outcome of this analysis is a comparative table for each category

10 http://participationcompass.org/. 11 http://www.involve.org.uk. 12 http://participatedb.com/. 13 http://www.intellitics.com/. 14 http://www.reformkompass.de.

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that shows, at a glance, an overview of different features found in each tool of the category.

Step 4: Conceptualisation During this step, we performed an overall discussion of the presented tools and technologies and their potential for enhancing policy making. To this end, we examined three main aspects for policy making—the type of facilitated activities, the type of targeted stakeholders, and the stages of the policy cycle. Finally, we drafted overall recommendations and conclusions.

7.3 Tools and Technologies for Policy Making

Based on the literature survey, we identified 11 main categories of ICT tools and technologies that can be used for policy making purposes as follows:

• Visualisation tools help users better understand data and provide a more meaningful view in context, especially by presenting data in a graphical form.

• Argumentation tools visualise the structure of complex argumentations and debates as a graphical network.

• eParticipation tools support the active engagement of citizens in social and political processes including, e.g. voting advice applications and deliberation tools.

• Opinion mining tools help analyse and make sense of thousands of public comments written in different application contexts.

• Simulation tools represent a real-world system or phenomenon and help users understand the system and the effects of potential actions in order to make better decisions.

• Serious games train users through simulation and virtual environments. • Tools specifically developed for policy makers have been recently developed to

facilitate the design and delivery of policies. • Persuasive tools aim to change users’ attitudes or behaviours. • Social network analysis (SNA) tools analyse social connections and identify

patterns that can be used to predict users’ behaviour. • Big data analytics tools support the entire big data exploitation process from

discovering and preparing data sources, to integration, visualisation, analysis, and prediction.

• Semantics and linked data tools enable large amounts of data to become easily published, linked to other external datasets, and analysed.

We present an analysis of each category of tools and technologies in the rest of this section.15

15 All tools mentioned in this section are summarized in the end of the chapter along with their links.

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7.3.1 Visualisation Tools

Visualisation tools enable large amounts of “raw” data to become visually represented in an interpretable form. Moreover, they provide appropriate means to uncover pat- terns, relationships, and observations that would not be apparent from looking at it in a nonvisual format. Therefore, users can explore, analyse, and make sense of data that, otherwise, may be of limited value (Osimo and Mureddu 2012). Today, there are many data visualisation tools, desktop- or web-based, free or proprietary, that can be used to visualise and analyse raw data provided by the user. Examples include Google Charts, Visokio Omniscope, R, and Visualize Free. Besides visual presentation and exploration of raw data, they provide additional features such as data annotation (e.g. Visokio Omniscope), data handling, and other statistical computations on raw data (e.g. R).

Over recent years, geovisualisation (shortened form of the term geographic vi- sualisation) has gained considerable momentum within the fields of geographic information systems (GIS), cartography, and spatial statistics. Some consider it to be a branch of data visualisation (Chang 2010). However, geovisualisation inte- grates different approaches including data visualisation, such as cartography, GIS, image analysis, exploratory data analysis, and dynamic animations, to provide visual exploration, analysis, synthesis, and presentation of geospatial data (MacEachren and Kraak 2001). Geovisualisation tools have been widely used to visualise societal statistics in combination with geographic data.

Several visualisation and geovisualisation tools have been developed to visualise and analyse demographic and social statistics in several countries across the world. Most tools are used for data coming from the USA. However, many efforts have been made, lately, to visualise statistics coming from all over the world (e.g. Google Public Data Explorer and World Bank eAtlas). The most important source of information for these tools is governmental reports which are made available by each state. Most tools support data transparency, mainly for downloading data and figures, while uploading of users’ data is available only in few cases. Visualisation tools are organised into static and interactive based on a categorisation proposed for web- mapping tools (Kraak and Brown 2001). A static tool contains a figure or a map displayed as a static image (Mitchell 2005), while interactive tools allow users to access a set of functions to have some interaction with the tool or the map, such as zooming in and out (Mitchell 2005). Table 7.1 summarises well-known visualisation and geovisualisation tools and compares their main characteristics. In particular, the table provides information on: (a) the number and subject of indicators, e.g. if they deal with demographic, health, environmental, or other social issues, (b) the coverage, namely, the countries supported, (c) the period for which statistics are available, (d) data transparency, and (e) whether it is a static or interactive tool.

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Table 7.1 Visualisation and geovisualisation tools for analyzing regional statistics

Indicators and Topic

Coverage (countries)

Period Data transparency

Static/ Interactive

Gapminder > 400 Demographics, social, economic, en- vironmental, health

> 200 Over the past 200 years

Download and upload

Interactive

Worldmapper ∼ 696 maps Demographics

All N/A Download (No custom maps)

Datasets, static

Dynamic Choropleth Maps

Multiple social, economic, and environ- mental

USA N/A Download (free to adjust the threshold criteria)

Interactive

DataPlace ∼ 2360 Demographics, health, arts, real estate

USA After 1990 N/A Interactive

Data Visualizer- World Bank

∼ 49 Social, economic, financial, IT, and environ- mental

209 1960–2007 N/A Interactive

World Bank eAtlas

∼ 175 Development challenges

200 After 1960 Download and upload

Interactive

State Cancer Profiles

Demographic data related to cancer

USA 2006–2010 N/A Interactive

Health Infoscape

Health conditions

USA January 2005– July 2010

N/A Interactive

OECD eXplorer

∼ 40 Demographics, economic, labour market, environment, social, and innovation

34 (335 large regions 1679 small regions)

1990–2005 Download and upload

Interactive (time animation) storytelling

Other tools investigated, but not included, in the above table include STATcompiler, Google Public Data Explorer, NComVA, Social Explorer (USA), PolicyMap (USA), All-Island Research Observatory (UK), and China Geo-Explorer II

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Demographic, social, environmental, health, and other public data, provided by governmental and public authorities, in raw form, can be transformed and presented through visualisation and geovisualisation tools into a more interpretable way. Thus, information and current trends hidden in this data can easily become apparent. This can assist policy stakeholders and decision makers to make more informed decisions. In addition, incorporating geographical knowledge into planning and formation of social and political policies can help derive more accurate spatial decisions. Obvious fields where visualisation and geovisualisation tools can be applied for policy making are investment, population, housing, environmental assessment, public health, etc.

7.3.2 Argumentation Tools

Argumentation tools visualise the structure of complex arguments and debates as a graphical network. In particular, they allow a large number of stakeholders to partici- pate, discuss, and contribute creative arguments and suggestions which then become visualised. This visual representation provides a better and deeper understanding of topics discussed. Thus, complex debates can become easily analysed, refined, or evaluated, e.g. by pinpointing possible gaps and inconsistencies or strong and weak points in the arguments, etc. (Benn and Macintosh 2011).

Table 7.2 summarises well-known argumentation tools and depicts their main characteristics (i.e. whether they are open source, whether they enable import- ing/exporting data, whether they are Web-based or collaborative, the argument framework, whether they support visual representation argumentation structure mod- ification and manipulation of layouts). DebateGraph, Rationale, Cope It!, and bCisive constitute proprietary solutions, while Cohere, Araucaria, Compendium, and Carneades were developed during research studies within universities and re- search projects. Most argumentation tools enable users to share ideas and collaborate upon “wicked problems”. For example, DebateGraph allows users to collaboratively modify the structure and the content of debate maps in the same way they can collaboratively edit a wiki. In addition, MindMeister and Compendium constitute desktop-based solutions that support collaborative argument analysis, while Mind- Meister and bCisive also enable real-time collaboration. Though most argumentation tools provide, even partially, a visual representation of discussions, only few sup- port an easy layout manipulation; such tools are Compendium, Araucaria, Cohere, and DebateGraph. Besides argument analysis, argumentation tools offer additional features, such as argument reconstruction, discussion forums, argument evaluation, etc. For example, Araucaria and Argunet enable users to reconstruct and map de- bates, Cohere enables any content on the web to serve as a node of information in the argument map, and Rationale allows users to judge the strength of an argu- ment by evaluating its elements. These judgments are also represented on the map. Similarly, Carneades allows users to evaluate and compare arguments as well as to apply proof standards. Finally, Cope It! supports a threaded discussion forum, while

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Table 7.2 Argumentation tools. (Source: Benn and Macintosh 2011)

Tool Open source

Import/ export

Web- based

Collab- orative

Argument frame- work

Visual represen- tation

Modify argument structure

Manipulate layouts

Araucaria Yes Yes No No Walton, Toulmin, Wigmore, Classical

Partially Yes Partially

Argunet Yes Yes Yes Yes Classical Yes Partially N/A

Carneades Yes Yes Yes No Walton Partially Yes N/A

Cohere Yes Yes Yes Yes IBIS Yes Partially Partially

Compendium Yes Yes No Yes IBIS Yes Partially Partially

Cope_it! N/A No Yes Yes IBIS Yes Partially N/A

DebateGraph No No Yes Yes Multiple (including IBIS)

Partially Partially Partially

Rationale No No No No Classical Partially Partially N/A

bCisive No No Yes Yes IBIS Partially Partially N/A

MindMeister No Yes Yes Yes N/A Yes Yes Partially

IBIS Issue-Based Information System

bCisive incorporates group planning, decision making, and team problem-solving capabilities.

Argumentation tools facilitate better-informed public debate, policy deliberation, and dialogue mapping on the web about complex political issues. For example, DebateGraph has been used by the Dutch Foreign Ministry in its recent consultation on its human rights policy16, the UK Prime Minister’s Office17, and the White House’s Open Government Brainstorming.18 Compendium has been used in a case study for consultation on regional planning in southeast Queensland (Ohl 2008). Carneades has been developed during the European Estrella project19 that aims to help both citizens and government officials to take part, more effectively, in dialogues for assessing claims and has been used in several applications.

16 http://debategraph.org/MR 17 http://debategraph.org/No10. 18 http://debategraph.org/WH. 19 http://www.estrellaproject.org/.

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7.3.3 eParticipation Tools

eParticipation tools have been specifically developed to involve citizens in the policy- making process, i.e. to enable citizens to get informed, to provide feedback on different policy issues, and to get actively involved in decision making (Gramberger 2001). These tools are mainly based on Web 2.0 features including a variety of social networking tools such as discussion forums or message boards, wikis, electronic surveys or polls, e-petitions, online focus groups, and webcasting.

eParticipation may entail different types of involvement, which are supported by different tools and functionalities, ranging from the provision of information, to deliberation, community building and collaboration, active involvement through consultations, polling, and decision making. The InternationalAssociation for Public Participation (IAP2) has produced a public participation spectrum20, which shows how various techniques may be employed to increase the level of public impact.

Recently, eParticipation tools have been widely used by governmental and public authorities. Through actively engaging citizens, in the planning, design, and de- livery process of public policies, they have moved towards improving democratic governance, preventing conflicts, and facilitating citizens’ active participation in the solution of issues affecting their lives. Table 7.3 presents a set of such recently developed eParticipation tools.

7.3.4 Opinion Mining Tools

The Web’s widespread use over the past decade has significantly increased the pos- sibility for users to express their opinion. The users not only can post text messages now but also can see what other users have written about the same subject in a variety of communication channels across the Web. Moreover, with the advent of Twitter and Facebook, status updates, and posts about any subject have become the new norm in social networking. This user-generated content usually contains relevant information on the general sentiment of users concerning different topics including persons, products, institutions, or even governmental policies. Thus, an invaluable, yet scattered, source of public opinion has quickly become available.

Opinion-mining tools (or otherwise called sentiment analysis tools) perform a computational study of large quantities of textual contributions in order to gather, identify, extract, and determine the attitude expressed in them. This attitude may be users’ judgment or evaluation, their affectual state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader; Stylios et al. 2010).

20 Available at: http://www.iap2.org/associations/4748/files/spectrum.pdf.

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7 A Comparative Analysis of Tools and Technologies for Policy Making 135

Table 7.3 eParticipation tools developed to improve people involvement in government

Typical actions Examples

Citizen Space Consultation and engagement software Create, organise, and publish public consultations across the net on complex policy documents Share consultation data openly in a structured way Provide a way to easily analyse consultation data (both qualitative and quantitative)

Used by government bodies to run e-consultations around the world

Adhocracy.de Participation and voting software Present and discuss issues Collaborate (develop and work on texts together) Make proposals, gather, and evaluate proposals Add polls for decision making Vote on issues

Used in the Munich Open Government Day where citizens could propose policies, projects, and actions of the city

MixedInk.com Collaborative writing software Large groups of people work together to write texts that express collective opinions Post ideas Combine ideas to make new versions Post comments and rate versions to bring the best ideas to the top

Used by the White House to let citizens draft collective policy recommendations for the Open Government Directive

Loomio.org Decision making and collaborative software Initiate discussions and present proposals that can then be discussed, modified, and voted (Agree, Abstain, Disagree, or Block, along with a brief explanation of why) Change their position any time

Used by the Wellington City Council for discussion with their citizens

CitySourced Mobile civil engagement platform Identify and report civic issues (graffiti, trash, potholes, etc.), and comment on existing ones

Used in San Francisco, Los Angeles, and several other cities in California

Puzzledbypolicy Consultation and opinion mapping software Learn about policy issues concerning immigration in the European Union (EU) Give their voice Graphically compare their views on immigration with national and EU immigration policies as well as with the opinions of relevant stakeholders Encourage to join discussions on particular aspects of immigration policy they feel strongly about

Used by the Athens and Torino municipalities and other stakeholders in Tenerife, Hungary, and Slovenia

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Table 7.3 (continued)

Typical actions Examples

Opinion Space Opinion mapping software Collect and visualise user opinions on important issues and policies (rate five propositions on the chosen topic and type initial response to a discussion question) Show in a graphical “map” where user’s opinions fall next to the opinions of other participants Display patterns, trends, and insights Employ the wisdom of crowds to identify the most insightful ideas

Used by US State Department to engage global online audiences on a variety of foreign policy issues

CivicEvolution.org Collaboration platform Engage citizens in structured dialogue and deliberation and develop detailed community-written proposals to make constructive changes

Used by the City of Greater Geraldton, in Australia, to facilitate collaboration and deliberation among participants in participatory budgeting community panels

UbiPol Mobile civil engagement platform Identify and report problems or suggestions Report policy issues

Used by TURKSAT, a publicly owned but privately operated company in affiliation with Ministry of Transportation in Turkey

OurSpace Youth eParticipation platform Engage young people in the decision-making process Enable collaboration

European and National Youth Organisations already using OurSpace

Dialogue App Set up a dialogue Share, rate, comment, and discuss ideas and bring the best ideas to the top

Department for Environment, Food and Rural Affairs in the UK is using Dialogue App to get thoughts, ideas and input on how to improve and formulate policy

In social media, opinion mining usually refers to the extraction of sentiments from unstructured text. The recognised sentiments are classified as positive, negative, and neutral, or of a more fine-grained sentiment classification scheme. Examples include Sentiment140, Sentimentor, Repustate, etc. Opinion-mining tools may also integrate a broad area of approaches including natural language processing, computational linguistics, and text mining. Text mining, for example, can provide a deeper analysis of contributions; it summarises contributions, helps highlight areas of agreement and disagreement, and identifies participants’ main concerns—the level of support for draft proposals or suggestions for action that seem necessary to address. Opinion

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mining tools providing such approaches include DiscoverText, RapidMiner, and Weka.

Classifying statements is a common problem in opinion mining, and different techniques have been used to address this problem. These techniques follow two main approaches; those based on lexical resources and neutral-language processing (lexicon-based) and those employing machine-learning algorithms. Lexicon-based approaches rely on a sentiment lexicon—a collection of known and precompiled sentiment/opinion terms. These terms are words that are commonly used to ex- press positive or negative sentiments, e.g. “excellent”, “great”, “poor”, and “bad”. The method basically counts the number of positive and negative terms, and de- cides accordingly the final sentiment. Machine-learning approaches that make use of syntactic and/or linguistic features and hybrid techniques are very common, with sentiment lexicons playing a key role in the majority of methods.

Table 7.4 presents several opinion mining tools that have been recently developed to analyse public opinions.

Opinion mining tools can help derive different inferences on quality control, pub- lic relations, reputation management, policy, strategy, etc. Therefore, opinion mining tools can be used to assist policy stakeholders and decision makers in making more in- formed decisions. In particular, knowing citizens’ opinion about public and political issues, proposed government actions, and interventions or policies under formation can ensure more socially acceptable policies and decisions. Finally, gathering and analysing public opinion can enable us to understand how a certain community re- acts to certain events and even try to discover patterns and predict their reactions to upcoming events based on their behaviour history (Maragoudakis et al. 2011).

7.3.5 Simulation Tools

Simulation tools are based on agent-based modelling. This is a recent technique that is used to model and reproduce complex systems. An agent-based system is formed by a set of interacting and autonomous “agents” (Macal and North 2005) that represent humans. Agents act and interact with their environment, including other agents, to achieve their objectives (Onggo 2010). Agents’ behaviour is described by a set of simple rules. However, agents may also influence each other, learn from their experiences, and adapt their behaviour to be better suited to their environment. Above all, they operate autonomously, meaning that they decide whether or not to perform an operation, taking into account their goals and priorities, as well as the known context. The analysis of interactions between agents results at the creation of patterns that enable visualising and understanding the system or the phenomenon under investigation.

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Table 7.4 Opinion mining tools

Purpose Sources Classification

SwiftRiver Aggregate, manage, filter, and validate web data Discover relationship and trends in data

Twitter, SMS, e-mail, and RSS feeds

Machine learning

DiscoverText (Text analytics) Search, filter, collect, and classify data Generate insights

E-mail archives, social media content, and other document collections

Machine learning

Repustate Categorise and visualise social media data Extract text sentiment Predict future trends

Twitter or Facebook Multiple languages

Machine learning

Opinion observer (Opinion mining) Extract text sentiment Discover patterns

Web pages Lexicon-based (feature category)

AIRC Sentiment Analyser

Extract text sentiment N/A Lexicon-based

Social Mention Aggregate and analyse social media data Extract text sentiment Discover patterns

Blogs, comments, social media including Twitter, Facebook, Social bookmarks, microblogging services, Images, News, etc.

Lexicon-based

Umigon Sentiment analysis Twitter Lexicon-based

Convey API Sentiment analysis Social media records Machine learning Natural-language processing Statistical modelling

Sentiment140 Sentiment analysis for tweets on a subject or keyword

Twitter Machine learning Natural-language processing

Sentimentor Sentiment analysis for tweets on a subject

Twitter Machine learning

Corpora’s Applied Linguistics

Document summarisation and sentiment analysis

Documents Natural-language processing in combination with an extensive English language lexicon

Attentio Sentiment analysis Blogs, news, and discussion forum sites

Lexicon-based Machine learning

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Table 7.4 (continued)

Purpose Sources Classification

Opinmind Sentiment analysis of bloggers opinion

Blogs Not available

ThinkUp Archive and analyse social media life

Twitter and Facebook Machine learning

In this sense, simulation tools are particularly suited to explore the complexity of social systems. A social system consists of a collection of individuals who interact directly or through their social environment. These individuals evolve autonomously as they are motivated by their own beliefs and personal goals, as well as the cir- cumstances of their social environment. Simulating social systems and analysing the effects of individuals’ interactions can result in the construction of social patterns (e.g. how society responds to a change) that can be used for policy analysis and planning as well as for participatory modelling (Bandini et al. 2009).

There are several general-purpose simulation tools. Most of them are open source and free to be accessed by anyone. Some of these are specially designed to focus on social systems. For example, Multi-Agent Simulation Suite (MASS) is a soft- ware package intended to enable modellers to simulate and study complex social environments. To this end, it models the individual together with its imperfections (e.g. limited cognitive or computational abilities), its idiosyncrasies, and personal interactions. Another tool focusing on the development of flexible models for living social agents is Repast.

An increasing number of tools for the simulation and analysis of social inter- actions has been developed in recent years. These aim to help policy stakeholders and decision makers to simulate the long-term impact of policy decisions. Table 7.5 presents such simulation tools that have been used in the field of health, environment, developmental policies, etc.

7.3.6 Serious Games

Agent-based modelling is used also in serious games, providing the opportunity for experiential and interactive learning and exploration of large uncertainties, divergent values, and complex situations through an engaging, active, and critical environment (Raybourn et al. 2005). Serious games enable players to learn from the accurate rep- resentations of real-world phenomena and the contextual information and knowledge and data embedded in the dynamics of the game. Abt (1987) defines serious games as games with “an explicit and carefully thought-out educational purpose and are not intended to be played primarily for amusement”.

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Table 7.5 Simulation tools simulating the long-term impact of policy decisions

Purpose Input Interface Scale

Threshold 21 Simulate the long-term impact of socioeconomic development policies

About 800 variables concerning economic, social, and environment factors

Flexible Customisable to suit the needs of any sector and country

GLEaMviz Simulate global epidemics

Detailed population, mobility, and epidemic–infection data (real-world data) Compartmentalised disease models

Visual tool for designing compartmen- tal models

Thirty countries in 5 different continents

The Climate Rapid Overview and Decision- support Simulator (C-ROADS)

Simulate long-term climate impacts of policy scenarios to reduce greenhouse gas emissions (CO2 concentration, temperature, sea-level rise)

Sources of historical data

Flexible equations are available and easily auditable

Six-region and 15-region mode

UrbanSim Simulate the possible long-term effects of different policies on urban development (land use, transportation, and environmental planning)

Historical data Flexible Any country

Modelling the Early Life Course (MEL-C)

Simulate the effects of policy making in the early life course and issues concerning children and young people

Data from existing longitudinal studies to quantify the underlying determinants of progress in the early life course

Flexibly adapted for new data and parameter inputs

N/A

Global Buildings Performance Network (GBPN) Policy Comparative Tool

An interactive tool that enables users to compare the world’s best practice policies for new buildings (residential and commercial)

N/A N/A N/A

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Table 7.5 (continued)

Purpose Input Interface Scale

CLASP’s Policy Analysis Modeling System (PAMS)

Forecast the impacts of energy efficiency standards and labelling programs Assess the benefits of policies, identify the most attractive targets for appliances, and efficiency levels

N/A N/A Support basic modelling inputs for over 150 countries Customisable where country- specific data is available

Scenario Modelling and Policy Assessment Tool (EUREAPA)

Model the effects of policies on environment, consumption, industry, and trade

Detailed carbon, ecological, and water footprint indicators

N/A N/A

Budget simulator

Budget consultation platform that enables to adjust budget items and see the consequences of their allocations on council tax and service areas

N/A Flexible Any country

CLASP Comprehensive, Lightweight Application Security Process, EUREAPA, MEL-C, C-ROADS

In policy making, serious games provide the opportunity for players to assume roles of real-world critical stakeholders whose decisions rely on extensive data col- lected from the world around them. In this way, players get educated on the process of decision making as well as on the limitations and trade-offs involved in policy making. Serious games may be used in fields like defence, education, scientific ex- ploration, health care, emergency management, city planning, engineering, religion, and politics (Caird-Daley et al. 2007).

Table 7.6 summarises a number of serious games aiming to tackle different social and political problems. In some of these, users assume the role of critical stakeholders. For example, in 2050 Pathways, users play as if they were the Energy and Climate Change Minister of the UK, while, in Democracy, users act as the president or the prime minister of a modern country. Other games enable users to apply policies/strategies and explore their potential impact. Such an example is the Maryland Budget Map Game that gives the option to make cost-cutting decisions and consider short-term and long-term budget effects. Serious games also help users gain virtual experiences for solving real-world problems. Thus, such games could be used to train citizens and public authorities on how to enforce a policy, e.g. a disas- ter or crisis management policy. For instance, Breakaway simulates critical incidents

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Table 7.6 Serious games focusing on policy making

Purpose Features Scope

2050 Pathways Users act as the energy and climate change minister and explore the complex choices and trade-offs which the UK will have to make to reach the 80 % emission reduction targets by 2050, while matching energy demand and supply

It covers all parts of the economy and all greenhouse gas emissions Users create their emission reduction pathway, and see the impact using real scientific data

Scientific exploration and engineering

Democracy Users are in the position of president (or prime minister) of a modern country and the objective of the game is to stay in power as long as possible

It recreates a modern political system as accurately as possible Users influence the voters and the country by putting in place policies

Education, political strategy

Maryland Budget Map Game

Users act as the administration and general assembly of a state Gives the options to make cost-cutting decisions, weigh revenue options, and consider short-term and long-term budget effects

It explains how budgeting decisions are made

Education, political strategy

NationStates— create your own country

Users build a nation and run it according to their political ideals and care for people

N/A Entertainment, education

Breakaway(disaster management— incident commander)

Helps incident commanders and other public safety personnel train and plan for how they might respond to a wide range of critical incidents

It models acts of terrorism, school hostage situations, and natural disasters

Education, emergency management

The Social Simulator

Trains communications, policy, and frontline staff in a variety of sectors using a number of crisis scenarios Users use the language, tools, and norms of the social web for crisis response

It models terrorist attacks, a leaked report spreads anger about a government policy, etc.

Education, emergency management, political strategy

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7 A Comparative Analysis of Tools and Technologies for Policy Making 143

Table 7.6 (continued)

Purpose Features Scope

CItyOne Users are poised with a series of problems concerning energy, water, or commercial investments (such as banking and payment systems) and are asked to address specific challenges

Planner players think through the sorts of energy, water, or commercial investments that might be needed for particular urban environments in the years to come

Education, awareness

World Without Oil

Engages people concerned with the world’s dependence on oil and both educate and move them to action and contribute “collective imagination”

Risks that oil extraction poses to our economy, climate, and quality of life

Awareness, public good

Urgent Evoke Empowers people all over the world to come up with creative solutions to the most urgent social problems

N/A Awareness

MP For A Week Enables users to learn about the work of a member of parliament (MP) and key features of democracy in the UK

N/A Education

Budget Hero Allows players to build a balanced budget

Creates and tests a budget policy and sees the effects of those cuts or increased expenses on the federal budget

Education, political strategy

and risk scenarios and helps players train and plan their responses. Last, they improve imaginary thinking by exploring possible futures and sparking future-changing ac- tions. For example, Urgent Evoke invites people to come up with creative solutions to the most urgent social problems. Other games focusing on a better world can be found in World-Changing Game21 and Purposeful Games22; however, they are not included in Table 7.6 due to their loose connection with the policy-making process.

21 http://www.scoop.it/t/world-changing-games. 22 http://purposefulgames.info/.

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Table 7.7 Political analysis tools

Purpose Features Scope

PolicyMaker Helps users to analyse, understand, and create effective strategies to promote point of view on any policy question or political issue

Conduct a stakeholder analysis Identify political dynamics of policy making Analyse systematically the supporters, why a policy may face opposition, and what strategies might help it be more effective Design political strategies to support a policy

Policy planning

Oracle Policy Automation for Social Services

Transform complex policies in human language Assess impact of policy changes by enabling what-if analysis of proposed amendments

It includes debugging, regression testing, policy simulation, and what-if analysis for policy changes

Policy delivery

7.3.7 Tools Specifically Developed for Policy Makers

Policy-making tools are designed to facilitate governments, industry, construction experts, and other stakeholders design and deliver national renovation policies and strategies. We present two illustrative examples of such tools in Table 7.7.

7.3.8 Persuasive Tools

Persuasive tools aim to change users’ attitudes or behaviours, such as exercising more or sticking to medication, by enhancing feedback, persuasion and social influence, but not through coercion (Fogg 2002). Persuasive tools can be applied in policy making for promoting different political causes and enhancing policies’ adoption by the public. The Behavioural Insights Team has published a paper on fraud, debt, and error that presents a completely new way of doing policy based on citizens’ behavioural reactions (Behavioural Insights Teem; BIT 2012).

Until recently, only indirect efforts have been made to persuade or motivate citizens adopting a specific policy. For example, the USA23 and Australia24 have developed smartphone applications that enable taxpayers to keep up to date with their tax affairs. In addition, the Australian Tax Office offers a “Tax Receipt Log” app that makes it easier to keep up to date on expenses and tax receipts by using the

23 http://www.irs.gov/uac/New-IRS2Go-Offers-Three-More-Features. 24 http://www.taxreceiptlog.com/blog/gst/tax-calculator/.

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phone camera to take a photo of a receipt, which is then processed and stored. These can serve as persuasive tools reminding citizens of their delayed fees or motivating them to ask for receipts for their purchases.25

7.3.9 Social Network Analysis Tools

A social network consists of nodes representing individual actors within the network and ties which represent relationships between the individuals. Social network analy- sis (SNA) tools facilitate the study of social structure, providing the means (methods) to determine if there are regular patterns in social relationships, and how these pat- terns may be related to attributes or behaviour (Tang et al. 2011). In addition, SNA could identify and map informal networks around any given issue. It can be used to identify who is connected to whom and thus adds value/does not add value, and who should be connected to whom to solve the issue at hand. It also identifies conflicts and broken links that need attention to facilitate more functional action-orientated relationships to achieve goals (Rowena 2010).

SNA can be used in policy making in order to identify a social network’s patterns and key actors and try to influence these (and, therefore, their networks) by applying appropriate targeted policy interventions. For example, SNA could be used to think through and tackle social issues such as unemployment. To do this, SNA may pinpoint the most influential key actors relevant to entrepreneurship or employment (e.g. pioneering entrepreneurs, venture capitalists, etc.) and target these for promoting entrepreneurship or employment policies.

Magus Networker26 was designed to illuminate complex, informal networks so that they become understandable. Powerful querying functions enable key patterns to be identified quickly, displaying where opportunities for improving performance can be developed.

7.3.10 Big Data Analytics Tools

Over the last decade, much information has gradually become open. Sources of such information include machine-operated sensors, video, digital images, e-mail, social media, and open data from government, research institutes, and nongovernmental organisations. The aim of open data movement is to make information freely avail- able, without restrictions and in standard machine readable format (United Nations Department of Economic 2010).

25 Due to the limited number of the identified tools for this category and the following ones, we decided not to summarise them in a table format. 26 http://www.magus-toolbox.com/Networker/.

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Open data create significant opportunities for achieving deeper and faster insights towards knowledge development, decision making and interdisciplinary collabora- tion. However, they have little value if people cannot use them. Thus, new tools and technologies were developed lately to address this problem. One of these technologies is big data analytics.

Big data analytics tools have emerged due to the increasing volume and variety of open data that became available on the web. The term big data refers to datasets so large and complex that are difficult to process using traditional data management and processing techniques.

Big data analytics tools aim to tackle several technological and analytical chal- lenges, such as analysing unstructured data, uncovering hidden patterns, exploiting social media, making fast decisions on massive data volumes, etc. Furthermore, big data predictive analytics aim to unlock the value of big data and make predictions about future, or otherwise unknown events, in a near-real-time mode (Nyce 2007).

Big data analytics tools can be used by government agencies for information purposes, e.g. for understanding what people are saying about government, and which policies, services, or providers are attracting negative opinions and complaints. Moreover, they can find out what people are concerned about or looking for, e.g. from the Google Search application programming interface (API) or Google Trends, which record Google’s search patterns of a huge number of internet users. Based on analysis of current and “historical” facts, they can develop accurate models and forecasts about the future.

In addition, big data can contribute to “smart” cities and governments and to trans- formational government. In particular, big and open data can foster collaboration; create real-time solutions to tackle challenges in agriculture, health, transportation, and more; promote greater openness; and introduce a new era of policy and decision making (Bertot et al. 2014).

Several applications utilising the power of big data are already available. An example is the case of an insurance company, named The Climate Corporation, which examines massive streams of climate data to assess future risk and current damage and provide insurance to farmers who can lock in profits even in the case of drought, excessive rains, or other adverse weather conditions.

Despite the wide adoption in the private sector, big data still have limited applica- tions in policy making. One of the few initiatives is that of New Zealand, which has recently expressed their intention to reform and/or create new governmental services to improve people, society, and economy. In particular, the Ministry of Education in New Zealand is already processing population projections, building consent data, and school enrolment data to work out where new schools are needed. In addition, using geospatial, population, traffic, and travel-to-work information, it is possible to locate the best place for a hospital, school, or community facility, to serve commu- nities most at need, or cut travel times. Moreover, Ministry of Social Development is using data to better learn which of its services get better outcomes for individuals and communities in order to waste less public expenditure on services that do not work and invest more on what does work (New Zealand Data Futures Forum).

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7.3.11 Semantics and Linked Data Tools

Semantic technology enables users to enrich their documents and contents with machine-processable semantics of data that make use of metadata to enable more sophisticated data mining (Berners-Lee et al. 1999). The explicit representation of the semantics of data is accompanied by domain theories, namely ontologies. Linked data is based on semantic web philosophy and technologies, but, in contrast to the full- fledged semantic web vision, it is mainly about publishing structured data in Resource Description Framework (RDF) using uniform resource identifiers (URIs) rather than focusing on the ontological level or inferencing (Hausenblas 2009). Thus, linked data refer to the ability to link together different pieces of information published on the Web and the ability to directly reference to a specific piece of information (Cyganiak et al. 2011; Heath and Bizer 2011).

Responding to this trend, traditional content management systems (CMS) have been improved to support semantic technology and provide semantic lifting of the textual content (Auffret 2001). For example, new CMS enable users to (collaborative) elaborate their documents and online texts submitting comments and annotations (e.g. Enrycher, Annotea). In other cases, users can define and store data based on custom ontologies created by them (e.g. WebNotes). Furthermore, some CMS have tried to support linked data techniques such as automatic detection of entities such as persons, places, and locations, and their linking to external sources, e.g. to dbpedia descriptions of resources (e.g. Apache Stanbol). On the other hand, several tools have been created to address collaborative creation of ontologies (OntoMat-Annotizer, OntoGen).

Considering the recent shift towards massively offering open nonpersonal gov- ernment data, one can easily understand the importance of linked data in the field of policy making (Kalampokis et al. 2011). One example of how Linked Open Data may be effectively used to inform discussions held by policy makers and others is the clean energy information portal, Reegle27. This portal interprets raw data in order to provide useful information and context for end users: It provides high-quality infor- mation on renewable energy efficiency and climate compatible development around the world as easily navigable graphs and tables with a lot of additional information at hand too.

7.4 Summary and Discussion

In the previous section, we presented emerging tools and technologies with the potential to enhance policy making. In this section, we would like to provide an overall discussion of this potential, especially with regard to three main aspects for policy making:

27 http://www.w3.org/2012/06/pmod/report.

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• The main activities facilitated by each tool and technology. Previous analysis showed that each tool category presents a different way for enhancing policy mak- ing. For example, some tools focus on providing information in a user-friendly manner, other tools promote deliberation, other tools are used to gauge pub- lic opinion, etc. Analysing this characteristic, we can draw conclusions on the different ways each emerging tool and technology may be used in policy making.

• The stage of the policy cycle facilitated by each tool and technology. It was previously mentioned that the policy making process is composed of a number of stages; these stages describe the policy life cycle. Analysing the “fit” of each tool and technology in the policy cycle stages promotes understanding of how each tool and technology can enhance the policy-making process. We will consider four main stages of the policy cycle as they were defined by Jann and Wegrich (2006): Agenda setting; policy formulation and decision making; implementation; evaluation and termination.

• The stakeholder types that can use each tool and technology. We categorise the previously identified stakeholders in policy making as follows: institutional stake- holders (i.e. the government), noninstitutional stakeholders (i.e. political parties, political consultants, and lobbyists, the media, nongovernmental organisations, civil organisations, and other interested parties), and the public. Analysing who of these stakeholder groups could use each tool and technology and in what ways, promotes understanding of how these tools and technologies can be adopted in policy making.

Following this, we examined each category of the identified tools and technologies with regard to these three aspects.

Visualisation tools are ideal mainly for information provision, namely for present- ing data in a user-friendly, easy-to-grasp representation. These tools can be used in any stage of the policy cycle, wherever the need for demographic, social, or spatial data representation emerges. For example, they can be used during the decision- making stage in order to fine-tune new policies, during the implementation and evaluation stage in order to understand whether the application of a certain policy brought any changes or even during the agenda-setting stage in order to identify problems that should be addressed with policies. All types of stakeholders may be potential users of visualisation tools depending on the topic addressed and due to the fact that no specialisation is required in order to use and understand them.

Argumentation tools are ideal for structured deliberation, namely for discussing specific issues with the aim to reach a common understanding or a commonly ac- cepted decision. As such, these tools can be useful in all stages of the policy cycle, whenever a targeted deliberation is needed; maybe they are more relevant for the agenda setting, the policy formulation and decision making, and the evaluation and termination stages where such discussions are usually performed. With regard to potential users, in principle, all stakeholders can use argumentation tools. However, previous experience in the field has shown that argumentation tools require a cer- tain degree of logic and critical thinking. It is, therefore, not easy for the general public to productively use these tools without prior training (Tambouris et al. 2011

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and Panopoulou et al. 2012). For this reason, argumentation tools may be more ef- fectively used for somewhat “closed” deliberation groups targeting a specific issue within a certain policy field.

eParticipation tools are ideal for involving the public in the policy-making process. They refer to many different activities such as information provision, deliberation, consultation, gauging public opinion, citizen engagement, community building, etc. eParticipation tools may be initiated by an institutional stakeholder (top-down partici- pation) or a noninstitutional stakeholder or even the public (bottom-up participation). Thus, all stakeholder types are potential users of these tools, although typical usage refers to interactions between the government and the public. Due to the wide spec- trum of supported activities, eParticipation tools may be used in any stage of the policy cycle.

Opinion mining tools are ideal for gauging the public’s opinions and sentiments, thus, they can be used in any stage of the policy cycle whenever such a service is needed. For example, they can be used for gauging the acceptance potential of a new policy or for detecting negative evaluations of a policy. Due to their technical complexity, opinion mining tools are better suited to be used by trained institutional stakeholders or noninstitutional stakeholders, but not the general public.

Simulation tools are useful in policy making for detecting and simulating social interactions and behaviour patterns. For example, they can be used for simulating the long-term impact of different policy alternatives and thus assist in the policy formulation and decision-making stage. Simulation tools are technically complex to implement; therefore, they are mostly suited for usage by a few specialised institutional or noninstitutional stakeholders.

Serious games are useful in policy making for educational purposes. They are mostly relevant to the policy formulation and decision-making stage of the policy cycle, as players may assume a stakeholder’s role in order to explore different policy scenarios on a given topic and make relevant decisions. Serious games can also be used in the implementation stage of the policy cycle, for educating citizens on how to apply a certain state policy, e.g. a health or environmental policy. The main stakeholder group of serious games is the wide public.

The two tools included in our analysis that were specifically developed for policy makers are relevant to the policy formulation and decision-making stage and to the evaluation and termination stage of the policy cycle. Of course, their user group includes only institutional or noninstitutional stakeholders.

Persuasive tools can be used by institutional or noninstitutional stakeholders for influencing public attitudes and behaviours. Thus, it is mostly relevant to the implementation stage of the policy cycle, for strengthening policy adoption.

Social network analysis tools are useful for identifying key actors and social patterns relevant to specific policy areas. These can be used in the policy formulation and decision-making stage, and in the implementation stage of the policy cycle for deciding alternative policies or for strengthening policies’ implementation. SNA is a complex process requiring specialised knowledge, thus it can only be used by trained institutional or non-institutional stakeholders.

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Big data analytics tools can be useful in policy making for processing huge amounts of information and, through this, for detecting and predicting patterns and trends of the public. These activities are relevant to all stages of the policy cycle, maybe less relevant to the implementation stage. Nonetheless, the users of this tech- nology can be the government per se or noninstitutional stakeholders interested in analysing data for a specific topic.

Semantics and linked data tools can be exploited for enhancing interoperability of government data and for creating linkages between open government data and social data. Thus, linked data tools can facilitate better understanding of social data and public opinion and better prediction of public reactions, e.g. to different policy alter- natives. For this reason, semantics and linked data tools seem relevant to all stages of the policy cycle. Again, the specialty required for applying these technologies means that only institutional or noninstitutional stakeholders may be the immediate users of such technologies (Table 7.8).

The table above shows that a policy stakeholder has a number of different ICT tools and technologies at hand. From these, they could choose the most appropriate ICT mix depending on the targeted activity and policy-making stage. For example, we can draw the following conclusions:

• Visualisation tools, argumentation tools, opinion mining tools, big data, linked data, and eParticipation tools may be used at any point of the policy-making process depending on the activities needed.

• Serious games and persuasive tools are the most appropriate in order to strengthen the implementation stage and promote policy adoption.

• The policy formulation and decision-making stage of the policy cycle is the most frequently addressed stage. This is not surprising as this stage involves multiple and diverse activities such as scenario analysis, policy drafting, public consultations, and decision making.

• Visualisation tools, big data analytics tools, and linked data tools can help enhance provision and analysis of large amounts of information.

• A number of different technologies have emerged for detecting opinions, senti- ments, trends, and other patterns of behaviour: opinion mining, simulation, social network analysis, big data analytics tools, and linked data tools. There is clearly a trend for using modern ICT towards analysing crowd knowledge already available online.

• For exploiting advanced tools and technologies expert skills are needed that can only be hired in the context of big (institutional or noninstitutional) organisations.

• For involving the public, visualisation tools, eParticipation tools, and serious games are the most appropriate choices.

Acknowledgments This work is partially funded by the European Commission within the 7th Framework Programme in the context of the eGovPoliNet project (http://www.policy- community.eu/) under grand agreement No. 288136.

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7 A Comparative Analysis of Tools and Technologies for Policy Making 151

Table 7.8 Potential of emerging tools and technologies for enhancing policy making

Tools and technologies

Main activities Policy cycle stages Stakeholder types

Visualisation tools

Information provision

All All

Argumentation tools

Structured deliberation

All (possibly less in the implementation stage)

All (though not easy for untrained public)

eParticipation tools

Information provision, deliberation, gauging opinions, citizen engagement

All All, typically for interaction between the public and the government

Opinion-mining tools

Gauging opinions and sentiments

All Institutional or noninstitutional stakeholders

Simulation tools Detecting and simulating social interactions and behaviour patterns

Policy formulation and decision making

Institutional or noninstitutional stakeholders

Serious games Policy education Policy formulation and decision making, implementation

The public

Tools specifically developed for policy makers

Policy analysis and assessment

Policy formulation and decision making, evaluation and termination

Institutional or noninstitutional stakeholders

Persuasive tools Influencing public attitudes and behaviours

Mostly relevant to Implementation

Institutional or noninstitutional stakeholders

Social network analysis tools

Identifying key actors and social patterns

Policy formulation and decision making, implementation

Institutional or noninstitutional stakeholders

Big data analytics tools

Information processing, detecting and predicting patterns and trends

All (possibly less in the implementation stage)

Institutional or noninstitutional stakeholders

Semantics and linked data tools

Understand opinions, predict public reaction

All Institutional or noninstitutional stakeholders

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Appendix

Visualisation Tools

China Geo – Explorer II http://chinadataonline.org/cge Data Visualizer-World Bank http://devdata.worldbank.org/DataVisualizer DataPlace http://www.dataplace.org http://devdata.worldbank.org/DataVisualizer Dynamic Choropleth Maps http://www.turboperl.com/dcmaps.html e-Atlas of Global Development–World Bank http://data.worldbank.org/products/

data-visualization-tools/eatlas Gapminder http://www.gapminder.org/tag/trendalyzer Google Charts https://developers.google.com/chart Google Public Data Explorer http://www.google.com/publicdata/directory Health Infoscape http://senseable.mit.edu/healthinfoscape NComVA http://www.ncomva.com OECD eXplorer http://stats.oecd.org/OECDregionalstatistics PolicyMap http://www.policymap.com R http://www.r-project.org Social Explorer http://www.socialexplorer.com STATcompiler http://www.statcompiler.com State Cancer Profiles http://statecancerprofiles.cancer.gov/micromaps Visokio Omniscpoe http://www.visokio.com Visualize Free http://visualizefree.com Worldmapper http://www.worldmapper.org

Argumentation Tools

Araucaria http://araucaria.computing.dundee.ac.uk/doku.php Argunet http://www.argunet.org bCisive https://www.bcisiveonline.com Carneades http://carneades.github.io Cohere http://cohere.open.ac.uk Compendium http://compendium.open.ac.uk/institute Cope_it! http://copeit.cti.gr/Login/Default.aspx DebateGraph http://debategraph.org MindMeister http://www.mindmeister.com Rationale http://rationale.austhink.com

eParticipation Tools

Citizen Space https://www.citizenspace.com/info Adhocracy.de http://code.adhocracy.de/en

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CitySourced https://www.citysourced.com CivicEvolution.org http://civicevolution.org Dialogue App http://www.dialogue-app.com/info/ Loomio.org https://www.loomio.org/ MixedInk.com http://www.mixedink.com Opinion Space http://opinion.berkeley.edu OurSpace http://www.ep-ourspace.eu/ Puzzledbypolicy http://www.puzzledbypolicy.eu UbiPol http://www.ubipol.eu/

Opinion Mining Tools

AIRC Sentiment Analyzer http://airc-sentiment.org Attentio http://www.attentio.com Convey API https://developer.conveyapi.com Corpora’s Applied Linguistics http://www.corporasoftware.com/products/

sentiment.aspx DiscoverText http://www.discovertext.com Opinion observer http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.

8899 Opinmind http://www.opinmind.com Repustate https://www.repustate.com Sentimentor http://sentimentor.co.uk Sentiment140 http://www.sentiment140.com Social Mention http://socialmention.com SwiftRiver http://www.ushahidi.com/products/swiftriver-platform ThinkUp https://www.thinkup.com/ Umigon http://www.umigon.com/

Agent-Based Modelling and Simulation Tools

Budget simulator http://www.budgetsimulator.com/info C-ROADS http://climateinteractive.org/simulations/C-ROADS CLASP’s Policy Analysis Modeling System (PAMS) http://www.clasponline.org/

en/Tools/Tools/PolicyAnalysisModelingSystem EUREAPA tool https://www.eureapa.net/ GLEaMviz http://www.gleamviz.org/simulator Global Buildings Performance Network (GBPN) Policy Comparative Tool

http://www.gbpn.org/databases-tools/purpose-policy-comparative-tool MASS http://mass.aitia.ai MEL-C http://code.google.com/p/jamsim

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Repast http://repast.sourceforge.net Threshold 21 http://www.millennium-institute.org/integrated_planning/tools/T21 UrbanSim http://www.urbansim.org/Main/WebHome

Serious Games

2050 Pathways https://www.gov.uk/2050-pathways-analysis Breakaway (Disaster Management-Incident Commander) http://www.

breakawayltd.com Budget Hero http://www.marketplace.org/topics/economy/budget-hero CItyOne http://www-01.ibm.com/software/solutions/soa/innov8/cityone/index.

html Democracy http://www.positech.co.uk/democracy Maryland Budget Map Game http://iat.ubalt.edu/MDBudgetGame MP For A Week http://www.parliament.uk/education/teaching-resources-lesson-

plans/mp-for-a-week-game/ NationStates—create your own country http://www.nationstates.net The Social Simulator http://www.socialsimulator.com Urgent Evoke http://www.urgentevoke.com World Without Oil http://worldwithoutoil.org

Policy-Making Tools

Oracle Policy Automation for Social Services http://www.oracle.com/us/industries/ public-sector/059171.html

PolicyMaker http://www.polimap.com/default.html

Semantics and Linked Data Tools

Annotea http://www.w3.org/2001/Annotea Apache Stanbol http://stanbol.apache.org Enrycher http://ailab.ijs.si/tools/enrycher OntoGen http://ontogen.ijs.si OntoMat-Annotizer http://annotation.semanticweb.org/ontomat Reegle http://www.reegle.info WebNotes http://www.webnotes.net

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Chapter 8 Value Sensitive Design of Complex Product Systems

Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers, Paulier Herder and Jeroen van den Hoven

Abstract We increasingly understand technical artefacts as components of complex product systems. These systems are designed, built, maintained, and deprecated by stakeholders with different interests. To maintain interoperability between compo- nents, standards are being developed. The standardisation process itself is, however, also influenced by different stakeholders.

In this chapter, we argue that a full, comprehensive overview of all relevant components of a system is increasingly difficult. The natural response to complex problems is to delve into details. We suggest that an opposite move towards a more abstract approach can be fruitful. We illustrate this by describing the development of smart meters in the Netherlands.A more explicit focus on the values that play a role for different stakeholders may avoid fruitless detours in the development of technologies. Policymakers would do well by not only addressing functional requirements but also taking individual and social values into consideration.

8.1 Complex Technology

Modern society is highly dependent on a number of infrastructures; electricity and telecommunications infrastructures are considered most critical (Luiijf and Klaver 2006). The desire to move towards a more sustainable energy system with a more decentralised structure, and with a focus on renewable energy sources such as solar energy and wind power, requires adjusting the existing, centralised electricity in- frastructure. By adding information and communication technologies (ICT) to the electricity grid at all levels of the system—from high-voltage transformers to washing machines—each node in the network can decentrally respond to its neighbourhood while safeguarding the reliability of the whole system. The concept of such a new electricity infrastructure is known as the smart grid.

The smart grid concept implies a number of changes at various system levels (national transmission grid, local distribution grid, and residential connections;

A. Ligtvoet (�) · G. van de Kaa · T. Fens · C. van Beers · P. Herder · J. van den Hoven Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands e-mail: [email protected]

© Springer International Publishing Switzerland 2015 157 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_8

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158 A. Ligtvoet et al.

Morgan et al. 2009) with a large role for ICTs (Mulder et al. 2012). However, the precise technological constellation of smart grid systems is yet unknown, and a matter of discussion for politicians and policymakers, (systems) engineers and standardisation bodies, energy providers and distributors, knowledge institutes and telecommunication organisations, and citizen representatives.

Even when limiting ourselves to the residential realm the number of interrelated issues is vast.A case in point is the smart meter. This improved version of an electricity meter is seen as an important element for electricity grid optimisation that also allows for end-user efficiency through insight into consumption patterns (EC 2011). The most comprehensive version of such a device provides information about household energy consumption (accounting for decentral generation). The smart meter transmits this information to energy providers and/or distributors to improve their systems, and control of electric devices remotely, for example, to optimise the load of the distribution grid, or to switch off consumers who have not paid their bills. In practice, however, smart meter deployment is guided by various motives (e.g. fraud detection or improved billing) that have different technical requirements (AlAbdulkarim 2013). At the same time, it has become clear that the roll-out of smart meters can only be successful if the end users in households also recognise their benefits (Cuijpers and Koops 2013; Hierzinger et al. 2013). Until recently, this has not been the case and citizens have voiced concerns about issues including privacy (McDaniel and McLaughlin 2009) and health effects (Verbong et al. 2013; Hess and Coley 2012).

In this chapter, we take the position that technology development is driven by the needs and requirements of a wide range of stakeholders. Among these, technology developers and their competitors play an important role in shaping and standardising technologies. Other stakeholders, such as households, may have a less prominent role in determining the development of technologies, but at times play a significant role in the acceptance of the technology (Mitchell et al. 1997). It is important to identify all the stakeholders involved, and to understand their motives and values so that the technology development can be adjusted in a timely fashion. The Dutch smart metering history provides a cautionary tale as the needs of households end users, one of the main stakeholders, were not sufficiently taken into account. This was one of the main reasons that the Dutch Senate rejected the proposed Energy Bill in 2008 (Cuijpers and Koops 2013) which consequently delayed the roll-out of smart meters for several years.

We aim to shed light on the development process of the complex product system and examine to what extent value-sensitive design (VSD) could have avoided this delay. We employ a case study analysis of the standardisation1 of smart metering in the Netherlands. We add insights from overlapping standards discussions in household automation and show that the interlinked nature of ICT, consumer products, energy systems, and home automation does not allow a strict delineation of technological

1 We follow the definition of standardisation as proposed by de Vries (1999): An activity of estab- lishing and recording a limited set of solutions to actual or potential matching problems, directed at benefits for the party of parties involved, balancing their needs and intending and expecting that these solutions will be repeatedly or continuously used, during a certain period, by a substantial number of the parties for whom they are meant.

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8 Value Sensitive Design of Complex Product Systems 159

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artefacts, and leads to an oversimplification of the issues at stake. We discuss to what extent earlier analysis of this information could have led to adjustments of standards such as the Dutch smart metering requirements (DSMR).

8.2 Smart Meters in the Netherlands

Early scholarly mentions of intelligent or smart meters suggest their (technical) development took place in the 1980s and 1990s (see e.g. Peddie 1988). As we are interested in official standardisation, we provide an overview of Dutch policies regarding smart meters (see also Fig. 8.1), its standardisation, and the stakeholders involved in this process.

Following a letter about security of energy supply from the Ministry of Economic Affairs to Dutch Parliament in 2003 (MinEZ 2003), SenterNovem, a ministry agency, was requested to investigate the standardisation, stakeholder involvement, and con- duct a cost–benefit analysis on the roll-out of a smart meter infrastructure (Dijkstra et al. 2005). Demand-side response was seen as a major contribution to security of supply during peak electricity consumption. The Dutch standardisation institute Nederlands Normalisatie-Instituut (NEN) was commissioned to formulate and de- scribe a national standard for smart meters. The societal cost–benefit analysis proved to be positive (a net gain of 1.2 billion €) with the citizens as main beneficiaries of the roll-out. Interestingly, in the ensuing stakeholder consultation, consumer represen- tatives were not heavily involved: “The point of view of the consumer, individually as a household, or collective via housing corporations, Home Owners Association or Consumers Association was not a key issue” (Dijkstra et al. 2005). The other stakeholders—energy producers, energy suppliers, grid operators, metering compa- nies, telecom, energy regulators—requested the ministry to clearly identify meter functionalities, expedite meter roll-out by setting a time frame, and provide regular consumption overviews (to make smart meters the only affordable solution).

Anticipating the EU Directive 2006/32/EC on on energy end use and energy services, the Ministry of Economic Affairs provided more information on smart meters requirements, citing billing administrative problems and the energy savings

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goals of the Commission as main arguments in favour of smart meters (MinEZ 2006). In 2007, NEN published the technical agreement NTA 8130, which set out a minimum set of requirements for smart metering. The organisation of grid operators (Netbeheer Nederland) took the lead in specifying these requirements, which became DSMR.

In 2008, the Ministry of Economic Affairs revised the electricity and gas bills that implemented the European directive. Grid operators became responsible for meter deployment, and energy providers were appointed the point of contact for consumers. This was supposed to increase clarity for consumers, efficiency, and create a level playing field for market parties. Consumers were required to cooperate in installing smart meters; not doing so would constitute an economic felony. After several rounds of reviews and discussions about privacy, and amendments as a response to the Dutch Data Protection Authority (Customs and Border Protection, CBP), the bills were passed by the Lower House of Parliament in July 2008. By that time, the smart meter and its privacy issues had gained wider public interest. Technical experts assessed possible security and privacy breaches of the meter, and legal experts deemed the proposed solution irreconcilable with the European Convention on Human Rights (Cuijpers and Koops 2013). When the bills were scrutinised by the Senate in 2009, it proposed amendments regarding the mandatory character of smart meters and revisions concerning consumer privacy.

The smart metering bill was amended into a voluntary roll-out of smart meters and reintroduced for political consideration in September 2010. The customer could now decline a smart meter and energy suppliers were required to give customers bimonthly statements with specific minimum information requirements. The grid operators set up uniform authorisation and authentication procedures to ensure that individual measurement data were only used for specific purposes and only after customer consent. The revised bills passed the Lower House of Parliament in November 2010 and was approved by the Senate in February 2011 (Hierzinger et al. 2013).

The Ministry of Economic Affairs agreed on a “small-scale” deployment of smart meters in 2012 and 2013. This 2-year period was used to test the practical implications of roll-out in approximately 400,000 households and to assess consumer response. A midterm review of the roll-out did not identify any major issues, with only 2–3 % of households rejecting the smart meter.At the end of 2013, there is still a political debate about whether the smart meter should be coupled with the functionality to switch off electricity and gas. In other countries, this was the main reason to install smart meters, but the Dutch Consumers’Association argued that remote-controlled switches would constitute a cyber threat on a nationwide scale. In its latest consultation round, the ministry seems to share this view.

Meanwhile, several stakeholders (notably hardware providers) argue that the Netherlands with its 8 million households and 750,000 small and medium enter- prise connections is not large enough to make a customised smart meter financially feasible. They emphasise that the DSMR should be abandoned in favour of European standards.

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8.3 Smart Meters as Complex Product Systems

A smart meter could be seen as an artefact that, like a pair of scissors, can be de- signed or bought on the market in relative isolation. However, our case study already indicates that the development of smart meters is contingent upon developments on international, national, and (inter)organisational levels. Literature on technology management has long conceptualised technological artefacts as subsystems that are linked together, as well as being a component of even larger systems (Clark 1985; Suarez 2004). Tidd (1995) calls these complex product systems, that have three distinctive characteristics:

• Systemic: the systems consist of numerous components and subsystems. • Multiple interactions take place across different components, subsystems, and

levels. • Nondecomposable: the systems cannot be separated into their components without

degrading performance.

This means that technologies, components, and interfaces incorporated in products are interdependent, and thus rely on standard interfaces, but also depend on differ- ent market segments and the range and specificity of performance criteria within these markets. This also means that technological designs, sponsored by different actors, compete for dominance through a process where economic, technological, and sociopolitical factors are intertwined (Rosenkopf and Tushman 1998). The more complex the product system, the greater is the number of actors needing to be aligned for a technological design, and thus the more complicated the actual design process becomes (Suarez 2004).

In the following sections, we indicate that the development of smart meters and home energy management systems (HEMS) is influenced by competing formal and industry standards (Sect. 8.3.1) and that a whole range of actors or stakeholders is involved in the development of these artefacts (Sect. 8.3.2). By combining these two analyses, a multifacted picture emerges.

8.3.1 Competing Standards

In the decision about smart meters and HEMS, competing formal and nonformal standards play a role. We have attempted to provide an overview of different standards that are related to these technologies in Table 8.1. This overview shows us that there are many options for the design of smart meters/HEMS components. Whereas, there may be some room for consolidation, some of the presented standards provide unique solutions to specific problems. As Gallagher (2007) indicates, it remains extremely difficult to pick “winners” ex ante.

Whereas smart metering falls under governmental regulation, the market for HEMS is not regulated. However, depending on the final specifications of the smart meter, some functionalities may overlap. Many different established and

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162 A. Ligtvoet et al.

Table 8.1 Overview of different (competing) standards in the converging technology realms

Component/Subcomponent Competing standards

Smart meter

Smart meter NTA 8130, DRMS X.X, DLMS, IEC 62056-21, NEN-EN 13757, IEEE Std 1901, IEEE P1703, IEEE 1377, DLMS/COSEM standard (IEC 62056 / EN 13757-1), IEEE 802.15.4, Wired M-Bus, M-Bus protocol (EN 13757), 6LoWPAN, ANSI C12.18, IEC 61107

Communication systems

Wired local area networks (application level)

Arcnet vs ATM vs CEPCA vs Ethernet vs FDDI vs Home plug and play vs homeplug vs hiperlan2 vs open air vs Passport vs Powerpacket, Smart Energy Profile 2, Universal Powerline Bus (UPB), DMX512

Wired local area networks (infrastructure level)

FRF vs MPLS/Framerelay vs Orthogonal frequency divising multiplexing vs Salutation vs SSERQ vs Token Bus vs Token Ring vs UPA

Wireless local area networks (infrastructure level)

HomeRF vs IEEE802.16 vs Open air vs IEEE802.11(Wifi), HiperLAN

Wireless personal area network

Bluetooth vs IEEE 802.15.3 vs IEEE 802.15.4 vs Irda vs Zigbee, Z-wave, 6LoWPAN

Power line communication IEEE Std 1901-2010, HomePlug,G.hn(G.9960), PRIME, PLC-G3, IEC-61334 SFSK

Computer networks (wired)

USB vs Convergence bus vs Firewire vs IRDA

Mobile telecommunications

3G vs Dect vs GPRS vs GSM vs UMTS

Home automation systems

Home networks DLNA vs HANA vs HAVi vs HomeAPI vs HOMAPNA vs Moca vs UPnP, IEC/TS 62654, NEN-ISO/IEC 15045-1, ISO/IEC 14543-3-7, IEC 61970, IEEE 1905.1, ITU-T G.9960, CEA-2027-B, CEA-2033, CAN/CSA-ISO/IE, NEN-EN 50090-1, MultiSpeak, IEC 62457, H950 SystemLink

Home automation (wired and wireless)

CEA851 vs CEBus vs Echonet vs EHS vs HBS vs HES vs HGI vs HomeCNA vs HomeGate vs HPnP vs Lontalk vs Smarthouse, ISO/IEC 14543KNX, Zigbee, digitalSTROM, ISO/IEC TR 15044, EN 50090 (KNX/EIB)

Building automation BACnet vs BatiBUS vs COBA vs DALI/IEC 60929 vs FND vs Instabus vs KNX vs Metasys vs MOCA vs Profibus vs Worldfip vs X10 vs Zigbee, NPR-CLC/TR 50491-6-3, ISO 16484-5BACnet NEN-EN 13321-1, NEN-EN 15232, ISO 16484-5, ISO 50001, ISO/IEC 18012-1, EnOcean, Modbus, oBIX

Consumer electronics

Video Displayport vs DVI vs HDMI vs Scart vs VESA vs VGA

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8 Value Sensitive Design of Complex Product Systems 163

Table 8.1 (continued)

Component/Subcomponent Competing standards

Energy systems

Heat/cold storage NEN-EN-IEC 60531, NEN-EN-IEC 60379

Electric stationary storage batteries

J537, 1679-2010 IEEE

Electric car batteries SAE J2847, SAE J2836/1-3, SAE J2931/5, SAE J2758, Smart Energy Profile (SEP 1.1), ASTM D 445, DIN 51 562 (part 1) , ISO 3105

Decentral electricity production systems

Solar photovoltaics IEC 61215, IEC 61646, UL 1703, IEC 60904

Small wind mills DIN EN 61400-25-4, AGMA 6006-A03, NEN 6096

Micro CHPs DIN 4709

newly emerging industries and product markets are involved in HEMS (den Har- tog et al. 2004). We observe a convergence between established industries such as telecommunications, consumer electronics, and home automation (domotics) with new developments in energy industries: photovoltaics, micro heat and power, mi- cro wind, storage, and home automation. ICT plays a crucial role at both national and local level. In the energy sector, integrating information technology with oper- ational technology (IT/operations technology (OT) integration) is seen as a major development. In short, ICT is what makes a smart grid smart.

Actors that originate from the different converging industries develop and pro- mote standards that enable communication not only for components within single industries but also for communication between components that originate in differ- ent industries (van de Kaa et al. 2009). van de Kaa et al. (2009) have performed a search for standards for home networking and present a graphical overview of these standards that originate in different converging industries. We use that graphical overview and have adapted it for the situation of HEMS (see Fig. 8.2).

While some of the standards mentioned in Fig. 8.2 clearly belong in one industry, we also see shifts taking place. We have indicated two of these shifts in the figure. Whereas Universal Serial Bus (USB) started off in the computer industry, it became increasingly used in consumer electronics, e.g. for allowing MP3 music files to be played on audio systems. Likewise, Digital Enhanced Cordless Telecommunications (DECT) was orginally a wireless telephony protocol, but was soon used in baby monitors, and since the development of the Ultra Low Energy variant in 2011 it is used in home appliances, security, healthcare, and energy monitoring applications that are battery powered.

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164 A. Ligtvoet et al.

USB DECT

SCART COAX

USB HDMI

Firewire WiFi

KNX ZigBee Zwave

PLC M-bus

DECT

Information and communication technology

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UPnP

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IEC60531 IEC60379 IEC60904

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TCP/IP GSM GPRS UMTS

IEEE1901/1905 SEP2.0

ISO 9241-11

EN 50491-12

Fig. 8.2 Converging formal and industrial standards in the realm of home energy management

8.3.2 Actor or Stakeholder Analysis

The fact that industries are converging broadens the number of stakeholders involved in technical developments. Although many of the traditional players in the energy field still play a role, opportunities have been created for niche players to take on a larger role. de Vries et al. (2003) have identified search directions for stakeholder identification: production chain, physical systems and their designers, end users and related organisations, inspection agencies, regulators, research and consultancy, education, representative organizations, and organised groups of stakeholders. We take this categorisation as a starting point, but add standardisation bodies as an additional category. We have attempted to capture the interaction of electricity grid components within a value chain representation in Fig. 8.3.

1. We take the electricity production chain as a starting point. Important players are the distribution system operators (DSOs). In the Netherlands, these include the largest DSOs Alliander, Delta, Enexis, and Stedin. Other players are the energy production companies (e.g. Nuon and RWE/Essent), energy suppliers (which may be middlemen between production and consumption), and the national grid operator Tennet.

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8 Value Sensitive Design of Complex Product Systems 165

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2. Various technology providers are involved in the development and design of phys- ical components that are part of the entire value chain. This includes companies such as Cisco Systems, IBM, Philips, Honeywell, and Siemens, meter companies such as Landis + Gyr, Echelon, Itron, and Iskra, and data managers and integra- tors such as Ferranti. Whereas some companies specifically focus on one aspect of the value chain, most have a broader involvement.

3. End users and related organisations are the actual home owners and tenants that may be organised in local groups such as local home owners’ associations (e.g. owners of houses in the same building or street) or national groups such asVerenig- ing Eigen Huis (home owners association of the Netherlands). Expert consultation revealed that consumer representation in standardisation committees is rare in the Netherlands but in some countries such as the UK and Japan consumers are involved in standard development.

4. Important inspection agencies with regard to smart meters include not only Keur- ing van Elektrotechnische Materialen te Arnhem (KEMA), who are charged with organising Dutch meter inspections, but also NMi Certin and Verispect.

5. Regulators (Autoriteit Consument en Markt (ACM) Energiekamer) and policy- makers (Ministry of Economic Affairs (Ministerie van Economische Zaken, EZ)) are involved heavily in the smart meter component.And also the European Union’s policies have effect upon the Dutch smart meters and HEMS.

6. Universities, research institutes, and consultants play an important and major role in standardization for HEMS in the Netherlands. Noteworthy in this context are KEMA (who have provided several cost-benefit analyses) and Nederlandse Or- ganisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO; who have advised the Ministry and parliament). The Netherlands’ national metrology in- stitute Van Swinden Laboratory (VSL) takes a special role in this category, as it is charged with certifying the (tools for) inspection agencies. Other important stakeholders include IT consultancy firms.

7. The education category is less relevant for the identification of stakeholders for HEMS. Although a lot of universities are actively engaged in research relating to smart grids, active participation in standardisation is rare.

8. National representative organisations include consumer organisations such as the Consumers’Association (Consumentenbond). But also DSOs are represented by Netbeheer Nederland and energy producers are represented by Energie Neder- land. At the European level, smart meter providers are represented by European Smart Metering Industry Group (ESMIG). For other networks, see below.

9. Standardisation bodies operate at different levels. Internationally, there are Inter- national Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Telecommunication Union (ITU), at the European level Comité Européen de Normalisation (CEN), Comité Européen de Normalisation Électrotechnique (CENELEC), and European Telecommuni- cations Standards Institute (ETSI), and at the national level NEN. See Sect. 8.3.3 below.

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8 Value Sensitive Design of Complex Product Systems 167

ISO IEC ITU-TInternational

CEN CENELEC ETSIEuropean

NENDutch

TelecommunicationElectrotechnicalBuilding, gas, water

Fig. 8.4 Standardisation organisations in different technology fields at (inter)national levels

8.3.3 Networks of Stakeholders

Not only are the stakeholders mentioned in the previous section active in influencing policy but also are members of various standards organisations and consortia.

Noteworthy are international formal standardisation organisations ISO (gen- eral standards; related to smart meters buildings, gas, and water), International Electrotechnical Commission (IEC; electrotechnical standards), and International Telecommunication Union-Telecom Sector (ITU-T; telecommunication standards). These organisations also have standardisation organisation counterparts active on the European level (CEN, CENELEC, and ETSI), and at the national level (in the Nether- lands this is NEN; see Fig. 8.4). Most standardisation organisations have different work groups or technical committees that develop standards for particular product markets and/or technical areas. Smart meters, for example, are covered by IEC tech- nical committee TC13 and its CENELEC counterpart (conveniently named TC13). However, some aspects may be covered by other technical committees, such as TC57 on power systems management and associated information exchange. Members of these work groups are to a large extent drawn from industrial partners or industrial consortia. The members are, however, deemed to provide their expertise independent of their employers.

Next to the formal standardisation organisations, various consortia exist that de- velop and/or promote standards for components of HEMS. These standards may be based on formal standards, but used in a specific application area. Several subcom- ponents are combined to create a set of coherent components, some of which are not formal standards. In the field of HEMS, these consortia include:

• KNX Association—promoting a standard for home and building control • ZigBee Alliance—promoting a wireless technology designed to address the

unique needs of low-cost, low-power wireless sensor and control networks • Salutation Consortium—promoting a service discovery and session management

protocol providing information exchange among and between different wireless hand-held devices and office automation equipment.

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• Echonet Consortium—promoting the development of basic software and hard- ware for home networks that can be used for remote control or monitoring of home appliances

• The Digital Living Network Alliance—publishing a common set of industry de- sign guidelines that allow manufacturers to participate in a growing marketplace of networked devices.

• Smart Grids European Technology Platform—a European forum for the crystalli- sation of policy and technology research and development pathways for the smart grids sector.

8.4 Values in the Design of Technical Artefacts

The vast amount of standards and stakeholders involved makes an overview of the possible technological trajectories nearly impossible. We suggest that a focus on values and the notion of VSD allows for a more comprehensive view of smart meter and HEMS development.

Values are mentioned in a wide array of disciplines (e.g. philosophy, sociology, economics) and generally denote what something is worth, opinions about that worth, and/or moral principles (Dietz et al. 2005). Values are also described as “enduring beliefs that a specific mode of conduct is personally or socially preferable to an opposite or converse mode of conduct or end-state of existence” (Rokeach 1968). In decision making, values, which can be described as an abstract set of principles, allow us to resolve conflicts by suggesting which preferences are better. They provide us with criteria to distinguish options (see, e.g. Keeney 1994).

Values also play a role in the design and use of technological artefacts. Whereas historically technology may have been considered purely instrumental and value-free (Manders-Huits 2011), it has become clear that technological artefacts exhibit moral and political choices and consequences (even though the moral and political dimen- sion may not be perceived by their designers and users). This means that the choice for a specific technology may imply a social and institutional order without which the technology might not work. Winner (1980) suggests that in “societies based on large, complex technological systems, . . . moral reasons other than those of practical necessity appear increasingly obsolete, ‘idealistic,’ and irrelevant. Whatever claims one may wish to make on behalf of liberty, justice, or equality can be immediately neutralized when confronted with arguments to the effect: ‘Fine, but that’s no way to run a railroad’ (or steel mill, or airline, or communications system, and so on)”. We would argue that the smart grid is one of those large, complex technological systems, for which the same argument holds.

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8.4.1 Value-Sensitive Design

A stream of research that focuses on moral and political dimensions of technology and technology design is called Value Sensitive Design (VSD) (Friedman et al. 2002; Borning and Muller 2012). VSD seeks to be proactive to influence the design of tech- nology early in and throughout the design process. It employs conceptual, empirical, and technical investigations (Friedman et al. 2008; van de Poel 2009):

• Conceptual investigations aim, for instance, at clarifying the values at stake, and at making trade-offs between the various values.

• Empirical investigations involve social scientific research on the understanding, contexts, and experiences of the people affected by technological designs.

• Technical investigations involve analysing current technical mechanisms and designs to assess how well they support particular values, and, conversely, iden- tifying values, and then identifying and/or developing technical mechanisms and designs that can support those values.

Many of the technological examples addressed inVSD literature relate to ICTs (Fried- man 1996; van den Hoven 2007; Friedman et al. 2008), which is why we expect the approach to be pertinent to smart meter/home/grid technologies. VSD started from the recognition that when designing information technologies, the predominant, tra- ditional focus of engineers is on functionality, i.e. the efficiency, reliability, and affordability of (new) technologies—conform the practical necessity argument iden- tified by Winner (1980). Furthermore, the point of reference is often the designer’s own experiences, needs, and values. For example, it has been shown that software designers (unknowingly) design software that is more aligned with males than with females. Friedman (1996) also mentions an example of educational software that is geared towards the American competitive education system which is less successful in foreign classrooms, where cooperation is considered more important.

8.4.2 Values in Our Research

Although it is embedded in moral philosophy, VSD uses a broad sense of values: values refer to what persons, either singularly or collectively, consider important to their lives. However, the 56 personal values that the Schwartz Value Survey, commonly used in social sciences (Dietz et al. 2005), defines, might not relate to technological artefacts and technology use. For this study, we therefore focus on a subset that is often mentioned in VSD literature. Next to the already mentioned func- tional values (accountability, correctness, efficiency, environmental sustainability, legitimacy, reliability, safety, tractability), we address social values (cooperation, courtesy, democracy, freedom from bias, identity, participation, privacy, trust) and individual values (autonomy, calmness, economic development, informed consent,

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ownership, universal usability, welfare). Most of these values are defined in Fried- man et al. (2008). For the purposes of our research, we have translated these into broad definitions which can be found in Table 8.2.

We have attempted to identify the values that played a role in the development of smart meters (Ligtvoet et al. in press). Based on expert elicitation, the five most important values associated with smart meters are:

1. Privacy: The system allows users to determine which information about them is used and communicated.

2. Correctness: The system provides correct data or performs the correct function. 3. Reliability: The system fulfils its function without the need to monitor/control it. 4. Informed consent: The system allows its users to voluntarily agree to its activation,

based on comprehensible information. 5. Economic development: The system is beneficial to its users’ economic or

financial status.

These results very closely match the general impression of the smart metering debate in the Netherlands. Privacy is a very important value that was virtually ignored at the start of the implementation process. As could be expected for a device that is designed to measure, the functional values of correctness and reliability are also ranked high. The individual values of informed consent and economic development emphasise that end users’ needs should be taken into consideration.

An interesting and unexpected finding of our expert group discussion was that these values depend on the delineation of the system. The experts indicated that the important values actually shift when the smart meter is not only seen as a connected measuring device but also more as an energy management nexus for households. This generates a new ranking of values for HEMS:

1. Economic development: The system is beneficial to its users’ economic or financial status.

2. Universal usability: The system can easily be operated by all users. 3. Privacy: The system allows users to determine which information about them is

used and communicated. 4. Autonomy: The system allows its users to make their own choices and pursue

their own goals. 5. Reliability: The system fulfils its function without the need to monitor/control it.

Here, we see a clear shift towards the individual and social values of the users and slightly less emphasis on the functional values of the technology. We believe that this corresponds with findings of Krishnamurti et al. (2012) and Balta-Ozkan et al. (2013). Compared with standalone smart meters, a clearly higher score was given for participation and well being, again emphasising the user experience. Also, the values legitimacy and freedom from bias became much less important. In the discussion, it became clear that HEMS are seen as a commercial consumer product, for which consumers are personally responsible.

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Table 8.2 Overview of 23 values that we found in the value sensitive design literature

Value Description

Accountability The system allows for tracing the activities of individuals or institutions

Autonomy The system allows for its users to make their own choices and choose their own goals

Calmness The system promotes a peaceful and quiet state

Cooperation The systems allows for its users to work together with others

Correctness The systems processes the right information and performs the right actions

Courtesy The system promotes treating people with politeness and consideration

Democracy The system promotes the input of stakeholders

Economic development The system is beneficial to the economic status/finances of its users

Efficiency The system is effective given the inputs

Environmental sustainability

The system does not burden ecosystems, so that the needs of current generations do not hinder future generations

Freedom from bias The system does not promote a select group of users at the cost of others

Identity The system allows its users to maintain their identity, shape it, or change it if required

Informed consent The systems allows its users to voluntarily make choices, based on arguments

Legitimacy The system is deployed on a legal basis or has broad support

Ownership The system facilitates ownership of an object or of information and allows its owner to derive income from it

Participation The system promotes active participation of its users

Privacy The system allows people to determine which information about the is used and communicateda

Reliability The system fulfils its purpose without the need to control or maintain it

Safety and health The system does not harm people

Tractability The functioning of the system can be traced

Trust The system promotes trust in itself and in its users

Universal usability The system can be easily used by all (foreseen) users

Welfare The system promotes physical, psychological, and material well-being

a We acknowledge that this is a limited definition of privacy

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8.5 Discussion

8.5.1 From Values to Design Requirements

VSD purports to be a holistic approach that combines theory with empirics (Manders- Huits 2011). The identification of values should therefore be linked to the formulation of design requirements for complex product systems, in our case smart meters and HEMS.

The need for (elements of) privacy was addressed in the NTA8130 standard and the requirement of an encryption protocol was added. Functional requirements of correctness and reliability were already covered by the Dutch measurement code and no further requirements were necessary. Informed consent is not easily addressed from a technological standpoint, but it did prove important in the debate in the Senate. The solution was not technical, but procedural. The end user was given four options: no smart meter but an ordinary one, a smart meter that does not communicate, low-frequency communication, or high-frequency communication. The economic development was addressed by several cost-benefit assessments and a restriction of the metering tariff.

Given the nature of HEMS (i.e. more like a consumer product), the values as- sociated with it should also be addressed in a slightly different way. The focus on economic development suggests a restriction in the price of the system and a clear indication of how much can be saved by installing such a system. Universal us- ability emphasises the need for easy-to-use interfaces: end users should not require an engineering degree to operate the system. Privacy remains an issue and requires communication channels to be secured—similar to (mobile) telecommunication and computing requirements. Autonomy suggests that the users should be in charge of their home energy management and automation: this is closely linked to ease of use. And finally, the system should be reliable like other consumer products.

We acknowledge that the current research has performed an ex post analysis of values and identified issues that were already resolved in the course of history of the Dutch smart meter (standard and requirements). The proof of the pudding would be an ex ante assessment and monitoring of the upcoming issues.

8.5.2 Values Salience

Our research has led us to question the extent to which values are important and identified, which one could call “values salience”. Comparing smart meters to other technological systems such as communication systems or smart grids, we find that the meter has received quite some attention. (On the basis of our interviews, we believe that smart meters were initially only considered a technical issue.) We sug- gest that values salience relates to the size of the technical system according to an inverted U as shown in Fig. 8.5. This means that small technical components such as communication protocols attract little attention and the general public remains

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Fig. 8.5 Values salience of a technical component depends on its size

Technical component size V

al ue

s sa

lie nc

e

low

high

small large

System- of-systems (e.g. smart

grid)

System (e.g. smart meter)

Technical (sub)component (e.g. RF protocol)

indifferent. The same argument holds for large systems such as an entire electricity grid—although the public may still be able to judge some of the importance of such an artefact for their own energy supply, they largely remain uninvolved. However, the level of the household, thus of the smart meter, is most visible to people and therefore their attention and ability to express values is much greater at this level.

8.5.3 Multidisciplinary Approach

Our research contributes to the literature on innovation management and standardis- ation (e.g. Schilling 1998, 2002; Suarez 2004; Sheremata 2004). Scholars in the area of innovation management and standardisation have attempted to explain standard dominance and draw from various areas of research including network economics and institutional economics (van de Kaa et al. 2011). They have come up with technology-, firm-, and environmental-level factors that explain standard dominance (Suarez 2004). In this chapter, we shed light on another level of analysis that is neglected in the literature: the end user. We provide a first illustration of the notion that societal acceptance of a platform will grow if a technological design is modified to changing user requirements related to ethical and societal values surrounding the technology. Privacy is the most salient value for the Dutch smart meter case, but informed consent also played an important role. Combining literature from philoso- phy and ethics on the one hand and technology management on the other hand, we provide a clearer view on the influence of factors relating to the end user. Future research could further explore the ex ante translation from identified values to actual design requirements.

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8.6 Conclusion

Our case study of the development of smart metering and smart metering standards in the Netherlands shows the complexity of introducing new technologies in an existing sociotechnical system. We believe that our findings can be generalised, not only to other components of the smart grid but also to other systems-of-systems that are deployed and used by a wide array of stakeholders.

Current technological development is often so complex that stakeholders are un- able to fully assess new technologies. Nor are they able to weigh the input of all stakeholders. First of all, because not all stakeholders are always involved and sec- ondly, because people’s opinions and beliefs change because of new information, insights, and experiences. Although the introduction of a new technical component may start off from a very functional and technical position, nontechnical issues (values) can be introduced by consumers’ associations and other stakeholders.

It can be argued that a lack of consideration for these values can lead to a delay in the roll-out of new technologies. Even though technical solutions only seem to address technical problems, they influence society through the interconnected nature of modern infrastructures. Especially, when technology is “visible” at the household level, consumers or their representatives can be expected to have an opinion. For pol- icymakers, it would be wise to foresee such stakeholder involvement and to address stakeholder values in an early stage.

The outcome of a values elicitation is a more balanced representation of the interests of all stakeholders, including end users: a combination of functional, social, and personal values. This focus on values may help designers in their search for better technical and functional specifications. However, such a design process is complicated by the fact that technical artefacts form an intricate part of larger systems- of-systems. As we have shown, depending on the system focus, the related values are somewhat different and there still may be some discussion to what extent an artefact serves a higher (system level) goal. This is certainly an area in which VSD could further develop and provide more guidance.

Acknowledgements This research was supported by Netherlands Organisation for Scientific Research (NWO) grant MVI-12-E02 on responsible innovation (“maatschappelijk verantwoord innoveren”). We are indebted to our valorisation committee (Gertjan van den Akker, Theo Borst, Johan Crols, Michiel Karskens, Gerrit Rietveld, Rick van der Tol, and Gerritjan Valk) for their insight and comments. We would also like to thank our interviewees: Johan Boekema, Coco Geluk, Tjakko Kruit, Erik Linschoten, Willem Strabbing, Jeike Wallinga, and Teus de Zwart.

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Luiijf EA, Klaver MH (2006) Protection of the Dutch critical infrastructures. Int J Crit Infrastruct 2(2/3):201–214

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Chapter 9 Stakeholder Engagement in Policy Development: Observations and Lessons from International Experience

Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink and Catherine Gerald Mkude

Abstract This chapter provides a starting point for better understanding how different approaches, tools, and technologies can support effective stakeholder par- ticipation in policy development. Participatory policy making involves stakeholders in various stages of the policy process and can focus on both the substance of the policy problem or on improving the tools and processes of policy development. We examine five international cases of stakeholder engagement in policy development to explore two questions: (1) what types of engagement tools and processes are useful for different stakeholders and contexts? And (2) what factors support the effective use of particular tools and technologies toward constructive outcomes? The cases address e-government strategic planning in a developing country, energy policy in a transi- tional economy, development of new technology and policy innovations in global trade, exploration of tools for policy-relevant evidence in early childhood decision making, and development of indicators for evaluating policy options in urban plan- ning. Following a comparison of the cases, we discuss salient factors of stakeholder

N. Helbig (�) · S. Dawes Center for Technology in Government, University at Albany, 187 Wolf Road, Suite 301, 12205 Albany, New York, USA e-mail: [email protected]

S. Dawes e-mail: [email protected]

Z. Dzhusupova Department of Public Administration and Development Management United Nations Department of Economic and Social Affairs (UNDESA), New York, USA e-mail: [email protected]

B. Klievink Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX, Delft, The Netherlands e-mail: [email protected]

C. G. Mkude Institute for IS Research, University of Koblenz-Landau, Universitätsstr. 1, 56070 Koblenz, Germany e-mail: [email protected]

© Springer International Publishing Switzerland 2015 177 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_9

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selection and representation, stakeholder support and education, the value of stake- holder engagement for dealing with complexity, and the usefulness of third-party experts for enhancing transparency and improving tools for engagement.

9.1 Introduction

Complex public problems are shared and dispersed across multiple organizations and domains (Kettl 2002). Consider, for example, the array of concerns associated with improving air quality or assuring the safety of food products. The formal governmen- tal responses to these specific public needs are addressed through public policies. Policy might focus on different geographic locations, processes, or products, or could specify how certain outcomes are defined, observed, and assessed. Moreover, individuals, families, communities, industry, and government itself are all affected by policy choices, and they all have interests in both the decision-making process and the final decisions (Bryson 2004).

In light of seemingly intractable and complex social problems, public administra- tors have shifted toward governance activities that allow citizens and stakeholders to have deeper involvement in the policy-making process and the work of government (Bingham et al. 2005). Governance models which focus on quasi-legislative activ- ities such as participatory budgeting, citizen juries, focus groups, roundtables, or town meetings (Bingham et al. 2005; Fishkin 1995) create opportunities for citizens and stakeholders to envision their future growth (Myers and Kitsuse 2000), clarify their own policy preferences, engage in dialogue on policy choices, or bring various groups to consensus on proposals (McAfee 2004). The models vary based on degree of involvement by the general population, whether they occur in public spaces, if the stakeholders are actually empowered, and whether they lead to tangible outcomes (Bingham et al. 2005).

Stakeholder engagement objectives may also vary by their point of connection with the policy process (Fung 2006). The policy process is complex and there are many different ways to conceptualize how it works. The stages heuristic of public policy making is one of the most broadly accepted (Sabatier 1991). Although the utility of the stages model has limits, and numerous advances in theories and methods for understanding the policy process have been made, the stages heuristic continues to offer useful conceptualizations (Jenkins-Smith and Sabatier 1993). While specifi- cation and content of the stages vary somewhat throughout the literature, however (as shown in Fig. 9.1), models often comprise some combination of problem identifica- tion, agenda setting, formulation, adoption, implementation, and policy evaluation (Lasswell 1951; Easton 1965; Jones 1977). More recent conceptualizations involve feedback across the various stages.

Research in both the public and private sectors has identified a number of bene- fits associated with stakeholder engagement in governance. Stakeholders’ interests illuminate the multiplicity of factors that underlie policy problems, decisions, and implementation. Direct engagement of stakeholders increases public understanding

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Fig. 9.1 Stages of the policy process

of the issues and the consequences of different choices. Accordingly, engagement generates more options for policies or actions. Engagement brings more information into the deliberation process from different kinds of stakeholders so that decisions are more likely to avoid unintended consequences and fit better into existing contexts. Engagement also reveals both conflicts and agreements among different stakeholder groups. While taking stakeholders into account is a crucial aspect of solving public problems, policy development includes both powerful and powerless stakeholders within the process (Bryson 2004). Some stakeholders have the power, knowledge, or resources to affect the policy content, while others are relatively powerless but nevertheless are affected, sometimes in dramatic ways (Brugha and Varvasovszky 2000). Thus, open and evenhanded stakeholder engagement, especially among those with conflicting viewpoints, can sometimes resolve differences and build trust in the policy-making process and help secure public acceptance of decisions (e.g., Klievink et al. 2012).

In the past 20 years, specialized technologies, electronic communication, and advanced analytical, modeling, and simulation techniques have been developed to support governance processes. Administrators, analysts, and planners must decide how and when to engage citizens and stakeholders in governance, particularly during the different stages of policy making. They must also consider which mechanisms

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to use for managing the relationships (Bryson 2004) and must select from a variety of tools and techniques. In this chapter, we begin to explore two questions: (1) What types of engagement tools and processes are useful for different stakeholders and contexts? And (2) what factors support the effective use of particular tools and technologies toward constructive outcomes?

The next sections start by reviewing the foundational elements of stakeholder the- ory and its relation to governance, including a summary of tools and techniques used to identify stakeholders and analyze stakeholder interests and ways to classify types of engagement. We then offer five case stories of stakeholder engagement in complex and dynamic settings across the world including e-government strategic planning in a developing country, exploring different uses of evidence in early childhood decision making, developing technology and policy innovations in global trade, and involving citizens in the design of energy policy and transportation planning. The cases vary in both policy content and the extent to which newer technologies were used to deal with the complexity of the engagement process, their accessibility and understandability to outsiders, and the advantages and disadvantages they offer to expert stakeholders as compared to laymen. We then compare the cases, discuss their similarities and differences, and conclude with a discussion of the usefulness of different tools and processes for different stakeholders and contexts and the factors that support their effectiveness.

9.2 Foundations of Stakeholder Engagement

Stakeholder engagement, as a concept, originated within organizational studies as an approach to managing corporations (Freeman 2010; Bingham et al. 2005; Donald- son and Preston 1995; Mitchell et al. 1997). This approach has since been adapted for use by public sector organizations to highlight the importance of stakeholders in various aspects of the policy-making process (Bingham et al. 2005). Bingham et al. (2005) situate stakeholders as part of “new governance” concepts where government actively involves citizens as stakeholders in decision making through activities such as deliberative democracy, participatory budgeting, or collaborative policy making. Research on stakeholder inclusion in government processes has been found to en- hance accountability, efficiency in decision-making processes, and good governance (Ackerman 2004; Flak and Rose 2005; Yetano et al. 2010). The growing popularity of stakeholder analysis reflects an increasing recognition of stakeholder influences on decision-making processes (Brugha and Varvasovszky 2000).

9.2.1 Defining Stakeholders

The term “stakeholder” is defined differently by different disciplines. Most defini- tions mention similar stakeholder categories such as companies and their employees

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or external entities such as suppliers, customers, governments, or creditors. In the public sector, the definition of stakeholder emphasizes categories of citizens defined by demographic characteristics, life stages, interest groups, or organizational bound- aries (Bingham et al. 2005; Ackerman 2004; Yetano et al. 2010). Stakeholders can be both internal to the government (e.g., the government organizations responsible for policy implementation) and external to it (e.g., the industries, communities, or individuals to be affected by government actions or rules).

In this chapter, we use Freeman’s (1984) definition of stakeholder as any group or individual who can affect or is affected by the achievement of an organization’s objectives. In the public sector, “organization” is understood to mean a government entity or body with responsibility for public policies or services. In the simplest terms, those who can affect or may be affected by a policy can be considered stakeholders in that policy. In traditional expert-based approaches to policy making, the needs of stakeholders are indirectly addressed by public agencies and acknowledged experts (Bijlsma et al. 2011; De Marchi 2003). In these expert-based approaches, internal and external stakeholders may be consulted, but in participatory approaches, stake- holders are not only consulted but are also involved in a structured way to influence problem framing, policy analysis, and decision making. Bijlsma et al. (2011) define participatory policy development as the “influence of stakeholder involvement on the development of substance in policy development, notably the framing of the policy problem, the policy analysis and design, and the creation and use of knowledge” (p. 51).

9.2.2 Stakeholder Identification and Analysis

Stakeholder identification and analysis is an important first phase in stakeholder en- gagement processes (Freeman 2010). Analysis typically involves five steps (Kennon et al. 2009): identifying stakeholders, understanding and managing stakeholders, setting goals, identifying the costs of engagement, and evaluating and revisiting the analysis. Through these various steps, an analysis helps to distinguish stakeholders from non-stakeholders and to identify the ways that stakeholders need to be engaged during different parts of the policy cycle. Over time, the mix of stakeholders in a particular policy issue is likely to change, as new stakeholders may join the engage- ment activities, while others may drop out (Elias et al. 2002) or shift among different types. Joining, dropping out, or moving among types thus dynamically changes the configuration and analysis of stakeholders over time.

Various techniques for stakeholder identification and analysis are reviewed in the literature. These techniques focus attention on the interrelations of groups or organizations with respect to their interests in, or impacts on policies within, a broader political, economic, and cultural context. These techniques also provide ways for analysts to understand stakeholder power, influence, needs, and conflicts of interest. Bryson (2004) characterized stakeholder identification as an iterative process highlighting the need to determine the purpose of involving stakeholders

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and cautioning that these purposes may change over time. He describes a stage approach to selecting stakeholders: someone or a small group responsible for the policy analysis develops an initial stakeholder list as a starting point for thinking about which stakeholders are missing. Brainstorming and the use of interviews, questionnaires, focus groups, or other information-gathering techniques can be used to expand the list. Bryson (2004) notes “this staged process embodies a kind of technical, political, and ethical rationality” (p. 29). He also lists a variety of analysis techniques, such as power and influence grids (Eden and Ackermann 1998), bases of power diagrams (Bryson et al. 2002), stakeholder–issue interrelationship diagrams (Bryant 2003), problem-frame stakeholder maps (Anderson et al. 1999), ethical analysis grids (Lewis 1991), or policy attractiveness versus stakeholder capability grids (Bryson et al. 1986). Each of these tools is used in different situations to help understand and identify various aspects of stakeholder interests.

9.2.3 Stakeholder Engagement

Stakeholder engagement methods are the means by which stakeholder views, infor- mation, and opinions are elicited, or by which stakeholders are involved in decision making. Engagement can take various forms. The International Association for Pub- lic Participation identified five levels of stakeholder engagement: (IAP2 2007). At the simplest level, informing, stakeholders are merely informed, for example, via websites, fact sheets, newsletters, or allowing visitors to observe policy discussions. The level of engagement in this form is very low and suitable only to engage those stakeholders with low urgency, influence, importance, or interest (Bryson 2004). Various methods are available for consulting, including conducting interviews, ad- ministering surveys to gather information, opening up draft policy documents for public comment, or using Web 2.0 tools to gather ideas. The main goal of this form of engagement is to elicit the views and interests, as well as the salient information that stakeholders have with regard to the policy concern.

Involving stakeholders is a more intensive engagement where stakeholders work together during the policy development process. Some tools used to ensure that ideas, interests, and concerns are consistently understood and addressed include scenario building (Wimmer et al. 2012), engaging panels of experts such as the Delphi method (Linstone and Turoff 1975), or group model building that includes simulating policy choices, games, or role playing (Andersen et al. 2007; Vennix et al. 1996). Models, simulations, or scenarios can be used as boundary objects (Black and Andersen 2012; Star and Griesemer 1989) to enable diverse sets of stakeholders to have a shared experience and to exchange localized or specialized knowledge in order to learn, create common understanding, and identify alternative choices. All these levels focus on the flow of information among actors, but the direction and intensity vary.

The most intense engagement is realized through full collaboration with or even empowerment of stakeholders. In the IAP2 spectrum of public participation, collab- oration means stakeholders’ advice and recommendations will be incorporated in

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the final decisions to a maximum extent (IAP2 2007). Empowerment means that the final decision making is actually in the hands of the public. Realistically, collabora- tion and empowerment exist within institutional and legal parameters. For example, the policy-making body (usually a government agency) will need to put some con- straints or boundaries around the policy options that comport with the limits of its legal authority. For both levels, consensus-building approaches are essential. This can be done through citizen juries (Smith and en Wales 2000), the enactment of a stakeholder board (urbanAPI1; Klievink et al. 2012), or by setting up living labs (Tan et al. 2011; Higgins and Klein 2011) in which stakeholders collaboratively develop, implement, and evaluate solutions within a given context. All of these approaches not only assist in incorporating stakeholders’ views into the policy pro- cess but also enhance acceptance by stakeholders because they were part of the deliberation process (e.g., see Klievink and Lucassen 2013).

9.3 Cases

Below we offer five case stories about stakeholder engagement in policy making. The cases were recommended by a diverse set of eGovPoliNet consortium partners who shared an interest in tools and techniques to support the policy process. The main goal of the case stories is to highlight the roles that stakeholders can play in policy development and to discuss how different methods, tools, and technologies could be used for engaging stakeholders in the policy process. Each case describes a situation where stakeholders were involved in the problem definition, agenda setting, and formulation stages of the policy cycle. In all cases, a trusted third party, generally university researchers, facilitated the process and applied the tools. The cases vary in policy content and in the extent of technology use in the engagement process. They represent different policy domains, and governments at different stages of develop- ment with different political systems. The first three cases focus on substantive policy choices for e-government strategic planning, alternative energy policy, and global trade inspection. The last two concentrate on stakeholder involvement in improving tools to support the policy-making process. Of those, the first focuses on connect- ing policy makers and modelers in building a supportive framework for assessing early childhood programs and second involves stakeholders in defining assessment indicators to be built into a model that supports urban planning decisions.

In this section, we describe these diverse situations as the foundation for the comparison presented in Sect. 9.4 where we identify similarities and differences that suggest approaches, tools, and techniques that are useful and effective in different contexts and with different kinds of stakeholders.

For each case below, we present the key characteristics of the policy-making situ- ation and assess the purpose of stakeholder engagement. With respect to stakeholder

1 UrbanAPI is an EC FP7 project focused on interactive analysis, simulation, and visualization tools for agile urban policy implementation http://www.urbanapi.eu/.

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identification and analysis, we cover both the identification of stakeholders (types) involved and the methods used for identification and analysis. With respect to stake- holder engagement (see Sect. 9.2.3), we analyze the engagement approach followed in each case, as well as the type of participation and the methods of stakeholder en- gagement. We also inventory which tools and technologies were used and describe the results and outcomes of each engagement process.

9.3.1 E-Government Strategic Planning in Afghanistan

The EGOV.AF project was a joint initiative of the Afghanistan Ministry of Commu- nications and Information Technology (MCIT) and the United Nations University– International Institute for Software Technology–Center for Electronic Governance (UNU-IIST-EGOV). One goal of EGOV.AF was to develop a nationally owned EGOV strategy and program (Dzhusupova et al. 2011). In many developing countries, two major challenges to long-term sustainability of e-government initiatives exist: (1) too much reliance on donor funding (Ali and Weerakkody 2009) and (2) lack of understanding regarding citizen demand for e-government services (Basu 2004). To mitigate these challenges, a strategy of the EGOV.AF project was to reach out to stakeholders in a systematic way before putting together a national e-government policy. Afghanistan is one of the poorest countries in the world (World Bank 2012) plagued by a recent history of war and conflict, with a significant digital divide between rural and urban areas. Thus, identifying important stakeholders and under- standing their interests, expectations, capacity, and influence were very important, but also very difficult.

In 2011, the UNU-IIST-EGOV team engaged in action research with the MCIT through the development of a stakeholder analysis tool and execution of a series of stakeholder identification exercises, analyses, and workshops. The MCIT was the project owner and lead agency, while the UNU-IIST-EGOV provided mentorship, additional experience, expertise to apply stakeholder analysis tools and engagement methods, and capacity to facilitate the process.

Historically, standard exercises at the MCIT around e-government planning had focused only on consultation with technology stakeholders, such as consulting com- panies. Initially, the MCIT did not see the value in involving citizens, local provinces, international organizations, academics, or nonprofit organizations that focus on gov- ernance. The case was made by UNU to engage people outside of government to address several factors: Many of the nonprofit organizations are advocates for trans- parency and good governance, donor organizations assert influence over the process through special programs and funding, and the provincial governments work closely and most directly with citizens.

To expand MCIT’s limited understanding of this broad set of stakeholders, they conducted a series of consultation and involvement activities. The first instance of engagement with stakeholders was a survey that asked questions about their inter- ests, needs, activities, and conditions. The team also collected additional contextual

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information from websites and professional contacts. The second stage of engage- ment occurred after the analysis of the survey. Using the stakeholder analysis tool developed by UNU, the MCIT identified from the survey results a set of interested and relevant stakeholders, defined the roles for major stakeholders in the policy pro- cess, and developed communication strategies. Later these stakeholders were invited to attend two stakeholder workshops. One workshop was designed as a “visioning” exercise and another designed to elicit “strategy development.” During the work- shops, MCIT and UNU-IIST-EGOV were able to provide participants with general knowledge about approaches and methodologies regarding strategy development, provided examples from other countries, and facilitated discussions focused on e- government in the local Afghanistan context. Participants in the workshops were encouraged to share their ideas and to discuss and prioritize strategic goals and tasks for e-government based on the mutual consensus among them. The last stage of the stakeholder engagement was to complete a series of face-to-face meetings and e-mails in which the MCIT collected suggestions on strategic actions. Additional feedback was taken through an e-forum set up on the government website to collect comments on a draft national strategy.

The key result of the overall project was the successful completion of a nationally owned EGOV vision and strategy agreed upon by most important stakeholders. The most critical points of the vision and strategy were to better respond to Afghan citizens’ expectations that e-government would bring convenient public services, transparency, accountability, and responsiveness and would help to deter widespread corruption. The project provided evidence that stakeholder engagement in national- level planning processes was possible, and that involving stakeholders can increase commitment, build consensus, and demonstrate transparency and openness in the strategic e-government planning process.

9.3.2 Renewable Energy Policy for Kosice, Slovakia

The process of developing an energy policy in Kosice self-governing region (KSR) in Slovakia is surrounded by political, economic, and environmental challenges. High dependency on imported energy from Russia and Ukraine, presented KSR with economic and political vulnerabilities. The emergence of domestic small to medium enterprises (SMEs) within the energy sector has provided new opportunities for employment and new technologies for utilizing local energy sources. Control of energy production with respect to emissions also impacted the policy-making environment. Any change in the sources of energy would likely affect the pricing of energy consumption and directly affect citizens and businesses. This case not only is a matter for policy makers and the authorities devising new energy policies but also affects the KSR government entities, energy importing companies, local SMEs, and citizens. Creating a new policy in such an environment required considerations of a wide variety of stakeholders; the goal was to ensure the new policy would be realistic, supported, and agreed upon.

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This case describes a pilot of the Open Collaboration for Policy Modeling (OCOPOMO) project.2 The main objective of the OCOPOMO project was to de- velop an online environment for, and information and communications technology (ICT) tools for, policy modeling in collaboration with stakeholders (Wimmer et al. 2012). Presenting complex information on policy choices for renewable energy re- quires some technical expertise and is influenced by individual beliefs. The pilot project in Kosice focused on capturing stakeholders’ views on alternative renewable sources of energy versus traditional energy production and consumption. It provided an understanding of various choices in relation to different policies for promoting the use of renewable energy, the perceived market potential for different energy sources, barriers hindering different kinds of energy generation in the region, and the moti- vating factors leading citizens and companies towards renewable energy sources. It also provided an early understanding of employment, financial, and environmental impacts of any potential policy (Furdík et al. 2010). This pilot was the first time that Kosice used advanced ICTs in policy making and the first time the region involved a range of stakeholders other than policy makers, experts, and key representatives from private heat producers and distribution companies.

The project team met with regional government committees and identified and analyzed relevant stakeholders ranging from heating producers to distribution compa- nies, building construction experts to technology experts, to household associations, citizens, and city employees. Desk research and surveys were used to identify the stakeholders, their roles, and expectations in the engagement process. The local au- thorities were mainly responsible for identifying the stakeholders. The project team and the local government applied action research to engage these stakeholders in the process and involvement was by invitation only. Several methods of engagement were used. Workshops were used to clarify tasks and expectations of stakeholders in the engagement process. Collaborative scenario development enabled stakeholders to provide evidence documents and to generate scenarios related to the policy problem. This method also allowed stakeholders to collaborate among themselves by exchang- ing views and concerns about the policy problem and possible solutions. Conceptual modeling transformed stakeholder-generated scenarios and evidences into formal policy models for simulation and then transformed the model-based scenarios into narrative scenarios to enable understanding of simulation results to stakeholders and steer further collaboration on the results. This process was iterative as new scenarios emerging from the discussions of results could be evaluated and simulated again.

The stakeholders first met with the project team and were given a tutorial of how the OCOPOMO online platform is used and they were free to use the platform for about 1 month. The online platform provided background and supporting materials to inform stakeholders of the different policy options available. After reviewing existing options, stakeholders could propose several scenarios—for example, they could propose a type of renewable energy and discuss what should be done from the stakeholder’s own perspective. Scenarios, based on these stakeholder proposals,

2 http://www.ocopomo.eu/in-a-nutshell/piloting-cases/kosice-self-governing-region-slovakia.

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were later turned into formal policy models for simulation. The consistent conceptual description (CCD) tool was used to perform this task.

The next phase began almost 1 year later with another face-to-face meeting to inform stakeholders of the purpose of the second iteration. Given the length of time between the first exercise and the second, some stakeholders were involved in the first face-to-face one but not the second, and some started in the second. In the second iteration, stakeholders were presented with simulation results of their policy choices. Additional background documents were provided to help educate them such as a return on investment (ROI) of different energy sources proposed. Stakeholders, particularly policy owners, provided comments on the model-based scenarios and then published one new evidence-based scenario. The topics which were most discussed leading to the new scenario included detailed technical pros and cons of a local versus central heating system, ROIs, legislation proposed by heat producers that would affect customers who decided to disconnect from the central heating system, and financial tools for investments in building renovation or installation of new heat sources.

The project was successful in highlighting the need for and usefulness of more innovative approaches to policy development processes. These innovative approaches proved to be particularly important with diverse stakeholders with different interests in an existing problem and potential solutions (Wimmer et al. 2012). The added value of OCOPOMO to traditional approaches is the added confidence for policy makers about the expected outcomes of a policy in respect to stakeholders involved. Moreover, the stakeholder engagement process in Kosice was positively viewed by the stakeholders themselves. It enabled better understanding of the policy problem through background documents provided in the platform, and it also provided a tool where different stakeholders’ views and expectations could be explicitly captured.

9.3.3 Redesigning the European Union’s Inspection Capability for International Trade

The European Union (EU) is implementing a risk-based approach (RBA) policy to government supervision of international trade lanes. As part of this approach, the risk posed by cargo entering and leaving the EU is analyzed on the basis of cargo information submitted electronically in a single declaration by operators prior to departure or arrival. However, this policy can only be effective if the data that circulate among the supply chain partners are accurate, timely, and of sufficient quality to be relied upon, which is currently not the case (Hesketh 2010). This case draws from two projects: Extended Single Window (ESW): Information Gateway to Europe, funded by the Dutch Institute for Advanced Logistics (DINALOG), and common assessment and analysis of risk in global supply chains (CASSANDRA), funded by the 7th Framework Program of the European Commission. The goal of both projects was to improve supply chain visibility.

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Transparency is important for both government and commercial interests; it re- lates to having access to the transaction data necessary to know what is actually happening in the supply chain. However, major challenges exist in today’s global supply chains, including lack of trust and understanding between public and private entities and among private entities (Klievink et al. 2012) about existing laws and ways of working among EU countries and other countries. Without the involvement of international trading businesses and other stakeholders, and without their active contribution to data sharing solutions that enable the RBA policy, the policy will not lead to the intended results for government and may lead to unnecessary increases in the administrative burden of legitimate traders.

To overcome these challenges, the project team assembled an international consor- tium of government bodies that included multiple European customs organizations, in addition to universities, IT providers, logistics operators, and standardization bod- ies. The project team conducted desk research and a survey based on Bryson (2004) to elicit stakeholders’ interests, urgency, influence, and importance. The total number of entities involved in international supply chains is so large that it was necessary to choose stakeholders that would reasonably represent the range of actors. Therefore, selection was based on criticality and representativeness. For example, the con- sortium involved representatives of a several very large and medium-sized freight forwarders. This was done to ensure different perspectives within this stakeholder group without having to involve the hundreds of parties that can be involved with the cargo on any single ship. Stakeholders were also grouped according to trade lanes. This approach limited the total number of actors by using the trade lane as a boundary. To ensure diversity in interests, ten different global trade lanes were modeled, in- cluding lanes between Shenzhen (China) and Felixstowe (UK), Penang (Malaysia) and Rotterdam (the Netherlands), Alexandria (Egypt) and Barcelona (Spain), and Bremerhaven (Germany) and Charleston (USA). Using this method, the stakehold- ers were able to see the common themes across trade lanes that are important for each of the key stakeholder groups.

In order to engage stakeholders to innovate within a real-life setting, a living lab approach was used. Tan et al. (2011) describe a living lab methodology as bringing together multiple stakeholders, across multiple locations, and seeing stakeholders as co-innovators. A living lab methodology is suitable for situations where a neutral party, often academics, acts as honest brokers to bring the different stakeholders to consensus. Each living lab group used real trade lanes to model the physical flow of data, information system landscape, and administrative burden in order to config- ure, demonstrate, and refine the entire system with the stakeholders. The consortium team created visual models and data-flow diagrams of the existing and to-be situa- tions to enable the stakeholders to sort out the policy and data-sharing issues among themselves. Another goal was for stakeholders to come to common understanding of their respective situations, ultimately joining up different systems of different stake- holders in order to capture the data they collectively needed. The overall dataset was visualized in a dashboard with role-based access. The dashboard enabled discussion of how the system would impact the day-to-day processes of the various businesses and inspection authorities.

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Involving stakeholders early helped increase commitment and consensus to this initiative. However, decision making remained relatively slow due to the consider- able time it takes to design technical tools, models, and diagrams, and to constantly update them to reflect the feedback from stakeholders’advice and recommendations. By providing a comprehensive overview of the roles, the data sources, and the work processes using them, parties came to an understanding of how the innovations were used. Through this, they over time build trust towards those potential vulnerabilities that the innovation might bring, would not be exploited. This facilitated acceptance and uptake by the various stakeholder groups. In addition, not all of the potential answers the living lab groups provided are also enabled by existing European legis- lation. Alignment between the business stakeholder groups, national governments, and European bodies is still needed. One of the outcomes of the project is therefore a consensus-based agenda for further policy development.

9.3.4 Understanding Child Health Outcomes in New Zealand

The next case examines the Modelling the Early Life-Course (MEL-C) project in New Zealand, which was supported by a public good research grant provided to researchers at the University of Auckland, New Zealand (Milne et al. 2014). Life- course studies examine “the biological, behavioral and psychosocial pathways that operate across an individual’s life course, as well as across generations, to influence the development of chronic diseases” (Ben-Shlomo and Kuh 2002). An abundance of research evidence can be found about the early life course of children and the determinants of health. The goal of the project was to develop a decision support software tool for policy makers to test different policy scenarios against realistic data and to consider this evidence alongside other policy-relevant information such as politics, other evaluations, or expert consultations. The main purpose was not to develop a specific policy but to develop a process and tool for better identification and use of data in this policy domain.

In an environment where a great deal of information about a policy exists, the tool is meant to help bridge the research–policy translation gap (Milne et al. 2014). The lack of research evidence uptake by policy makers is well documented (Lomas 2007; Van Egmond et al. 2011). One main factor is the lack of uptake in the “translation gap”—characterized as the mismatch between the knowledge that research produces and the knowledge that policy makers want (Milne et al. 2014). Milne et al. (2014) identify two solutions to bridge the gap—knowledge brokers (Frost et al. 2012; Knight and Lightowler 2010; Lomas 2007) and research–policy partnerships (Best and Holmes 2010; Van Egmond et al. 2011). Knowledge brokers act as translators, turning the research evidence into information that is easily understood and usable by policy makers. Research–policy partnerships involve a more intense interaction between both groups, where they work together to produce the evidence needed for policy purposes. Previous work focused on database interventions aimed at knowl- edge translation where all relevant documents synthesizing research results could be

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found (Milne et al. 2014). However, with the online databases the onus is still on policy makers to search for relevant papers, assess their content for relevance, and evaluate their importance for the policy question under consideration. The MEL-C project took a different approach with a decision-support tool “where the evidence is embedded in a working model and can be interrogated to address specific policy questions” (p. 8).

Using a micro-simulation model, the tool incorporates longitudinal data to determine the normal transition of children through their life course and the im- pact of policy interventions on their outcomes. Two representatives each from four New Zealand government ministries—Health, Education, Justice, and Social Development—formed a “policy reference group” for the project (Milne et al. 2014). The representatives were selected because they represented people who could under- stand the aims of the project and were data and technology savvy. Thus, the boundary for engagement was limited to the translation gap, and did not extend to the behavior of the children modeled within the system. The main strategy for involving policy makers was to hold regular, face-to-face meetings for almost 2 years to discuss the development of the MEL-C tool, including the simulation model and graphical user interface. The discussions were facilitated and documented by the task leader for end-user engagement.

The simulation model was shown to stakeholders who then provided feedback and became collaborators in the development of user interfaces and the types of key policy questions that the model needed to be able to address. The results of this specialized form of stakeholder engagement included a much more useful decision-support tool than might have been developed otherwise, an ongoing process of collaborative refinement, and a set of potential users and advocates for the tool.

Results of the model are beginning to be explored. For example, for child health service use outcomes it was found that appreciable improvement was only effected by modifying multiple determinants; structural determinants (e.g., ethnicity, family structure) were relatively more important than intermediary determinants (e.g., over- crowding, parental smoking) as potential policy levers; there was a social gradient of effect; and interventions bestowed the greatest benefit to the most disadvantaged groups with a corresponding reduction in disparities between the worst-off and the best-off (Lay-Yee et al. 2014).

9.3.5 Transportation and Urban Planning Indicator Development in the USA

Understanding how choices today will impact life in the future is a major concern for policy making in any area. In transportation and urban planning, it is even more important because the infrastructure created is not easily changed, once roads and buildings are built and patterns of living start to evolve around them. The urban plan- ning context is fraught with different stakeholders who often have fundamentally opposing beliefs and value systems (Pace 1990; Borning et al. 2005). They embody

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widely divergent opinions regarding urban development and land use. Each stake- holder group is likely to have their own philosophies about different forms of land use in urban environments, and different views about how long-term planning should occur, what situations constitute problematic conditions, what solutions should be sought for those problems, and what constitutes successful outcomes.

Under these contentious conditions, advanced computer simulation tools that show the long-term potential effects of different choices can contribute to legitimation of the policy process as well as to well-considered decisions. However, in order to achieve this, the model itself must be considered legitimate. In other words, its structure, inputs, processes, and outputs must be transparent and understandable to all stakeholders. Our last case, UrbanSim, is a land-use modeling system, developed over the past 20 years, that helps policy makers and stakeholders understand the 20–30-year impacts of different choices regarding land use and transportation on community outcomes including effects on the economy and the environment. It has been used widely in the USA and Europe and is of growing interest globally. The system not only estimates the direct effects of different infrastructure and policy choices but also estimates how individual and group responses to those choices will affect the outcomes (Borning et al. 2005; Borning et al. 2008).

UrbanSim simulation results are mainly presented to users as indicators. These indicators are variables that convey information about an attribute of the system at a given time. Indicators in UrbanSim include such variables as the population density in different neighborhoods, the ratio of car trips to bus trips for the region, and the projected cost of land per acre in different parts of the region. These and other indicators are presented under different possible scenarios over the course of the full simulation, generally 30 years. Indicator values are presented in tables, graphs, charts, or maps (Friedman et al. 2008). These indicators allow stakeholders to assess and compare the results of different policy scenarios on a consistent set of dimensions. For example, if a city has the goal of supporting more walkable densely populated urban neighborhoods as an alternative to sprawl surrounding the city center, then changes in the “population density” indicator in different neighborhoods could be used to assess the simulated outcomes of different policies over time (Borning et al. 2005).

In recent years, enhancements to UrbanSim have concentrated on making the model more realistic and meaningful to stakeholders by expanding, categorizing, and differentiating the stakeholder values represented by the indicators. The Urban- Sim team had two goals: to make advocacy for different views more explicit and contextualized, and to improve the overall legitimacy of the system by incorporating these values in a wider range of indicators in the simulations. The involvement of stakeholders, essentially a process of codevelopment of the model, was guided by an overarching theory of value sensitive design (Friedman 1997). A key feature of value sensitive design is designing technology that accounts for human values with an emphasis on representing direct and indirect stakeholders (Borning et al. 2005).

The UrbanSim team partnered with three local organizations in the Seattle, Wash- ington, region to develop and test new ways of expressing their values to model users through the choice of indicators and related technical information. The partners (a

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government agency, a business association, and an environmental group) were se- lected to represent a range of known issues and stakeholder views about development in the region. The goal was to create for each group a narrative value indicator per- spective that explained the values of most importance to that group and to select, define, and incorporate key indicators representing those views in the model. Stake- holders were convened in separate groups so that they could work independently to formulate their indicator perspectives. This was an important design choice because the goal was to present each group’s values and desires by essentially telling a story advocating particular values and criteria for evaluating policy outcomes (Borning et al 2005). The team engaged each stakeholder group through a series of face- to-face meetings and semi-structured interviews to help them craft and write both narratives and descriptions of indicators that closely matched their core values and views.

To assess the extent to which these approaches enhanced the legitimation of the model, a separate group of citizen evaluators reviewed each grouping of stakeholder- selected indicators and along with associated technical documentation as well as the indicators in the system as a whole. They considered coherence, informativeness, usefulness for supporting diverse opinions, usefulness for advocating for differing views and values, and usefulness for supporting the democratic process. The evalu- ation showed positive scores on all measures and also produced additional findings about the usefulness of different kinds of information (technical compared to advo- cacy), the importance of explicitly presenting and balancing diverse views, and the overall perception of transparency and lack of bias in the modeling system itself.

9.4 Case Comparison

Table 9.1 presents key elements of each case story based on the following points of comparison: (a) situation and approach, (b) types of stakeholders and type of participation, (c) methods for stakeholder identification, (d) methods for stakeholder engagement, (e) tools and technologies used, and (f) results.

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ne th

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st em

to w

ar ds

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in te

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m od

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nt en

tio us

po lic

y ar

ea s

[email protected]

194 N. Helbig et al.

Ta bl

e 9.

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ue d)

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ro -s

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at io

n m

od el

in g

Si m

ul at

io n

m od

el

[email protected]

9 Stakeholder Engagement in Policy Development 195

Ta bl

e 9.

1 (c

on tin

ue d)

C as

e 1

C as

e 2

C as

e 3

C as

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C as

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R es

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/o ut

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es of

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pr oc

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itm en

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d co

ns en

su s

am on

g ke

y st

ak eh

ol de

rs

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st ak

eh ol

de r

en ga

ge m

en tp

ro ce

ss w

as pe

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am on

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rs as

a us

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po rt

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ro ce

ss in

po lic

y an

al ys

is

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di ca

te d

gr ou

p in

no va

tio n

se tti

ng en

ab le

d th

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rs to

be tte

r un

de rs

ta nd

th e

ne ed

s be

tw ee

n th

em ,

w hi

ch en

ab le

s “t

ru st

” an

d pr

op ag

at e

so lu

tio ns

th at

w er

e no

tp os

si bl

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ye ar

ag o

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en ga

ge m

en t

fa ci

lit at

ed th

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to f

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up po

rt to

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r po

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m ak

in g

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am ew

or k

an d

te m

pl at

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r de

fin in

g, pr

es en

tin g,

an d

in co

rp or

at in

g va

lu e-

ba se

d in

di ca

to rs

in th

e m

od el

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ea se

d tr

an sp

ar en

cy an

d op

en ne

ss of

th e

st ra

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c pl

an ni

ng pr

oc es

s

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ag em

en te

na bl

ed un

de rs

ta nd

in g

of th

e po

lic y

ca se

am on

g st

ak eh

ol de

rs ,a

nd th

e to

ol fa

ci lit

at ed

th e

sh ar

in g

of vi

ew s

to su

pp or

ts ta

ke ho

ld er

s’ vi

ew s

in a

ne w

po lic

y

M ak

in g

st ak

eh ol

de rs

pa rt

of th

e fa

ct -fi

nd in

g an

d so

lu tio

n- de

ve lo

pm en

t pr

oc es

s su

pp or

te d

co m

m itm

en to

f st

ak eh

ol de

rs to

th e

so lu

tio n

T hi

s en

ga ge

m en

ta ls

o es

ta bl

is he

d a

gr ou

p w

ho w

er e

ab le

to be

ea rl

y ad

op te

rs of

th e

de ci

si on

-s up

po rt

to ol

, an

d w

ho ar

e ab

le to

ad vo

ca te

fo r

it

A m

et ho

d th

at al

lo w

s di

ff er

en ts

ta ke

ho ld

er s

to ad

vo ca

te fo

r di

ff er

en t

va lu

es ,b

ut fo

r al

l st

ak eh

ol de

rs to

vi ew

th e

im pl

ic at

io ns

of th

os e

va lu

es in

a st

an da

rd se

t of

ag re

ed -u

po n

in di

ca to

rs th

at m

ea su

re th

ei r

lo ng

-t er

m ef

fe ct

s

Jo in

tp ro

ce ss

su pp

or ts

co ns

en su

s am

on g

st ak

eh ol

de rs

(a tl

ea st

in th

e sa

m e

tr ad

e la

ne )

[email protected]

196 N. Helbig et al.

9.5 Discussion

In this section, we return to our two guiding questions: What types of engagement tools and processes are useful for different stakeholders and contexts? And what factors support the effective use of particular tools and technologies toward con- structive outcomes? The extant literature reveals a rich history of examining the role of participation in democratic theory and complex governance (Fung 2006; Fung et al. 2007). Various analytical tools in the literature address participant selection, modes of communication, and involvement and many of these were present in the cases. The cases confirm previous research regarding the importance of stakeholders and the need for careful and goal-oriented stakeholder selection and engagement. The cases also demonstrate the importance of support and education for participants and the role of trusted facilitators, contributing to the knowledge in this field. This section presents the key findings of our case comparison.

Identifying and Representing Relevant Stakeholders New governance means bring- ing in stakeholders who are not traditionally part of the policy-making process. Fung (2006) describes a continuum of types of stakeholders in new governance, including state representatives (described as expert administrators or elected representatives) and mini-publics (described as professional and lay stakeholders with organized interests). Professionals are paid participants (such as lobbyists) or not-for-profit organizations. Lay stakeholders are those who volunteer their services such as in- dividuals serving on school councils or neighborhood associations. The cases show that effective stakeholder engagement requires a nuanced understanding of who are the relevant stakeholders with respect to the specific goal of the engagement. Each case represents a complex policy area where the different stakeholders selected or invited to engage in the policy process represented particular aspects or viewpoints about a complex problem. Our study confirms that stakeholder analysis helps pol- icy makers understand differences in stakeholder behavior, intentions, preferences, interrelations, and interests. It also helps them assess the influence and resources different stakeholders bring to decision-making or implementation processes (Var- vasovszky and Brugha 2000). We found that ordinary citizens were seldom involved in these cases. Despite the common rhetoric of “citizen” participation, the cases show how it is often impractical to engage members of the public or representatives of the full range of relevant stakeholders. In these situations, policy modelers and policy makers needed to appreciate the limitations of stakeholder engagement and aim for results that take advantage of less-than-complete stakeholder participation.

For example, in the UrbanSim case, only three organizations participated in the codevelopment of new indicators. The modelers did not treat these stakeholder views as complete or definitive but rather they used this limited experience to create a value-based indicator framework to guide further development of new indicators and future applications of the UrbanSim model. In the international trade case, the main stakeholder groups were each represented by up to four “exemplary” actors. In this way, the key positions of these groups were reasonably well represented in the various activities in the project. These representative actors also served as a

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9 Stakeholder Engagement in Policy Development 197

starting point to identify specific trade lanes where innovations could take place, and thereby also created awareness of other stakeholders that play a role in those trade lanes. In the Kosice energy policy case, stakeholder identification was done using a technique similar to that proposed by Bryson (2004). The local government was mainly responsible for identifying relevant stakeholders that were invited to the engagement process. Other complementary techniques such as surveys were used to assess stakeholders’ roles and expectations. In the international trade case, similar techniques were applied.

Providing for Participant Support and Education In order to participate in mean- ingful ways, stakeholders in our cases needed to be educated regarding the purpose of the engagement, the processes and tools to be used, and the ways in which stake- holder input would be considered. For all the cases presented, stakeholders, including those that were often not directly involved in policy making (e.g., citizens, smaller companies), were made aware of the policy problem in some depth, presented with opportunities to deliberate the different policy choices, and presented with the in- formation necessary to understand the expected outcome from implementation of different policy options.

In the case of EGOV Afghanistan, stakeholders were provided with the results of an EGOV readiness assessment exercise for them to understand the crucial prob- lems to be solved through the implementation of a national e-government policy. Workshops offered them general knowledge about approaches and methodologies for strategy development. In Kosice, participants were provided with the energy pol- icy problem and background documents for additional information about the policy such as the energy conceptions proposed for various cities in the region and studies of ROI for various combinations of heat energy sources. The descriptive scenarios and background documents were important for stakeholders to understand the policy issue, its boundaries, and its challenges. In UrbanSim, the stakeholders were guided through the process of creating narrative value statements as well as ways to describe and document indicators in accurate, neutral language. All of these education and support activities made the stakeholders’ deliberations and input more usable and more relevant to the problem at hand.

Using Stakeholder Engagement Methods to Reveal and Explain Complex Policy Problems and Contexts Our cases illustrated that stakeholder engagement is an im- portant process in policy development as evidenced in the literature reviewed in Sect. 9.2.3. Engagement helped in all cases to assure that policy processes and pol- icy decisions were well grounded and responsive to both social values and practical needs. Action research and living labs helped assure that involvement was not based on an oversimplified view of the policy problem, Different tools acted as boundary objects to facilitate knowledge sharing, consensus building, listening, and negotiat- ing. Models of many kinds were used to break down complex processes and revise mental models.

In very intractable public problems like trade lanes, in order to understand how various actors would be affected by different policy options, it was important to un- derstand how information flowed between actors. The specificity of the models used,

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198 N. Helbig et al.

as well as their comprehensiveness in representing the actual situation, facilitated a focused debate between businesses and government agencies, forcing each party to be clear about their precise activities and relevant policy concerns. As a result, no stakeholder could hide behind a policy that allegedly forced or blocked a certain so- lution, and the consensus process could focus on the policy options that were feasible in practice. The Kosice energy policy problem required a balance of diverse interests of stakeholders both supplying and consuming energy. This presented policy mak- ers with challenges in identifying and engaging those interests that will affect the implementation of the new policy. Collaborative scenario building engaged both cat- egories of stakeholders. This method was particularly important for policy makers to increase the level of certainty of the policy choice by understanding the intersecting interests of these stakeholders. Formal policy modeling and simulation were also important to inform all stakeholders and policy makers of the different possible out- comes of their scenarios. In the child health case, stakeholders were educated about the concepts and assumptions underlying the policy-modeling tool being developed. They also learned from each other about the policy questions of greatest importance to child health and development. The methods used in these cases are similar to those identified in literature (Andersen et al. 2007; Vennix et al. 1996) and can be employed to contribute to many different policy development efforts.

Using Trusted Third Parties to Enhance Transparency of the Process and Improve the Tools of Engagement Negotiating, brokering, and collaboration skills and exper- tise with engagement tools are all essential for achieving new forms of governance (Bingham et al. 2005). The tools and technologies used in our cases have different characteristics that affect choice and suitability, including available expertise and fi- nancial resources, level of participation, type of policy problem, and the geographic location or dispersion of stakeholders. The cases also address a factor that is less often critically addressed, namely the ways that “trusted” third parties, such as re- searchers, are used in stakeholder engagement. In these situations, researchers were not only doing academic research on engagement but also crafting, testing, and im- proving meaningful tools toward practical outcomes. As “brokers” in the process, researchers and the tools and technologies they use can inhibit or promote better models of engagement in policy making and governance.

In the case of EGOV Afghanistan, the use of online surveys by the UNU-IIST team solved the issue of trying to reach a distributed set of stakeholders separated by geography and also provided a confidential way to gather information about stake- holder interests, while the stakeholder analysis tool provided by UNU-IIST helped MCIT to understand stakeholder preferences and concerns and to assess their po- tential to influence the policy process. The technology tools used were not intended to “socialize” the interests of stakeholders but to gather intelligence by a trusted third party that could be used in the strategic planning process. By comparison, the intention of the online OCOPOMO platform used in the Kosice case was to bring the stakeholders themselves into a virtual meeting place where they could see the interests of other stakeholders. This technology choice, implemented by expert re- searchers, was intended to facilitate knowledge sharing in a multidirectional way.

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9 Stakeholder Engagement in Policy Development 199

In the UrbanSim case, the stakeholders’ values and interests were intentionally de- veloped in isolation from one another because the goal was to represent the distinct values of each stakeholder type within the model. The simulation mechanism, built by the academic experts, could then model and report indicators showing how these different interests might interact over time. In the international trade case, a neutral party designed the modeling approach and helped the stakeholder groups in each trade lane model their own existing situations. This approach facilitated joint prob- lem identification and solution development. In the New Zealand child health case, researchers helped policy makers discover policy-relevant material while the pol- icy makers helped the researchers understand what formats and other factors made that material relevant and usable. Each example demonstrates the role of trusted, independent experts who can select technology options, tools, and techniques that introduce transparency into the process and are technically and practically suitable to the situation. The researchers/modelers were trusted independent brokers who gathered data, facilitated engagement, and built models or systems to transparently reflect the reality of the stakeholders.

9.6 Conclusion

All of the cases we reviewed above used an active approach, assisted by third-party experts, to bring stakeholders together in workshops, through a collaboration plat- form, or in living labs to support interaction in problem identification, codevelopment of solutions, and foundations for gaining commitment or consensus by different types of stakeholders. These experiences go well beyond eliciting stakeholders’ positions and requirements, leaving the interpretation and balancing to be done by the policy maker independently. The approaches used in these cases supported the stakeholders directly in gaining a shared understanding of the problem, providing some insight into the position and reasoning of other stakeholders, laying the groundwork for potential negotiation or other ways to find common ground with respect to the policy issue, and in some cases establishing or reinforcing trust among different stakeholders as well as trust in the participation process. In line with the literature on this topic, the cases also illustrate some of the cautions and limitations of stakeholder engagement, with particular emphasis on the realistic limits of involvement and representation, and the consequent necessity to match stakeholder selection and engagement methods to a well-defined goal within the larger policy process.

We find that a careful identification of stakeholders is required, and the selec- tion depends on the goals of engaging stakeholders. The appropriate selection of stakeholders to involve can evolve over time, the identification and engagement of stakeholders is a continuous process, as Bryson (2004) suggests. To illustrate this in one of the cases, in the international trade case, the process started with a set of stake- holders needed to identify and initiate the demonstration trade lanes. These provided grounds for further identifying other stakeholders that play a role in those trade lanes or that were relevant to the initial set of stakeholders. These needed to be engaged

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200 N. Helbig et al.

also in order to meet the goals of engaging stakeholders. The goals themselves can also evolve along the changing stakeholder involvement. In this case, especially in the beginning, stakeholders were involved to elicit their views and interests in the matter, whereas during the process this shifted toward engaging stakeholders to en- sure commitment and to facilitate building consensus among the stakeholders. There are similarities among the cases such as the use of surveys and convenience sam- pling as methods to identify stakeholders, face-to-face meetings, and workshops as methods of engagement and use of modeling techniques as tools and technologies. Although the literature provides various available methods and techniques used in stakeholder engagement processes, the cases illustrate that the approaches, tools, and technologies selected in each case are highly influenced by the purposes and expected outcomes of the engagement effort. Therefore, we emphasize that every stakeholder engagement needs to be tailored with well-selected processes and tools that suit the overall purpose and expected outcomes.

As frequently highlighted in the literature, stakeholders’ involvement in policy processes can help build consensus by balancing stakeholder interests and pref- erences, increasing their commitment for policy implementation, and ensuring transparency and openness of the process. Often, these advantages of stakeholder engagement are linked to the idea of empowering stakeholders as much as possi- ble (i.e., stakeholders make key decisions). However, our study shows that all of these advantages can also be gained by involving stakeholders, with less emphasis on empowerment. We posit that these benefits can be realized when stakeholders understand their roles and the objectives of their engagement, enabling them to bring their own interests to the table while also gaining an understanding of other interests and factors that influence decisions and results. Therefore, our findings on the im- portance of offering support and education for participants in order to enable them to understand their role and the engagement process are an important contribution to the literature. In a similar vein, the role that trusted (third-party) facilitators could play in the engagement process is often underestimated in the literature, but is clearly an important ingredient in the cases presented in this chapter.

Tools can take many different forms, some using technology and some not—the important factor is to match the tool to the objective and the capabilities of the stake- holders involved. Making this match requires an understanding of the capabilities of the stakeholders to use such tools and technologies, sometimes also in a spe- cific country context. Furthermore, as the UrbanSim and child health case shows, stakeholders can not only contribute to policy analysis and choices but also make significant contributions to improving the effectiveness of policy processes, and the validity and usability of models, and other tools.

Based on these findings, our study offers some practical insights for policy mak- ers (and researchers) that want to engage stakeholders for policy development. The first critical step is identification of salient stakeholders or stakeholder types. The literature reviewed in this chapter as well as the five cases offer various approaches to identify stakeholders. As concluded above, the method used to identify stake- holders is closely related to the intended purpose of stakeholder engagement. For

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9 Stakeholder Engagement in Policy Development 201

example, when aiming to learn from stakeholders about a specific domain, a con- venience sample of relevant actors is a suitable method. However, if the goal is to ensure commitment or to build consensus, the methods employed need to be rigor- ous in identifying all key stakeholder groups. Desk research, surveys, interviews, and stakeholder or interests mapping tools are useful approaches to do this. Iterative stakeholder identification often helps create a more complete array of relevant stake- holders. Our research in combination with the relevant literature also shows other purposes for stakeholder engagement that guide the selection of stakeholder types. For example, transparency of the process, facilitating adoption, improving useful- ness and usability of tools, and enhancing legitimacy are purposes of stakeholder engagement we found in the cases.

Once the relevant stakeholders have been identified and the objective of involving them is clear, the approach to stakeholder engagement needs to be selected. Whereas the literature presents various options, all the cases we covered were in an advanced stage and almost all employed some form of action research, in which stakeholders (especially practitioners and policy makers) worked closely with each other and with researchers in a collaborative way. This was found in all cases, as all cases were fo- cused on involving stakeholders. In case the objective is to primarily inform or consult stakeholders, other approaches are more suitable, and some suggestions have been provided in the background section. When involving stakeholders, policy makers and researchers will have to carefully consider what role the engaged stakeholders will have; involving stakeholders to work in real-world complexity as much as pos- sible will benefit from action research or living labs, but requires that the material, objectives, activities, etc. be carefully prepared and designed, as stakeholders do not always have a clear idea of what their involvement should look like or contribute to. On the other hand, complexity can also be broken down to make the matter more comprehensible for stakeholders. For this, modeling tools and simulations can be used for both purposes. In either case, tools and models can function as boundary objects that stakeholders can view, discuss, or manipulate to better understand how a particular decision might play out. However, the conceptual capacity stakehold- ers that will need to have affects the kind and amount of work that should go into preparing the engagement.

While much remains to be learned about stakeholder engagement in policy mod- eling, this chapter provides a starting point for better understanding how different approaches, tools, and technologies can support effective stakeholder participation toward better policy choices and outcomes. The cases presented here demonstrate that stakeholder engagement processes, tools, and technologies are versatile and use- ful to both policy makers and the stakeholders themselves. With careful selection and application, they can work in a wide variety of situations including different policy domains and kinds of problems, different political systems, and different levels of social and economic development.

Acknowledgment This comparison and analysis was conducted as a collaborative activity of the eGovPoliNet Project, funded through the European Commission Framework 7 Program as agreement FP7-ICT-2011–288136, and supported by US National Science Foundation (NSF) grant

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202 N. Helbig et al.

IIS-0540069 to explore policy modeling and governance through an international consortium of research institutions. Ideas and opinions expressed by the authors do not necessarily represent those of all eGovPoliNet partners.

We also gratefully acknowledge the information and reference material provided by Peter Davis and Barry Milne of the COMPASS Center at the University of Auckland regarding the New Zealand case, and Alan Borning at the University of Washington regarding the UrbanSim case.

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Chapter 10 Values in Computational Models Revalued The Influence of Designing Computational Models on Public Decision-Making Processes

Rebecca Moody and Lasse Gerrits

Abstract This chapter aims to add to the technology debate in the sense that it aims to research the role of values and trust in computational models in the policy process. Six case studies in which a computational model was used within a complex policy context were research for the role values play within these models. Conclusions deal with the role of the designer of the model, the number of different actors, the amount of trust already present, and the question of agency by humans or technology. Additionally, margins of error within the model are discussed as well as authority by one actor over others concerning the model.

10.1 Introduction

Policy makers are tasked with making decisions on issues characterized as wicked problems because of controversies, unknown relationships between causes and con- sequences, and (consequently) uncertain futures. From this perspective, it would be desirable to map the decisions and their possible outcomes prior to the actual deci- sion making because that would generate certainty in ambiguous situations. Broadly speaking, this provides the motive for using computational modeling for policy making as expressed in, e.g., policy informatics. Although there are computational models that are ready off-the-shelf, it is more common to work with models “mod- ded off-the-shelf” (MOTS) or even tailor-made models to suit specific questions and conditions. As such, the model itself becomes part of the decision-making process during the acquisition.

We observe that this phase, during which scope, functionality, and deployment are determined by commissioning actors and designers, is essential to the way the models influence policy making. Although it may be assumed that such models are neutral or value-free, they are not because of the changes that designer and client

R. Moody (�) · L. Gerrits Department of Public Administration, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands e-mail:[email protected]

L. Gerrits e-mail:[email protected]

© Springer International Publishing Switzerland 2015 205 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_10

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206 R. Moody and L. Gerrits

introduce to the original model. This chapter aims to shed light on the relationships between computational models and policy making by looking at the role values play in commissioning, designing, and using such models in policy making. We will rely on the notions set forward in the technology debate in order to understand the way technical design can be perceived by actors and used in policy making. These notions will also be used in our analysis in order to understand the way actors within the policy process reach conclusions based on the models. We will primarily look at the perception of the models in terms of values on which we will elaborate below. We carried out a secondary analysis of case study data we collected for other research (Gerrits 2008; Moody 2010). The case studies concern: (1) predicting effects of deepening operations in rivers in Belgium and the Netherlands and (2) in Germany; (3) determining flood risk prediction in Germany and the Netherlands; (4) determining the implementation of congestion charging in the UK; (5) predicting and containing the outbreak of live stock diseases in Germany; (6) predicting particular matter concentrations in the Netherlands. The chapter is structured as follows. We will first discuss the theoretical background of our analysis by looking at autonomy of technology and technology as being deterministic, blending notions from the technology debate with notions from public administration and public policy in Sect. 10.2. The methodological approach is discussed in Sect. 10.3, the case studies in Sect. 10.4, the analysis in Sect. 10.5, and the conclusions in Sect. 10.6.

10.2 Technological Perceptions: The Debate

To understand the implications of the design of computational models it is necessary to understand the underlying assumptions of the design process. The way modelers design different models can be viewed from different viewpoints as pointed out in the technology debate. This is an ongoing debate in philosophy of science as well as in sociology and technical studies. The technology debate revolves around technology and humans, technology and society, and technology itself. It reflects on questions of who drives technology: Are humans the drivers of technology or does technology drive humans? Does technology possess any values of its own and are these values given to technology by humans or does technology have no values whatsoever and is it completely neutral? What is the relationship between technology and society, does technology constitute society or is it the other way around?

A large number of authors have described the technology debate and placed their opinion (see: Smith and Marx 1994; Scharff and Dusek 2003; Kaplan 2004). In the technology debate, several issues are discussed. A central issue is who masters the other, do humans master over technology, or does technology control humans? An- other key theme is the question whether technology is autonomous and determines its own causality. Another key feature is whether technology incorporates values or should be seen as neutral. Finally, the relationship between technology and society

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10 Values in Computational Models Revalued 207

is important—which drives the other? A number of standpoints within the technol- ogy debate can be identified. For the sake of briefness, we will only look at social constructivism and technological determinism and technological instrumentalism.

Within technological determinism, it is believed that technology is not neutral or value-free. Technology can be good or bad or a mixture of both—this goes for effects as well as consequences. These consequences may not be dependent on the desired goal but are dependent on the technology. Technological development, therefore, does not depend primarily on the intention of the user but is fixed within the technology itself, it is inevitable and cannot be steered or controlled by humans. Agency here is not given to the human user but is attributed to technology. It is argued that certain political and social norms and values are hidden inside the technology. Therefore, the technology will bring about consequences according to these norms and values (Ellul 1954, 1990; Zuboff 1988; Heilbroner 1967, 1994; Winner 1977, 1980, 1983, 1993).

In social construction of technology, the viewpoint held is that choices need to be made in the design and the direction of technology. Economy, society, institutions, and culture shape the direction and scope of technological development, the form of technology, the practice, and the outcome of technological change. Agency in this approach is given back to humans. Technology is neither seen as autonomous nor does it have a fixed outcome with inevitable consequences. All technology is seen as a human construct and is thus shaped, or made by humans (Bijker 1993, 1995; Hoff 2000).

What is very important in understanding the approach of social construction of technology is the technological frame. This technological frame consists of goals, problems, problem-solving strategies, requirements to be met by problem solutions, current theories, tacit knowledge, testing procedures, design methods and criteria, users practice perceived substitution function, and exemplary artifacts (Bijker 1995). The technological frame is thus the set of rules, ideas, and meanings within a group and it determines the interaction between the members of a group. This means the technological frame determines which meaning a group will attribute to a technology (Bijker 1995).

Within technological instrumentalism, technology is seen as a neutral and value- free tool. This means a number of things. Firstly, that the technology can be used to any end. Secondly, this means that technology is indifferent to politics. The technol- ogy can simply be used in any social or political context since it is not intertwined with any context. Thirdly, technology is viewed as being rational. It is based on causal propositions; it can therefore be transferred into any other context as well. Finally, technology is seen as universal, it stands under the same norm of efficiency in any and every context (Feenberg 1991). Within the approach of technological instrumental- ism, technology is not attributed with any agency. This means that technology itself cannot account for any form of causality; humans cause this causality. Technological progress, therefore, is viewed as desired progress since it is the human actor who pursues it (Bekkers et al. 2005). Technology is developed and implemented with the purpose of achieving one’s goal and the technology serves as a means to achieve this goal.

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208 R. Moody and L. Gerrits

Authors within all positions agree, however, to the point that in computer technol- ogy it becomes very difficult to model the real world. Reality is composed of infinite variables and relationships that pose practical limits to what data can be processed. Computer models, their designers, and users all are bound in the degree of ratio- nality they can display. Among others, Simon (1976, 1957), Dror (1968); Lindblom (1959), and March and Simon (1993) recognized that public decision makers limit the number of options they consider because of this cognitive limitation. While with computer models it is assumed by many that this bound in rationality can be lifted, it can also be argued that this is not necessarily the case (Moody 2010).

10.3 Technology and Public Decision Making

The argument above means that synoptic decision making where the model maps decision outcomes, to be followed up by the actual decision, its implementation and possible feedback, is too optimistic an approach. It assumes that a model would deliver (nonbiased) data, which is judged by decision makers to generate alternatives, of which the best alternative is chosen and consequently carried out (March and Simon 1993; Winner 1977; Beniger 1986; Goodhue et al. 1992; Chen 2005). It is then assumed that a computational model is a value-free tool that will provide a neutral oversight of all available alternatives with their consequences. Therefore, it is believed by some that these models will decrease the bounds in rationality that decision makers face and that public policy making will become a more rational process in which all consequences are foreseen prior to decision making (Ware 2000; Moody 2010; Beniger 1986; Goodhue et al. 1992).

This line of reasoning corresponds with the technological instrumentalist view- point. However, while public decision making is also a political process in practice, we see that computational models, next to not being able to include all variables needed for complete consequences, also suffer from limits on the side of political values. It must be noted that the designer of the model is not a neutral object either and becomes able to influence the model (Winner 1977; Chen 2005; Ware 2000; Wright 2008). Known margins of error can be manipulated toward political values and the necessary choice which needs to be made on which variables to include in the model is value-driven as well.

The question we need to ask ourselves here is not only whether computational models are a value-free or neutral tool, but moreover who or what determines the values within these models. Technological determinists would argue that values are inherent for the models themselves, and the outcomes of the model are fixed before use. Social constructivists would argue that the models would be attributed with value through a process of using the model. We want to take this reasoning a step further, without taking position in the debate, and look at who designs the model, who decides which variables should be put into the model, and which variables should be excluded. Who decides what the functionality of the model is—what is it able to do and what not—and how do policy makers react to this?

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10 Values in Computational Models Revalued 209

While the above demonstrates that the topic on values in a computational model is a very loaded and complex topic to begin with, we find that next to the complexity in the model itself in terms of values, the process of policy making deals with additional values and its own complicatedness. We have to also address the complexity in the actual decision-making process because of the multiple actors with diverging norms, beliefs, and interests. Following the previously stated and driving on outcomes and hypotheses of previous research (Gerrits 2008; Moody 2010) we find that in dealing with values in computational models and public policy, some core characteristics can be identified:

1. The values attributed to the data on which the computational model is based. These values can be subdivided into: a. The dominant ideas actors hold on these data, for example, are the data correct,

are they trustworthy? (Trust) b. The margin of error in the data and how this margin of error is communicated

to policy makers. Are they aware of the correct margin of error, do they under- stand what this implies, do they feel this is an acceptable margin? (Margins of error)

2. The values attributed to the model itself, this can be subdivided into: a. The organization that owns or commissions the model. Is there one organiza-

tion who owns the model, or are there clusters of organizations owning the model, if so, do they share the same values? (Ownership)

b. Perceptions and values toward the model itself by designers, owners, and other actors. Do they trust the model, do they feel the outcomes the model produces are correct? (Beliefs)

3. The values within the decision-making process, this can be subdivided into: a. Who is the organization which makes the final decision on policy? Are they

codependent on other organizations in order to be able to make the decision or do they have sole authority? (Authority)

b. Are there other actors involved in the policy making? These actors do not necessarily need to have the authority to make the decisions but are present in a policy arena or community affecting the decision or the reception of this decision. Are there many of such actors? (Multiactors)

In the analysis of the cases, these are the core characteristics to be analyzed.

10.4 Methodology

As mentioned above, we carried out a secondary analysis on original case studies by us. This was done to change the perspective of our original analysis, which dealt more with outcomes instead of process. The selected cases share three basic characteristics. Firstly, they all featured new tailor-made computational models that were deployed for the first time in the case. Secondly, all cases concern policy issues with the natural or built environment. Thirdly, all cases concern highly complex and controversial issues.

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210 R. Moody and L. Gerrits

10.5 Case Studies

The case studies are presented in this section. Each case has a brief introduction. The main characteristics are presented in Table 10.1. An overview of the stakeholders in each case can be found in Table 10.2.

Case 1: Morphological Predictions in the Westerschelde (Belgium and the Netherlands) The Westerschelde estuary runs from the Belgian port of Antwer- pen through the Netherlands before ending at the North Sea coast. The estuary has a limited depth and the Antwerpen port authorities were seeking ways to deepen the main channel in the estuary to facilitate larger ships and thus promote economic growth. However, the estuary is Dutch territory and the Dutch authorities are reluc- tant to facilitate the wishes of their Belgian counterparts. They regard the estuary as a fragile complex system that has a high ecological value and fear that the ecol- ogy could be destroyed by yet another deepening operation. The estuary consists of multiple channels through which the tide flows. Those channels are considered pivotal to the very specific and rare estuarine ecology of which very few remain across Europe. Negotiations starting in the early 2000s included the extensive use of computational models to assess the extent to which a deepening would harm the multichannel morphology of the riverbed and with that the ecological value. Re- search was jointly commissioned by the Dutch and Belgian authorities. A Dutch research institute, Deltares (formerly WL-Delft Hydraulics), was the main contrac- tor, with a small number of subcontractors. It deployed two computational models: Sobek, which is modified off-the-shelf; and Delft3d, which was a brand-new model and considered the more advanced but less tested model of the two. Both models were used to simulate the consequences of dredging operations. The results of Sobek seemed more robust but were considered relatively crude, while the results of Delft3d appeared more advanced but featured more model and outcome uncertainty.

Case 2: Morphological Predictions in the Unterelbe (Germany) Like the West- erschelde, the German Unterelbe is also an estuary that gives access to a major European seaport. It runs from the port of Hamburg through the federal states Niedersachsen and Schleswig–Holstein before flowing into the North Sea. Similar to the first case, the port authorities are seeking for a deepening of the main channel to facilitate larger ships. Such a deepening was carried out in the 1990s but had resulted in severe (partly) unforeseen and unwanted changes to the estuary that many people felt had harmed the ecological state. Here it appears as if the desire to deepen the estuary had influenced the outcomes of the computational model. The ensuing societal and political protests, from both nongovernmental organizations (NGOs) and the federal states except Hamburg itself, led to a different approach when considering a new deepening in early 2000. The Hamburg port authorities and the Hamburg Senate commissioned the research to the federal research institute Bunde- sanstalt für Wasserbau (BAW). This institute collected the relevant data and built its own model in-house to generate directions for dredging and ecological development.

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10 Values in Computational Models Revalued 211

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212 R. Moody and L. Gerrits

Table 10.2 Overview of relevant stakeholders per case

Case no. Actors involved, including societal stakeholders

1 Bureau Getijdenwateren, Havenbedrijf Antwerpen, Ministerie van Verkeer en Waterstaat, Office BeNeLux, Provincie Zeeland, Port of Antwerp Expert Team, ProSes, Rijksinstituut voor Kust en Zee, Rijkswaterstaat Directie Zeeland, Waterschap Zeeuwse Eilanden, WL Borgerhout, WL Delft Hydraulics, Zeeuwse Milieufederatie

2 ARGE-Elbe, BUND Hamburg, Bundesanstalt für Stadtentwicklung und Umwelt, Bundesanstalt für Wasserbau und Schifffahrt, Hamburg Hafen und Logistik AG, Hamburg Port Authority, Handelskammer Hamburg, Landkreis Stade, NABU Hamburg, Rettet die Elbe, Senat Hamburg

3 Ministerie van Verkeer en Waterstaat, Rijkswaterstaat, Taskforce Management Overstromingen, Municipalities Netherlands, Gemeenten Municipalities Germany, Waterboards, Citizens, Royal Haskoning, Provinces Germany

4 City of London, Transport for London, Citizens, National Alliance against Tolls

5 Friedrich Loeffler Institute, Bundeslanden, Universities, Veterinarians, Citizens

6 Provinces Netherlands, Municipalities Netherlands,Rijks Instituut Volksgezondheid en Milieu, Landelijk Meetnet Luchtkwaliteit, environmental organizations

Because of the belief that it should be seen as neutral in the controversial debate, BAW took the unusual decision to make its data and the model parameters available to any third party interested to replicate the results or to develop different models. Consequently, NGOs used the data to model their own particular version of the dredging works and their consequences. They arrived at different conclusions, which meant that the commissioning actors were obliged to engage in a dialogue about the future of the estuary. This slowed down and altered the original plans.

Case 3: Flood-Risk Prediction (Germany and the Netherlands) In the last 2 years, it was decided to run one application in the Netherlands and Germany with the goal of predicting and managing floods from rivers. Before this, applications and authorities were divided on the subject. The application named FLood Information and WArn- ing System (FLIWAS) was to integrate different applications and organizations to make sure water management and flood prediction could be done more efficiently. FLIWAS was developed and the application will predict on the basis of weather con- ditions, satellite data, past results, and the height of the water whether a flood will occur and what the damage would be in terms of economics, damage to landscape and lives. Also, the application is able to calculate proper evacuation routes. The implementation of the application has resulted in the water sector becoming more integrated and being able to communicate to policy makers what the result of certain actions are. It is now more the case than before that water management professionals are invited to the negotiation table in matters of urban planning, where they are able,

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10 Values in Computational Models Revalued 213

on the basis of predictions and scenario sketching to convince governments that some plans might not be wise.

Case 4: Determining the Implementation of Congestion Charging in London (UK) The city of London has had a large problem with congestion. In order to find a solution to this congestion problem the local government has come up with a plan to reduce congestion by imposing a charge on all vehicles that enter the zone in which the congestion is worst. A computational model was used to determine where this zone should be so the location of the zone would be most effective in not only reducing congestion but also gaining the government enough money to reinvest in public transportation and cycling facilities. On the basis of traffic data, alternative routes, and public transportation plans the organization Traffic for London had de- cided on a zone in which the measures are implemented. The application to do so finds its basis in scenario sketching so different alternatives of the location of the zone could be viewed with their effects.

Case 5: Predicting and Containing the Outbreak of Livestock Diseases (Germany) Due to European regulations and after the outbreak of mouth and foot disease in the 1990s which caused significant financial damage, the German govern- ment decided to centralize all information on contagious livestock diseases into one application, TSN (TierSeuchenNachrichten). The application holds information on farms and animals. Further, the application will make scenarios on how to contain and prevent outbreaks of contagious diseases. On the basis of the contagiousness of the disease, the estimated health of animals, natural borders, wind and weather conditions, and the location of farms, a decision can be taken on what measures to take. These measures include the killing of the animals, vaccination of the animals, or installing a buffer zone in which no traffic is allowed. The German government appointed the Friedrich Loeffler Institute with the task to develop and manage the application.

Case 6: Predicting Particular Matter Concentrations (the Netherlands) Particulate matter in recent years has become an issue more and more prone to attention. Due to European regulations, the countries in the EU are to make sure the concentration of particulate matter in the air does not exceed a set norm. Therefore, whether buildings and roads can be built becomes dependent on this norm, not only for the effect on air quality by the building process but also for the effect of the plans once in use. Applications have been made to predict the potential concentrations of particulate matter after implementation of building plans, the outcome of the prediction determines whether a building can be built. The problem in this case lies in the fact that the way to calculate particulate matter to begin with is unclear, scientists are not sure of the calculation as of now, the health effects are not clear as well, just like the prediction itself. Furthermore, other NGOs have made their own application to predict concentrations, in which mostly the outcome differs significantly from the applications local governments use. This causes each building process to be reevaluated for their legitimacy, and this causes a lot of distrust.

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214 R. Moody and L. Gerrits

10.6 Analysis

When analyzing our empirical data on the basis of the core characteristics earlier established we find a number of things. These will be discussed below.

Values in Data We have distinguished in trust and margin of error and there are some trends to be discovered in our six cases. First, we find that the trust and the reliance on the data in the model are large in the cases 1–4 and low in the cases 5 and 6. In order to explain this we must realize that this in fact has no causal relation to the data itself but to the cases in specific and the actors involved. What we find is that in cases 1–4 the actors involved in the cases share a common goal and common values, in the cases 5 and 6 there are several groups of actors with different goals. The trust an actor has in the data can, therefore, be explained by the trust and common values and goals he has with other actors. When there are different goals among actors or groups of actors, we find that the trust in the data itself in the model decreases. When relating this back to our theoretical notions we find that a determinist viewpoint would be difficult to hold since the trust in the data does not depend on the manner of collection of data or on the model itself but on the diversity of the goals and values of actors involved. It must be noted however, that each of the actors, both in the cases in which there is trust in the data as well as those cases without trust in the data themselves do hold a deterministic viewpoint. They feel that the data will lead to better solutions in cases 1–4 and in cases 5–6 they believe the data will only lead to a politically motivated outcome serving another actor.

When looking at the margins of error we find that the margins of error are relatively high in all cases. This can be explained by the large number of variables within the cases and their complex interrelation. In all cases, those actors involved acknowledge these errors but also realize that politicians want to hear a nominal “yes” or “no” answer. Therefore, these margins of errors disappear in the communication between the experts and policy makers as the experts simplify the presentation of their results. While the trust in the data and the reliance on the model is high in most of our cases, we still find a high margin of error which is only acknowledged by actors in cases in which the trust in the data is low. This shows us that the objective margin of error will only be perceived as a “problem” or an “issue that needs to be taken into account,” when there is little trust in data. Not only are these margins emphasized but also on the basis of these margins actors accuse each other of manipulation of the model and the data for their own political goal. Taking this into account it can be concluded that for both the trust in the data and the margins of error, the group of actors and their goals, are determinant for the course of the process. When actors agree on goals the trust in the data is high and margins of error are neither communicated, nor seen as a problem. When actors do not agree on the political goal, trust is low, the margins of error are emphasized, and the manipulability of the data is communicated very frequently.

Values in the Model When we look at the values in the model itself, we have distinguished between ownership and beliefs. In terms of ownership, it can be found

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10 Values in Computational Models Revalued 215

that most models, except for the case 6 are owned and developed by one organization. Therefore, it is the case that they have a monopoly on the information generated by the model, which grants them the power to use this monopoly in terms of decision making. Only in cases 2 and 6 we see that other actors use the data and the information to build their own model. In both cases, this has led to conflict. We also find that in case 5 some issues of ownership have occurred, but these issues were only addressed by actors not sharing the same political goal as the actors who owned the application, they were not granted access, while other actors who did agree with the political goals of the owner were granted access. What this leads us to conclude is that ownership by actors who share different political goals will lead to a conflictuous process of policy making. The manner of ownership or monopolization of a model by a (group of) actor(s) with the same political goal, therefore, does influence the outcome of the policy-making process as well as the process itself.

In terms of beliefs or trust in the model, we find the same results as for trust in the data, which would appear logical. The explanation of why some models are trusted and others are not, is the same explanation as for the trust in the data. In cases with actors with different goals, the trust in the model is generally low, the situation is conflictuous, and those opposed do not trust the model and accuse the owner of the model of distrustfulness, using the application for their own political, motivation, and manipulation of the model so their preferred outcome will prevail. Here as well we find that actors individually hold a fairly deterministic viewpoint regarding the model in question. Additionally, it shows us that data in the model and the model itself cannot be seen separate from each other in terms of trust.

Values in the Decision-Making Process When we look at the values of the decision- making process we have distinguished between authority and multiactor setting. We can find that in terms of authority an interesting situation exists. In some cases, cases 1, 4, and 5, there is a clear line of authority. In these cases, it is agreed upon who should provide the data, the model, and the results on the basis of which policy should be made. In most cases, this is institutionally arranged, by legally making one organization responsible. In some cases, this is arranged by a code of conduct in which all agree this to be the organization dealing with this topic. In the cases 2, 3, and 6, we find that there is no clear agreement on who holds authority. This can be explained by the idea that more than one organization is using the same data but reaches different policy conclusions based on this data which eroded authority (cases 2 and 6) or by monopolization issues, in which one organization used to hold authority over policy decisions but because of the emergence of the model and the monopolization of this information authority has become blurred (case 3). The lack of clear lines of authority accounts for a situation of conflict, different actors are trying to use the outcomes of the models for their own political goals. A very social constructivist situation in which technological frames of actors create a situation in which they believe the outcome of the model supports their claims, policy solutions, and goals.

A final factor is the number of actors and their relation with one another. Natu- rally, a number of different interests can be found in each case and a clear trend on

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this variable is not to be found, it seems to be rather case specific. In case 1, we see that there is a high number of actors involved in the decision-making process and that the diversity of the actors regarding their political goals and convictions is also high. This complicates the decision-making process. Case 2 shows that the number of actors involved is somewhat low in the first case but the main authorities share the same convictions, which ostensibly simplifies the decision-making process. The fact that opponents have organized themselves efficiently and have had access to the same data but with different results means that in the end the decision making was as tiresome as in the first case. Cases 3 and 5 provide us with insight on how a group of actors can become very powerful in the decision-making process because they have the monopoly on the information. In case 4, it becomes clear that institutional arrangements can reduce complexity since only one organization has formal author- ity. Finally, case 6 tells us that the lack of trust, the enormous difference in political opinion, and the lack of one owner and authority make decision making so complex that a decision that is seen as legitimate by all actors becomes impossible. In general, taking the previous part of the analysis into account it shows that the actual number of different actors has no influence, it is the number of different goals they hold.

10.7 Conclusions

This chapter aims to answer the question how the designing and using models and the communication between designer and policy maker influences the process of public decision making in terms of values. Analysis of the six cases shows that this influence is considerable. We find that a large diversity exists within the cases on the different values we have evaluated and that the impact of these values, perceptions, and beliefs is very important for the process of policy making. This is because when actors think and believe the same things, they tend to think that their work encompasses all possi- ble variety. In other words, being of the same mindset triggers unintentional selective blindness. Consequently, the models are not under close scrutiny and decisions made using a certain model reflect the biases that were unintentionally programmed into the model. For example, in the case of predicting the outbreak of livestock diseases, it appeared that the option “clearing of animals” could never be a feasible outcome of the model, whereas in reality it could be a possible answer.

A high diversity in actors raises a situation of conflict as multiple actors bring forward their own perspectives that are in many cases only partly convergent and downright contradictory in some cases. In other words, higher diversity leads to more obvious clashes of goals, beliefs, and values. The models that are used and the results that the models generate are being questioned more explicitly and openly, consequently leading to a higher perceived complexity as it becomes much more dif- ficult to reach a quick conclusion. Diversity or lack, thereof, is partly a design feature of the institutional dimension, partly an unintentional process between actors who trust and believe each other. As a design feature it emerges when the commissioning, developing, and using models are clustered around one or a limited set of tightly

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10 Values in Computational Models Revalued 217

coupled actors. Such a concentration of power, where both the research and the final decision are strongly linked, causes the actors to develop a bias towards their own ideas. Whether this link is institutionally determined or not, does not influence this. The models are consequently used as such. When such close links are contested or absent, the diversity raises because of the possibility of questioning current ideas and beliefs. As an unintentional process, it emerges when actors develop relationships of trust and belief. Although actors are not aware of it, such relationships still promote convergence of thinking, thus decreasing contradictory ideas.

If anything, the current research shows that models and data never speak for itself. On the contrary, they are heavily influenced by the social dynamics of the context they are developed in. Not only, as social constructivists claim, because of the values attributed to the models while they were used in their own political and institutional context but also as the technological determinists argue, because of the values in the model itself. They have been put there by the designers’ choice, often unintentionally; however, public policy makers are unaware of these choices. This leads us to conclude that computational models have a very large influence on the decisions that are made, as our case study shows. Following this we can argue that the potential power of these models within public decision making is substantial. Even though throughout this chapter we have argued that humans do have agency over technology our case studies show that this agency at some points is limited to the designer of the application and not to the public decision maker using the application in order to come to a decision. This raises questions for the future in which we ask ourselves that when computational models are normative because they cannot mimic full reality and instead reflect the developers’and users’ideas, what this means for those elected officials using the computational model for decision making. We observed often that belief in the model as the right descriptor and predictor of reality was almost absolute at the level of policy makers. “If it has a number it must be true.” We argue that this number is as much a reflection of the developers’ ideas as it reflects reality.

Furthermore, we can conclude that not only the designer of the model is able to place values into the model but these values are also incorporated by the users of the application. Not because through a technological frame, they attribute a certain meaning to a technology but because the data in the model itself is not flawless. This means that is possible that models used by different actors generate entirely different outcomes. It is not as much a design flaw of the model but rather a consequence of the complexity of values of data and models. However, public decision makers are often unaware of this and regard the model as being neutral and value-free. Designers often in their communication with public decision makers are trying to simplify their message and are trying to hide the normative biases.

Concluding, we can state that while the technology debate remains an ongoing debate, in terms of computational models and public decision making it is also important to research the relationship between the designer of the model and the public policy maker, the role of the designer and its interaction with end users and policy makers should be further researched. The nature of this relationship accounts partly for a more deterministic or more social constructivist view on the side of the

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public policy maker regarding technology. Therefore, this chapter cannot conclude whether technology should be viewed in either a social constructivist manner or a deterministic manner, but can conclude that different actors view the same technology in different epistemological manners.

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Philosophy of technology: the technological condition. An anthology. Blackwell, Malden. pp 386–397

Ellul J (1990) The technological bluff. William B. Eerdmans, Grand Rapids Feenberg A (1991) Critical theory of technology. Oxford University Press, New York Gerrits L (2008) The gentle art of coevolution. Erasmus Universiteit Rotterdam, Rotterdam Goodhue DL, Kirsch LJ, Quillard JA, Wybo MD (1992) Strategic data planning: lessons from the

field. MIS Quart 16(1):11–35 Heilbroner RL (1967) Do machines make history? In: Scharff RC, Dusek V (eds) Philosophy of

technology. The technological condition. An anthology. Blackwell, Malden, pp 398–404 Heilbroner RL (1994) Technological determinism revisited. In: Smith MR, Marx L (eds) Does

technology drive history? The dilemma of technological determinism. MIT Press, Cambridge, pp. 67–78

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Kaplan DM (ed) (2004) Readings in the philosophy of technology. Rowman & Littlefield, Lanham Lindblom CE (1959) The science of ‘muddling through’. Public Admin Rev 19(1):79–88 March JG, Simon HA (1993) Organizations. Blackwell, Cambridge Moody R (2010) Mapping power. Geographical information systems, agenda-setting and policy

design. Erasmus University Rotterdam, Rotterdam Scharff RC, Dusek V (eds) (2003) Philosophy of technology. The technological condition. An

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determinism. MIT Press, Cambridge

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Ware C (2000) Information visualization: perception for design. Morgan Kaufmann, San Francisco Winner L (1977) Autonomous technology: Technics-out-of-control as a theme in political thought.

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technology. Rowman & Littlefield, Lanham, pp 103–113 Winner L (1993) Upon opening the black box and finding it empty: social constructivism and the

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Chapter 11 The Psychological Drivers of Bureaucracy: Protecting the Societal Goals of an Organization

Tjeerd C. Andringa

Bureaucracy is the art of making the possible impossible –Javier Pascual Salcedo A democracy which makes or even effectively prepares for modern, scientific war must necessarily cease to be democratic. No country can be really well prepared for modern war unless it is governed by a tyrant, at the head of a highly trained and perfectly obedient bureaucracy. –Aldous Huxley Whether the mask is labeled fascism, democracy, or dictatorship of the proletariat, our great adversary remains the apparatus—the bureaucracy, the police, the military. Not the one facing us across the frontier of the battle lines, which is not so much our enemy as our brothers’ enemy, but the one that calls itself our protector and makes us its slaves. No matter what the circumstances, the worst betrayal will always be to subordinate ourselves to this apparatus and to trample underfoot, in its service, all human values in ourselves and in others. –Simone Weil

Abstract This chapter addresses the psychological enablers of bureaucracy and ways to protect bureaucrats and society from its adverse effects. All organizations benefit from formalization, but a bureaucracy is defined by the dominance of coer- cive formalization. Since bureaucrats are not bureaucratic among friends, one might ask what changes someone at work into a bureaucrat and why do bureaucrats and bureaucratic organizations exhibit their characteristic behaviors?

The pattern of behavior arises from fundamental psychology and in particular (1) our capacity for habitual behavior, (2) the difference between intelligence as manifestation of the coping mode of cognition and understanding as manifestation of the pervasive optimization mode, and (3) the phenomenon of authoritarianism as the need for external authority through a lack of understanding of one’s living environment. The combination of these phenomena leads to a formal definition, the “Bureaucratic Dynamic,” in which the prevalence of coercive formalization scales

T. C. Andringa (�) University College Groningen, Institute of Artificial Intelligence and Cognitive Engineering (ALICE), University of Groningen, Broerstraat 5, 9700 AB, Groningen, the Netherlands e-mail: [email protected]

© Springer International Publishing Switzerland 2015 221 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_11

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with “institutional ignorance” (as measure of how well workers understand the con- sequence of their own (in)actions, both within the organization as well on the wider society) and “worker cost of failure.”

Modern organizational theory has become progressively more aware of the inef- ficiencies and dangers of bureaucracy. The framework developed in this paper can be applied to protect society, organizations, and workers from the adverse effects of bureaucracy. Yet while non-bureaucratic organizations can produce excellence, they also rely on it and are therefore somewhat fragile. Improved protective measures can be developed using the framework developed in this chapter.

11.1 Introduction

In 2005 a Dutch insurance company aired a television commercial1 in which they showed a mother and daughter trying to collect their “purple crocodile” at a lost- and-found department. The clerk reaches for the missing object form—just next to the huge purple crocodile—and hands it to the mother to be filled in. After a few attempts the form is filled-in to the clerk’s satisfaction and he instructs the family to collect the missing object the next morning between 9 and 10 a.m. “But it’s there” the mother remarks. “Yes it is there” the clerk responds with an empty expression to this completely irrelevant remark.

Clearly, the original societal role of this lost-and-found department was replaced by a new goal: procedural correctness, irrespective of the state of the world and the implications of following procedure. The commercial ended with the remark that less bureaucracy is preferable.

We all know these blatant examples of bureaucracy, where form and proce- dure have become stultifying, any genuine empathy and human decency is absent, and the organization is no longer serving its original purpose efficiently. Yet the most shocking, albeit not normally acknowledged, aspect of these examples is that bureaucrats—outside the direct working environment—are just regular law-abiding individuals who might do volunteer work and who will gladly return something without insisting on a form to fill in first: among friends no-one is a bureaucrat.

I consider bureaucracy and bureaucratic mindsets as suboptimal or even patho- logical for the organization because it has adopted self-serving goals in favor of its original societal goal and for the bureaucrat because he or she is reduced—at work— to a shadow of his or her full human potential. This paper addresses the psychological reasoning on which this opinion is based.

Administration is not necessarily bureaucratic. And formalization—the extent of written rules, procedures, and instructions—can both help and hinder the overall functioning of the organization. In this chapter, I define bureaucracy as the dominance of coercive formalization within professional organizations. Coercive formalization

1 https://www.youtube.com/watch?v = 2Rw27vcTHRw

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11 The Psychological Drivers of Bureaucracy 223

takes away autonomy and changes a worker into the direction of an automaton: someone who can be easily replaced by information technology or a robot.

All human activities benefit from some form of formalization. Formalization allows automating routine tasks, to agree on how to collaborate, determine when and how tasks should be executed, and when they are finished. As such, procedures should not be changed too often so that they become and remain a stable basis for organizational functioning. Yet procedures should also not be too static and too strictly adhered to so that they lead to stultification, suboptimal task execution, and, above all, to loosing track of the societal goals of an organization. These are all signs of bureaucracy.

The bulk of this chapter comprises the formulation of a psychological framework that explains the phenomenology of bureaucratic and non-bureaucratic organizations. This framework is based on the two cognitive modes—the coping mode and the per- vasive optimization mode—that we defined in an earlier paper on LearningAutonomy (Andringa et al. 2013). Since bureaucracy has a lot to do with preventing worker au- tonomy, it is not surprising that our paper contains relevant ideas. What I found quite surprising, and highly relevant, was how well the “coping mode of cognition” fitted with the bureaucracy literature (Adler and Borys 1996; Weber 1978). In Learning Autonomy we had addressed the phenomenon of authoritarianism: the need for and acceptance of centralized or group authority. In this chapter I show that bureaucracy is a manifestation of authoritarianism in the context of professional organizations. Based on the defining characteristics of authoritarianism, I predict the incentives for coercive formalization, and with that the incentive for bureaucracy, as follows:

Incentive for coercive formalization = Institutional ignorance × Worker cost of failure I call this the Bureaucratic Dynamic. Maximizing “institutional ignorance” and “Worker cost of failure” leads, via psychological mechanisms outlined below, in- evitably to more bureaucracy. Fortunately, minimizing these will reduce bureaucracy. I predict that this formula can be used as an effective means to improve our under- standing of the phenomenon, to improve effective anti-bureaucracy measures, and to expose ineffective ones.

This chapter provides a transdisciplinary approach of bureaucracy. Transdisci- plinarity entails that I will ignore traditional (and often quite arbitrary) disciplinary boundaries and I will address multiple description levels; in particular a number of subdisciplines of psychology (fundamental science level), organizational research (applied science level), policy (normative level), and ethical considerations (value level) (Max-Neef 2005).

I start in Sect. 11.1, with an interdisciplinary analysis addressing how the diversity of bureaucracy can be understood through the degree and the type of coercive and enabling formalization. This analysis outlines many manifestations of bureaucracy that, together with the observation that no one is a bureaucrat among friends, demand a psychological explanation.

Section 11.2, forms the fundamental science bulk of this chapter. In it, I start with habits as effective and goal realizing activities that require only a minimal involvement of the higher faculties of mind because the behavior originates from

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and is guided by the (work) environment. This is followed by the observation that the two modes of thought we have defined in our earlier paper on LearningAutonomy (Andringa et al. 2013) match bureaucratic and non-bureaucratic strategies. The two modes differ in the locus of authority: external for bureaucracy and internalized for non-bureaucratic approaches. The centrality of the concept of authority becomes even clearer when I change the perspective to political psychology and in particular to the opposition authoritarians–libertarians. These groups of people differ in whether or not they (unconsciously) consider the complexity of the world too high to act adequately and feel comfortable. The shared feelings of inadequacy motivate them to instill order through coercive formalization and group or centralized authority: a phenomenon known as the “Authoritarian Dynamic.” This dynamic, in this chapter, applied in the context of professional organizations, drives the growth or demise of bureaucracy according to the “Bureaucratic Dynamic.” Section 11.2 closes with a short reflection on the (serious) detrimental effects of bureaucracy might have on bureaucrats (value level).

This chapter closes with a shorter section on how three modern management paradigms (applied science and policy level) can be classified according to the prevalence of the coping or the pervasive optimization mode. This entails, in some sense, that experiential evidence has already discovered what I argue from a psy- chologically informed perspective. Yet this perspective complements and enriches the experientially acquired understanding. I then direct attention to non-bureaucratic or “libertarian” organizations. One crucial aspect of these is that they not only are able to deliver pervasive optimization of all organizational roles, they also depend on it. This entails that they are fragile and easily wrecked by workers with insufficient institutional understanding. I give examples of how this degradation process typi- cally occurs and indicate a number of “red flags.” I end the chapter with a number of conclusions and observations.

11.2 Characteristics of Bureaucracy

This section is based on the analysis of organizations with different types, levels and forms of bureaucracy by Adler and Borys (1996). They provide an insightful and fairly comprehensive analysis of bureaucracy and its diverse forms. In addition Adler and Borys propose a structured typology of organizations that matches very well with our recent paper on open-ended (lifespan) development and in particularly with the development of bounded or full autonomy (Andringa et al. 2013). Taken together, these two articles provide an interesting generalized perspective on bureaucracy and, in general, on some foundational perspectives on human autonomy and human organizations.

Adler and Borys address the issue of worker autonomy in many different examples and remark “that much of the literature on the sociology of scientists and engineers asserts that employees in these occupations typically aspire to high levels of auton- omy in their work and that bureaucratic formalization undermines their commitment

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11 The Psychological Drivers of Bureaucracy 225

and innovation effectiveness.” Yet other employees might benefit from bureaucratic formalization. Consequently:

Organizational research presents two conflicting views of the human attitudinal or outcomes of bureaucracy. According to the negative view, the bureaucratic form of organization stifles creativity, fosters dissatisfaction, and demotivates employees. According to the positive view, it provides needed guidance and clarifies responsibilities, thereby easing role stress and helping individuals be and feel more effective.

In terms of autonomy, it seems that the bureaucratic form of organization stultifies the functioning of highly autonomous and motivated employees, while it actually pro- vides the less autonomous employees guidance and effectiveness in roles in which they would otherwise not be able to function. So bureaucracy constrains the au- tonomous employees, but enables the less autonomous to contribute more effectively. Accordingly, Adler and Borys conclude that the study “of the functions and effects of bureaucracy has split correspondingly with one branch focused on its power to enforce compliance from employees assumed to be recalcitrant or irresponsible and the other branch focused on bureaucracy’s technical efficiency.”

Based on this observation and a number of examples, Adler and Borys propose two structural dimensions for organizations: the type of formalization, spanning a continuum from coercive to enabling, and the degree of formalization from low to high. This leads to a two-dimensional representation with four quadrants resulting from the intersection of the axes as depicted in Fig. 11.1. The degree of bureaucracy is represented by the diagonal connecting a high degree of coercive formalization— characteristic of a highly bureaucratic or “mechanistic” organization—to a low degree of enabling formalization in the non-bureaucratic, or “organic,” organization. The other diagonal corresponds to a highly centralized, or “autocratic,” organization or a decentralized “enabling bureaucracy.”

The key component of this organizational typology is formalization and Adler and Borys describe many different aspects of formalization. A number of these are summarized in Table 11.1.

It will be clear from Table 11.1 that some degree of suitable formalization is highly beneficial, and probably defining for any organization and as such is broadly supported. Yet, too much formalization or formalization of an unsuitable kind will be detrimental for the employees and the way the organization realizes its societal mission and as such enacts its raison d’être.

Adler and Borys couple the two types of formalization—coercive and enabling— to perspectives on the organization. The “enabling approach” considers workers as sources of skill and intelligence to be activated. This works, of course, for workers who enjoy to be challenged, who aspire to develop their skills, and who feel a personal or shared pride regarding the work they are performing. In the “coercive approach” workers are treated as sources of problems to be eliminated. In this approach the opportunism and autonomy of workers (skilled or not) is to be feared and it leads almost inevitably to a deskilling approach. Deskilling is, of course, resented by those who consider work autonomy and skill-development essential for personal growth, but for the less skilled and probably more insecure workers, who know they will not be able to contribute effectively without strict and firm guidance, the coercive approach

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Fig. 11.1 Types of organization. (Based on Fig. 1 in Adler and Borys (1996))

is a way to contribute on a higher professional level than they would otherwise be able to achieve. Adler and Borys provide many properties of coercive and enabling formalization, which are summarized in Table 11.2.

As Table 11.2 shows, the basic logic of the coercive approach is to curtail the scope of behavioral options of workers through centralized and/or (corrective) group au- thority. In contrast, the basic logic of the enabling approach is to use diversity of insights and independent judgment of all employees to improve all aspects of the orga- nization (in the context of all its roles and obligations). As such the enabling approach relies on a combination of group authority and individual authority. But note that the role of group authority differs between the two approaches: in the coercive approach it is to signal and correct any deviant behavior, while in the enabling approach it is a means to aggregate organizational understanding in a common mode of working.

Asymmetries in power, of course, promote the coercive approach, but the same holds for ignoring or actively suppressing the skills and knowledge of the workers since this almost inevitably impoverishes the understanding of the organization and as such it leads to organizations that progressively become out-of-sync with reality: instead the organization creates its own peculiar realities based on whatever pleases the power structure, which progressively makes it more difficult to apply the ob- servations, knowledge, and insights of the workers for the proper execution of the organization’s societal role.

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11 The Psychological Drivers of Bureaucracy 227

Table 11.1 Positive and negative aspects of formalization. (Based on Adler and Borys (1996))

Negative effects of formalization: Positive effects of formalization:

Higher absences Formalization can increase efficiency

Propensity to leave organization Embrace of well-designed procedure facilitates task

Physical and psychological stress Performance and pride on workmanship

Reduced innovation Reduction of role conflict and role ambiguity

Reduced job satisfaction Increased work satisfaction

Reduced commitment to the organization Reduction of feelings of alienation and stress

Can help innovation if it capture lessons of prior

Lower motivation Experience or help coordination of larger-scale projects

broad preference and benefits for routine tasks

Formalization is disfavored if: Formalization is favored if:

Rules benefit managers: especially when rules are also used to sanction

Work is considered as a cooperative endeavor rather than the abrogation of autonomy

Bad rules/procedures: Good rules/procedures:

Resented Taken for granted

If possible ignored or avoided Hardly noticed

Adler and Borys couple the motivations (Deci and Ryan 1987) to participate in the organization to the type of formalization. The coercive formalization corresponds to external (authority enforced, fear of punishment, rule compliance) or introjected motivation (internal or esteem-based pressures to avoid harm) because it does not tap into whatever is intrinsically motivating for the employees. The enabling for- malization does just that: it allows motivation based on identification with personal importance or compliance with personal goals. It might even allow intrinsic moti- vation in the form of completely unconstrained and self-determined activities that involve highly enjoyable states like flow and play.

These motivations—in this order—have been coupled to the perceived locus of causality (PLOC), which reflects the degree the individual or some external author- ity or influence originates the behavior (Ryan and Connell 1989). It is a measure of autonomy and agency. The more autonomous the behavior, the more it is endorsed by the whole self and is experienced as action for which one is responsible (Deci and Ryan 1987). In particular for activities with an external PLOC individuals do not re- ally feel a personal responsibility and probably no moral responsibility as well. This then suggests that it is possible to realize highly unethical goals by promoting the coercive form of formalization: the workers will not feel any sense of responsibility. This explains why bureaucracies (or more general hierarchical organizations subject to coercive formalization, such as the military, intelligence agencies, or some multi- nationals) are so often involved in atrocities. Aldous Huxley’s quote at the beginning of this chapter acknowledges this as well.

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Table 11.2 Properties of coercive and enabling formalization

Coercive formalization Enabling formalization

Basic attitude Basic attitude

Workers as sources of problems to be eliminated. Opportunism of workers to be feared: deskilling approach

Workers as a source of skill and intelligence to be activated

Key properties: Key properties:

The formal system (e.g., organogram) is leading, workers exist to serve their role

The formal system exists to enable and support the workers in executing the societal function of the organization

Deviation from the protocol is suspect Deviations from procedure decided by the work- ers

Procedures often non-transparent to keep knowl- edge about the organization from the employees to prevent “creative interaction”

Deviations from the protocol signals the need for better procedures or methods and are a learning opportunity

Procedures as assertions of duties (not to help) Procedures help to explain key components and codifying best practices

“Global transparency” highly asymmetric, with procedures that, for example, help to real- ize a panopticon (so that employees know that superiors can monitor them at any time)

Procedures to provide insight into personal per- formance

Global transparency of the organization is a source of employee initiative and as such a risk to be minimized

Global transparency provides insight in the role of processes in the broader context of the orga- nization as necessary source of innovation and improvement for the whole organization

Procedures define, in detail, a sequence of steps to be followed and force the employee to ask approval for any deviation of the protocol (such as skipping unnecessary steps)

Forces promoting the coercive formalization: Forces promoting the enabling formalization:

Asymmetries in power Societal preference for enabling formalization

Absence of reality checks associated with an in- ward focus in which local conflicts become more important than organizational goals

A necessity of a very complex task environment (such as in times of competitive pressure)

The results of automation (whatever ICT pro- duces) needs to be communicated and followed- up to the letter

Automation first replaces routine operations (their formalization become part of the ICT) and leads to a demand for more skilled employees

Motivation type: Motivation type:

External (authority enforced, fear of punish- ment, rule compliance)

Intrinsic motivation (completely self-determined activities)

Introjected motivation (internal or esteem-based pressures to avoid harm)

Identified (with personal importance) or inte- grated (compliance with personal goals)

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11 The Psychological Drivers of Bureaucracy 229

In this section I have outlined a number of properties of bureaucracies for which I will propose the psychological underpinnings in Sect. 11.3. This section will focus on why the phenomena outlined before, emerge inevitably from basic psychology.

11.3 Psychological Roots of Bureaucracy

11.3.1 Habits

Since the formalization, and therefore automation, of behavior is an integral part of bureaucracy it makes sense to address the topic of habits and habitual behavior because the psychological term “habit” refers to an automatic response to a specific situation (Ouellette and Wood 2003; Wood and Neal 2009). The ability to behave ha- bitually is a wonderful thing, because it means that we have learned to do something so efficiently that our minds are kept free for other things. Habits can be nested so that for example, the habit of driving can be part of daily, weekly, monthly, or yearly routines.

Habits are well-trained perception–action relations that are efficiently combined so that they address our daily affairs with minimal mental effort and their combination may lead to an endless variety of effective, while still seemingly effortless, behaviors. During the execution of a habit it is the environment that determines your actions: if there is a door on your path, you open it. You will not normally initiate the door opening behavior without a door. You can of course, willingly, try to activate door- opening behavior in the middle of a lawn. But nothing in the lawn-environment will activate this particular behavior. This holds for steak cutting, hair combing, wall painting, and turning the page of a newspaper: you can do it whenever you want, but it is only productive (and looks less silly) if you let the environment activate the desired behavior. That is the reason why each habit is activated in situations that provide the affordances to activate the behavior.

The way we respond to social or work situations is also for a large part habitual. In particular we find “that mental content activated in the course of perceiving one’s social environment automatically creates behavioral tendencies” (Bargh 2010). The first time we encounter some situation we might not know what to do and to give it all our attention to decide on appropriate behavior, but after a few times practice, the situation is neither novel nor challenging and we respond habitually and according to, for example, the stereotypes activated by the environment. Because of the flexibility of habitual components and because of the minimal mental effort it costs to combine them adaptively, most of our daily activities are habitual, which is good because during habit execution we are left with ample opportunities to direct our attention to interesting, useful, or important things.

William James, one of the first and still one of the greatest psychologists, had much to say on habits. In fact he addresses the topic of habits as one of the foundations of psychology. And what is relevant for this chapter, he explicitly defined habit, 125 years ago, as the flywheel that keeps society (and the organizations that constitute it) stable (James 1890, p 16–17).

Habit is thus the enormous flywheel of society, its most precious conservative agent. It alone is what keeps us all within the bounds of ordinance, and saves the children of fortune from the

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230 T. C. Andringa

envious uprisings of the poor. It alone prevents the hardest and the most repulsive walks of life from being deserted by those who are brought up to tread therein. It keeps the fisherman and the deckhand at sea through the winter; it holds the miner in its darkness, and nails the countryman to its log-cabin and its lonely farm through all the months of snow; it protects us from invasion by the natives of the desert and the frozen zone. It dooms us all to fight out the battle of life upon the lines of our nurture or our early choice, and to make the best of a pursuit that disagrees, because there is no other for which we are fitted, and it is too late to begin again.

So habits do not only free our minds for more important things they also keep us within the bounds of the status quo or pursuits once started. Habits are not a genetic inevitability, but are the result of the way we are raised, educated, and introduced in our professional lives. James defines the role of education therefore in terms of acquiring habits.

The great thing, then, in all education, is to make our nervous system our ally instead of our enemy. It is to fund and capitalize our acquisitions, and live at ease upon the interest of the fund. For this we must make automatic and habitual, as early as possible, as many useful actions as we can, and guard against the growing into ways that are likely to be disadvantageous to us, as we should guard against the plague. The more of the details of our daily life we can hand over to the effortless custody of automatism, the more our higher powers of mind will be set free for their own proper work.

The original text had an emphasis in italic, here I have added an emphasis in bold to focus on the fact that habits may not necessarily be beneficial to us, they might in fact be more beneficial to whoever has defined the status quo and now benefits from the habitual continuation of that status quo. This status quo can typically be some sort of working or living environment that has not been designed by the individual himself, but results from some reasoning that is predominantly or wholly beyond the individual’s understanding. In a situation like this we have, as far as work is concerned, no “opportunities to direct our attention to interesting, useful, or important things.” In these conditions habitual behavior dominates the work floor and very little of the behavior that characterizes the individual in the rest of its life is visible.

This already explains part of the bureaucratic syndrome by answering, at least par- tially, the question “What shuts down so much of a bureaucrat’s mental capabilities?” The partial answer is that a difficult to understand environment that effectively acti- vates habitual behavior leads to the activation of habitual behavior while denying the bureaucrat self-selected opportunities of intrinsic interest, usefulness, or importance. Consequently, absent the understanding of their significance in the bigger scheme of things, the true bureaucrat has no real responsibilities other than maintaining the conditions in which habitual functioning is facilitated, which is exactly what I saw in the introductory example.

The conclusion that the bureaucrat’s single or main—self-imposed— responsibility is to uphold the conditions for its own habitual functioning explains to a large degree the stability of bureaucracies. But note that this is especially the case for work environments that exceed the scope of understanding of workers and management: only here they have no choice but to uphold the conditions in which they function habitually. With sufficient organizational understanding, workers and management can break this cycle. We will return to this topic in the subsection on “Authoritarianism” (Sect. 11.3).

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11 The Psychological Drivers of Bureaucracy 231

11.3.2 Two Modes of Thought

The previous subsection already separated a habitual mode of thought, which requires very little attentional control, and forms of cognition that are not (yet) habitual be- cause they do require highly focused attention, for example because they are new, ever changing, or otherwise engaging or challenging. This opposition arises from two large families of cognitive phenomena that McGilchrist (2010) (with extensive justi- fication and highly compelling historical support) couples to the left and right brain hemispheres. In a recent paper (Andringa et al. 2013) we generalized McGilchrist’s interpretations as two complementary modes of cognition: the coping mode and the pervasive optimization mode.2

The coping mode is concerned with control: with preventing things (the whole world actually) from spinning out of control. Problem solving and the suppression of interfering diversity are central concepts for this mode. The pervasive optimization mode on the other hand is, as the name suggests, concerned with the optimization of all processes in the context of everything else. Where the coping mode is concerned with the problems of the here and the now, the pervasive optimization mode is concerned with promoting the likeliness of beneficial states in the near and distant future; both here and elsewhere, and for yourself (body and mind) as well as the rest of the world (family and friends, and the natural and social environment). Where the coping mode is highly focused and aims at tangible results in a structured and predictable way, the pervasive optimization mode is much more diffuse; it has no sequential demands and does not necessarily lead to directly tangible results. It does however set-up, in a statistical sense, the conditions for an unproblematic future. The coping mode relies on situational control and intelligent problem solving skills. The pervasive optimization mode relies on a broad understanding of the world and its dynamics in combination with the skills to relate to and work with these dynamics (Andringa et al. 2013).

The concept of “intelligence,” especially as conceptualized and measured in an IQ-test, summarizes the coping mode because it measures one’s ability to produce standardized and expected answers to self-contained problems. Intelligence is proven through the ability to solve problems posed by others. The minimal capacity to do this is simply by reproducing and applying appropriate formal operations without understanding neither the problem nor the situation that gave rise to it. This rule- application ability—apparent as formalization—is capitalized on in a stereotypical bureaucracy.

This can be contrasted to the concept of “understanding”—according to the New Oxford Dictionary “the ability to perceive the significance, explanation, or cause of

2 The term pervasive-optimization mode has been introduced in this paper. In Andringa et al. (2013) we did not use a single term and we described this mode as cognition for exploration, disorder, or possibility. In a recent paper “Cognition From Life” (Andringa et al. 2015) we introduced the term cocreation mode of cognition. We decided to use the term pervasive-optimization mode in this paper since the term co-creation mode requires additional explanation.

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232 T. C. Andringa

(something)”—which captures strengths of the pervasive optimization mode. If you understand something you can use it not only reproductively or in a scripted way, but you know how to apply it in novel and open application domains. Consequently you can prove your depth and breadth of understanding through realizing novel or nonstandard results in the world. Conversely you proof your lack of understanding by making a mess of your live (indicating the utter failure of pervasive optimization). Another way to proof your lack of understanding is by reducing your life to an existence where very little novel or nonstandard happens (e.g., the extension of a bureaucratic attitude to the rest of life). In positive terms, the discovery of relations (between everything) and the detection of possibilities (in oneself, in others, at work, or in the whole of the environment) is strength of the pervasive optimization mode.

Returning to the example I started with. A bureaucrat is unlikely to act bureau- cratically when not at work and especially not while among friends. The pervasive optimization mode seems, therefore, the default mode, while the coping mode is a fall-back mode that shines when the pervasive optimization mode was unable to prevent immanent or pressing problems. Interpreted as such, a bureaucracy is a working environment that forces (coerces) employees into a problem-solving, problem-preventing, or problem-control mode: the coping mode.

As outlined in our earlier paper on Learning Autonomy (Andringa et al. 2013), the pervasive optimization mode assumes autonomous participation in an open, dynamic, and infinite world of nested processes that form dynamically stable and continually evolving entities: the real continually developing and never fully graspable world. For the pervasive optimization mode of being, truth is defined as accordance with reality, which is to be tested by acting in the world; as such understanding and experiences are essentially subjective. This mode of being is particularly effective in situations where new aspects of the dynamics of the world are to be investigated to expand one’s thought-action repertoire (Fredrickson and Branigan 2005) and where novel and creative solutions are appropriate.

In contrast, the coping mode assumes a closed, static, and self-contained (and therefore finite) world, in which entities are symbolic, discrete, and abstract and in which perfect solutions may be possible. It is also a mode in which one is an “objec- tive” observer instead of a participant. It is the world as represented in a computer program: highly functional, perfectly repeatable, and subject to rational consider- ations, but ultimately devoid of life. In this mode of being, truth is defined as the result of consistent reasoning and consensually agreed on linguistically shared and presented facts. This mode of being is particularly effective in situations in which (immediate) problems have to be solved or addressed in a detached, rational, stan- dardized, and communicable way. Bureaucracies, but also scientific communication, are typical examples of this.

Because the coping mode assumes a closed, static, and self-contained (and there- fore finite) world it needs an external influence to maintain the conditions in which it can function in the first place. As we argued in Learning Autonomy, authorities— defined as processes or agents that create, maintain, and influence the conditions in which agents exist—fulfill this role. The authority for the left hemispheric coping mode is either its own right hemisphere or some external authority such as parents,

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11 The Psychological Drivers of Bureaucracy 233

leaders, governments, or cultural influences in the broadest possible sense. In prac- tice, it is a combination of internal and external authority, and it defaults to external authority whenever the right hemisphere is unable to act as reliable authority. Put differently, when the right hemisphere is unable to generate a sufficient level of un- derstanding of the situation, it cannot remain in the lead and the left hemisphere becomes dominant at the cost of surrendering autonomy to some (actually any) ex- ternal authority. Importantly, this switch is subconscious. Still we can become aware of it through metacognition (like observing a change of emotions and/or a change in attitude or strategy). We will return to the role of understanding in the section on authoritarianism.

Table 11.3 provides a summary of properties ascribed to the coping and the per- vasive optimization mode. It is based on Table 11.1 of Andringa et al. (2013), which in turn is based on Chap. 1 of McGilchrist (2010). The remarks in italic are examples of a bureaucratic (for the coping mode) and a non-bureaucratic (for the pervasive optimization mode) interpretation of these properties. It will be clear from Table 11.3 that the strengths of the coping mode can be used to illustrate typical and/or extreme bureaucratic functioning, while the pervasive optimization mode can be used to il- lustrate a non-bureaucratic alternative. Note that the original table was intended as a summary of left and right hemispheric strengths to be used in a quite different context: that it can be used to illustrate typical properties of bureaucratic and non-bureaucratic organizations, is a serendipitous observation that I consider highly meaningful.

11.3.3 Authoritarianism

At the end of the last subsection, authority was defined as the ability to create, maintain, influence, or exploit a living environment (Andringa et al. 2013). This entails that whenever individuals do not know how to self-maintain proper living conditions, they must rely on some sort of “authority” to keep living conditions within manageable bounds. This need for authority scales inversely with the scope of inadequacy: the more pervasive the inadequacy, the greater the need for and role of authority. Conversely, the better individuals cope with and maintain their own living environment—the more they have internalized authority—the less they need external authorities. This essential (and existential) need for authority is the defining characteristic of the concept of authoritarianism.

Within the domain of political psychology people with a strong need for au- thority are known as authoritarians and those who do not as libertarians (Stenner 2005, 2009, 2009). Authoritarians prefer (centralized) group authority and unifor- mity, while libertarians prefer (decentralized) individual authority and diversity. The structure and properties of authoritarian behavior have been studied in detail in “The Authoritarian Dynamic” by Princeton researcher Karen Stenner (2005). Authoritar- ianism is characterized by a strong tendency to maximize oneness (via centralized or group control) and sameness (via common standards), especially in conditions where the things that make us one and the same—common authority and shared values—appear to be under threat.

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234 T. C. Andringa

Ta bl

e 11

.3 C

og ni

tiv e

m od

es de

fin e

or ga

ni za

tio ns

. C

om pa

ri ng

pr op

er tie

s of

th e

co pi

ng m

od e

of co

gn iti

on (a

ttr ib

ut ed

to th

e le

ft he

m is

ph er

e) an

d th

e pe

rv as

iv e

op tim

iz at

io n

m od

e (a

ttr ib

ut ed

to th

e ri

gh t

he m

is ph

er e)

. A

ss oc

ia te

d w

ith ea

ch to

pi c,

in ita

lic ,

a bu

re au

cr at

ic in

te rp

re ta

tio n

fo r

th e

co pi

ng m

od e

an d

a no

n- bu

re au

cr at

ic in

te rp

re ta

tio n

fo r

th e

pe rv

as iv

e op

tim iz

at io

n m

od e.

(B as

ed on

A nd

ri ng

a et

al .2

01 3)

To pi

c C

op in

g m

od e

of co

gn iti

on (L

ef th

em is

ph er

e) B

ur ea

uc ra

ti c

in te

rp re

ta ti

on

Pe rv

as iv

e op

tim iz

at io

n m

od e

of co

gn iti

on (R

ig ht

he m

is ph

er e)

N on

-b ur

ea uc

ra ti

c in

te rp

re ta

ti on

M ai

n co

nc er

n Pr

in ci

pa lc

on ce

rn is

ut ili

ty Pr

io ri

tiz es

w ha

ta ct

ua lly

is an

d w

ha tc

on ce

rn s

us

Te ch

ni ca

ll y

qu al

ifi ed

pe rs

on ne

li s

ab le

to us

e th

es e

ut il

it ie

s to

th e

m ax

T he

or ga

ni za

ti on

ad ap

ts it

se lf

fle xi

bl y

an d

ef fe

ct iv

el y

to th

e cu

rr en

ts it

ua ti

on

T he

w or

ld as

a re

so ur

ce

T he

cl ie

nt sh

ou ld

su pp

or t

(“ fe

ed ”’

) an

d co

m pl

y w

it h

th e

bu re

au cr

ac y

ir re

sp ec

ti ve

it s

fu nc

ti on

in g

Sc op

e L

oc al

sh or

t- te

rm vi

ew B

ig ge

r pi

ct ur

e (b

ro ad

er ,l

on g-

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vi ew

). D

ra w

s at

te nt

io n

fr om

th e

ed ge

s of

aw ar

en es

s

Fo cu

s on

sh or

tt er

m so

lu ti

on s

E ve

ry th

in g

th at

th e

or ga

ni za

ti on

ca n

co nt

ri bu

te to

th e

la rg

er w

or ld

is po

te nt

ia ll

y im

po rt

an t

D ea

lw ith

w ha

ti tk

no w

s

O nl

y w

ha ti

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fic ia

ll y

en te

re d

in th

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re au

cr ac

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ex is

ts

A tti

tu de

to w

ar ds

w or

ld R

ep re

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in g

th e

w or

ld :t

he w

or ld

as a

co py

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ex is

ts in

a co

nc ep

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,s ui

ta bl

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nc in

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,o pe

n fo

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w ha

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ap ar

tf ro

m ou

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w ith

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pr ec

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d no

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g on

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cu s

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pr oc

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co m

bi na

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w ri

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(e xp

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t) co

m m

un ic

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nl y

w ha

tc an

be re

pr es

en te

d w

it hi

n th

e bu

re au

cr ac

y ex

is ts

an d

is su

bj ec

tt o

m an

ip ul

at io

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or ga

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ti on

in al

li ts

fu nc

ti on

s ca

n ad

ap tt

o w

ha t

“t he

w or

ld br

in gs

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11 The Psychological Drivers of Bureaucracy 235

Ta bl

e 11

.3 (c

on tin

ue d)

To pi

c C

op in

g m

od e

of co

gn iti

on (L

ef th

em is

ph er

e) B

ur ea

uc ra

ti c

in te

rp re

ta ti

on

Pe rv

as iv

e op

tim iz

at io

n m

od e

of co

gn iti

on (R

ig ht

he m

is ph

er e)

N on

-b ur

ea uc

ra ti

c in

te rp

re ta

ti on

In te

re st

s In

te re

st ed

in th

e fa

m ili

ar an

d th

e kn

ow n,

di ffi

cu lty

w ith

di se

ng ag

in g

fr om

th e

fa m

ili ar

In te

re st

ed in

th e

no ve

l

Fo rm

s an

d pr

oc ed

ur es

fo rm

th e

on ly

ob je

ct of

in te

re st

A lw

ay s

in te

re st

ed in

w ay

s to

ad ap

tt he

or ga

ni za

ti on

to a

ch an

gi ng

an d

de ve

lo pi

ng w

or ld

C on

ce rn

ed w

ith w

ha ti

tk no

w s

C on

ce rn

ed w

ith w

ha ti

te xp

er ie

nc es

W ha

tc an

no tb

e de

al tw

it h

in th

e bu

re au

cr ac

y do

es no

te xi

st N

ew in

fo rm

at io

n, ne

w sk

ill s,

em ot

io na

le ng

ag em

en t

C on

ce rn

ed w

ith m

an -m

ad e

ob je

ct s

C om

pe te

nc e

de ve

lo pm

en to

fw or

ke rs

is no

ts cr

ip te

d bu

t B

ec au

se th

es e

ar e

ty pi

ca ll

y st

at ic

an d

fo r

a pa

rt ic

ul ar

us e

de ve

lo ps

on th

e jo

b th

ro ug

h in

di vi

du al

ex pe

ri en

ce an

d de

ve lo

pm en

t. W

or ks

sh ou

ld be

in he

re nt

ly re

w ar

di ng

N on

liv in

g ob

je ct

s sp

ec ia

lis t.

L iv

in g

en tit

ie s

as to

ol s

or in

st ru

m en

ts M

or e

co nc

er ne

d w

ith liv

in g

in di

vi du

al s.

L iv

in g

in di

vi du

al s

as ot

he r

in di

vi du

al s

Pe op

le an

d an

im al

s re

du ce

d to

nu m

be rs

th at

ca n

m an

ip ul

at ed

in a

si m

il ar

w ay

as ot

he r

re so

ur ce

s E

ac h

in di

vi du

al cu

st om

er ha

s to

be tr

ea te

d in

th e

w ay

m os

t su

it ab

le fo

r th

e in

di vi

du al

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236 T. C. Andringa

Ta bl

e 11

.3 (c

on tin

ue d)

To pi

c C

op in

g m

od e

of co

gn iti

on (L

ef th

em is

ph er

e) B

ur ea

uc ra

ti c

in te

rp re

ta ti

on

Pe rv

as iv

e op

tim iz

at io

n m

od e

of co

gn iti

on (R

ig ht

he m

is ph

er e)

N on

-b ur

ea uc

ra ti

c in

te rp

re ta

ti on

St re

ng th

s T

ho ro

ug hl

y kn

ow n

an d

fa m

ili ar

G at

he ri

ng ne

w in

fo rm

at io

n

St an

da rd

iz ed

ta sk

ex ec

ut io

n by

sp ec

ia li

st s

Im pr

ov e

un de

rs ta

nd in

g of

al lr

el ev

an tp

ro ce

ss es

an d

as pe

ct s

of th

e jo

b

E ffi

ci en

ti n

ro ut

in e

si tu

at io

ns an

d fa

m ili

ar sk

ill s

G oo

d w

he n

pr ed

ic tio

n is

di ffi

cu lt

Tr ai

ni ng

to re

du ce

er ro

r fr

eq ue

nc y

F le

xi bl

e ta

sk ex

ec ut

io n

by ge

ne ra

li st

s

Pr io

ri tiz

es th

e ex

pe ct

ed an

d ge

ne ra

te s

ex pe

ct at

io ns

A no

m al

y (i

nd iv

id ua

lit y)

de te

ct or

:i nd

iv id

ua ls

H el

p st

an da

rd cu

st om

er s

fir st

,i rr

es pe

ct iv

e of

ur ge

nc y

A da

pt or

ga ni

za ti

on to

th e

si tu

at io

n

T hi

ng s

m ad

e fix

ed an

d eq

ui va

le nt

:t yp

es .A

ll th

at is

re -p

re se

nt ed

as ov

er -f

am ili

ar ,i

na ut

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ic ,l

if el

es s

ca te

go ri

es M

or e

ef fic

ie nt

ly w

he n

in iti

al as

su m

pt io

ns ne

ed to

be re

vi se

d or

w he

n ol

d in

fo rm

at io

n ne

ed s

to be

di st

in gu

is he

d fr

om ne

w in

fo rm

at io

n. A

ll th

at is

“p re

se nt

” as

ne w

,a ut

he nt

ic ,a

nd in

di vi

du at

ed

E qu

at e

pe op

le w

it h

(c as

e) nu

m be

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ua ra

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11 The Psychological Drivers of Bureaucracy 237 Ta

bl e

11 .3

(c on

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of it

s be

ha vi

or

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238 T. C. Andringa

Table 11.4 Child rearing qualities used to determine authoritarianism

Authoritarians Children should:

Libertarians Children should:

Should obey parents Be responsible for their actions

Have good manners Have good sense and sound judgment

Be neat and clean Be interested in how and why things happen

Have respect for elders Think for themselves

Follow the rules Follow their own conscience

Stenner (2005) used the 5 two-option questions about child rearing values to determine the degree of authoritarianism that are depicted in Table 11.4.

The difference between the answers that authoritarians and libertarians choose is qualitative: authoritarians teach children to behave in certain proscribed ways and to obey external authorities (elders, parents, norms), libertarians teach children how to understand the world and how to act responsibly and autonomously. The difference between authoritarians and libertarians is, therefore, neither ideological nor political: it depends on a combination of two aspects (1) internal or external authority, and (2) the depth and pervasiveness of understanding of the current living environment. Authoritarianism is, therefore, both, a personality trait and a state-of-being that is manifested in some situations, but not in others: the more individuals are brought into situations they do not (have learned to) understand and the more they are pressured to act, the more they will exhibit authoritarian behavior (See subsection Authoritarian Dynamic).

The child rearing qualities reflect the conditions that were identified for the left hemispheric coping mode and the right hemispheric pervasive optimization mode. As such it makes sense to interpret authoritarian behavior as behavior guided by the logic of the coping mode and libertarian behavior as behavior guided by the pervasive optimization mode. It also follows that bureaucracy is a manifestation of authoritarianism. Which also explains the reason why even strong bureaucrats are never bureaucratic among friends: here they are responsible for their own actions, expected to have a good sense and sound judgment, to be interested in others, to think and decide for themselves, and to follow their conscience. It is just that their working environment forces them out of this mode and into the coping mode.

11.3.4 Two Attitudes Toward a Complex World

According to Stenner (2009) authoritarians are not endeavoring to avoid complex thinking so much as a complex world. Authoritarians are just as intelligent as

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11 The Psychological Drivers of Bureaucracy 239

libertarians3, but they understand the world more shallowly and less pervasively. Consequently, two individuals can experience and interpret a shared world quite differently. If it is experienced as too complex to comfortably deal with, one is in a coping or authoritarian mode of being. Consequently one’s highest priority is to eliminate all sources of diversity to bring complexity down to manageable levels. And this can explain why people in the authoritarian mode take control over de- cision processes and become subtly or overtly intolerant to uncontrolled diversity through, for example, coercive formalization. It is not because they think they can do it better—although they might be convinced of that—but because of a strong unconscious urge to establish a larger measure of control over the situation with the aim to simplify it.

In the libertarian or pervasive optimization mode the complexity of the world is well below daily coping capacity and where authoritarians see problems they see opportunities. This can actually be problematic because realizing these opportuni- ties is bound to lead to further social or organizational complexification that might aggravate authoritarians even further. Libertarians are therefore, quite unwittingly, major sources of feelings of inadequacy in authoritarians.

And this leads to a one-sided resentment—a shared and therefore unifying emotion—toward anything beyond coping capacity among authoritarians of which libertarians are typically completely unaware. In fact encroaching bureaucracy can be interpreted as a (low-intensity) war between two ways of facing reality. While libertarians are unaware of any war being fought (because they fail to see any need for it), they can be blamed for co-creating a complex world surpassing authoritarian coping capabilities. And authoritarians, with their limited understanding, share a deep anxiety and are highly motivated to do something about it collectively.

This subconscious anxiety motivates to oppose all sources of complexity, unpre- dictability, novelty, and growth that complexify, confuse, and destabilize an ordered and predictable state of affairs. In fact people in an authoritarian mode want to distance themselves from all of these things and the people (e.g., immigrants, homo- sexuals, libertarians) that embody or promote them. One driving emotion is disgust (Frijda 1986; Inbaret al. 2009): the urge to distance oneself from an unhealthy or otherwise harmful object, activity, person, or influence. Authoritarians in this state speak quite frankly and clearly about the moral decline that they see all around them and that disgusts them (and often enough explicitly worded). And they are quite mo- tivated to do something about it. Vocal moral outrage about the organization losing its values and morals (typically in response to some gentle questions about the state of the organization) is an indication of an organization ready to become dominated by an authoritarian mindset and the associated urge to bring the complexity of the world/organization back to within coping capacity.

3 Authoritarians might value intelligence more than libertarians. For example more than half of the 21 Nazi Nuremburg defendants had a superior intelligence (belonging to the most intelligent 3 to 0.2 %) and only one had average intelligence (Zillmer et al. 2013). This suggests that authoritarians select on intelligence.

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240 T. C. Andringa

In fact this same process seems to occur on a societal scale during revolu- tions. In his seminal book on revolutions in the nineteenth and twentieth century, Billington (1980) explicitly mentions the revolutionary’s strong motivation to reduce complexity when he concludes:

The fascinating fact is that most revolutionaries sought the simple, almost banal aims of modern secular men generally. What was unique was their intensity and commitment to realizing them.

Billington concludes that popular revolutions invariably aim to bring society back to a simpler state of affairs. Those revolutions, equally invariably, seem to coincide with periods of increased intolerance (against moral violators, freethinkers, or libertarians) and the rise of bureaucracy will not be surprising. It is all part of the same dynamic.

11.3.5 The Authoritarian Dynamic

This complexity reducing dynamic has a name: it is called theAuthoritarian Dynamic (conform the name of Stenner’s 2005 book). In its original form it was formulated for the domain of Political Psychology as the correlation:

Intolerance = Authoritarianism × Threat In this “formula” “Intolerance” refers to intolerance to diversity and in particular intolerance to (perceived) violations of norms or the normative order. “Authoritarian- ism” initially (Stenner 2005) referred to how often one chooses the left-side answers of Table 11.4, which in turn is a (crude) measure of the shallowness of understand- ing of the world and the need for external (central or group) authority to create or maintain a world in which one feels adequate. “Threat” refers to the perceived threat and/or abundance of indicators of moral decline. The multiplication symbol “ × ” refers to the “and”-condition entailing that for “intolerance to diversity” to become prominent both authoritarian disposition and perceived threat are required to build up the motivation to restore order through intolerance (or coercive formalization).

Note that this combination of (1) a low level of understanding of the world— ignorance— and (2) the threat-induced significance of acting appropriately leads to deep feelings of personal inadequacy. This entails that the fundamental driver of the authoritarian dynamic can be reformulated as the “Ignorance Dynamic.”

Motivation to restore personal adequacy = Ignorance × Cost of failure to act appropriately The deep feelings of personal inadequacy can—from the perspective of the Authoritarian—only be improved through the realization of a more tightly controlled and less diverse world. Interestingly, violence researcher Gilligan (1997) argues that shame, due to the public display of personal failure to act appropriately, is the root cause of all violence. This is yet another perspective on the coercive nature of intolerance.

There is a perfectly viable alternative approach to improve one’s deep feelings of personal inadequacy, but, unfortunately, authoritarians generally do not come up

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11 The Psychological Drivers of Bureaucracy 241

with this among themselves. This alternative is to educate oneself out of feelings of personal inadequacy through acquiring a deeper and more pervasive understanding as a basis for more advanced strategies. Shallow understanding in combination with normal or good intelligence prevents this. The strength of the coping mode’s “in- telligent” ways of treating problems as self-contained (such as the problems in an IQ-test) leads authoritarians to redefine or ignore reality until it fits with their current solution repertoire.

This is another way to understand authoritarian intolerance. It is intolerance against anything opposing successful coping with an existing solution repertoire. It is therefore also intolerance against advanced strategies—based on a deeper and more pervasive understanding—that are not (yet) fully understood. Only when the threat level and the “cost of failure to act appropriately” diminish, these coping strategies can be replaced by pervasive optimization strategies. This entails that whoever controls the threat-level, controls the level of intolerance to diversity and growth, the moment intolerance becomes dominant, and the number of people in an authoritarian mode.

The Authoritarian Dynamic can be defined on the level of the individual as well as on a group or even societal level. A single authoritarian in an organization will defer its own authority to the more skilled and knowledgeable around. But the same authoritarian in a context with more authoritarians will be highly motivated to collec- tively adopt and enforce measures, i.e., introduce coercive formalization, expected to reduce situational complexity and personal inadequacy. Actually a small, but highly motivated, fraction of a society might start a revolution to (re)turn to a simpler, more controlled, and better understood world according to Billington’s (1980) conclusions.

For example one of the slogans of the French revolution Liberté, égalité, fraternité (freedom, equality, brotherhood), which became the French national motto a century later, is appealing to the libertarian values of diversity and individual authority. Yet it is also consistent with an urge to a simpler and better understood state of affairs, where people are more equal (similar), more brotherly responsible for each other (more able to keep each other to the norm), and free to define new (narrower) social norms. In this light it is not at all surprising that the French Revolution included a period called “the Reign of Terror” in which all perceived opposition to the revolution was punished at the guillotine. It was the period of about a year in a highly chaotic revolutionary decade in which intolerance peaked.

Yet the intolerance to diversity of anxious authoritarians is a normal coping re- sponse to a situation of which the complexity has developed out of coping capacity of some fraction (per definition the authoritarian fraction) of the population. It is their good and democratic right to do something about a situation that they perceive as highly troublesome. The problem is that their understanding of society, compared to the libertarian fraction, is lower and this may easily lead to the adoption of sub- optimal or counterproductive strategies. Yet, the feelings of inadequacy and anxiety that authoritarians share and that unite them are genuine and these deserve to be taken very seriously. Ideally they should not be ignored or derided by libertarians, although they neither share nor understand their outlook on reality.

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242 T. C. Andringa

The more pervasive and deeper understanding of libertarians should allow them to understand authoritarians much better than vice versa. This entails that the libertarian fraction of society, at least in principle, holds the keys to the way the authoritarian dynamic will play out. Libertarians can influence the perceived complexity of so- ciety through coaching, education, and media and they can in some cases respond adequately to the threats perceived and moral decline experienced by authoritarians. Simply taking these seriously and addressing the root causes may result in a society in which considerably less people are in the authoritarian mode. In such a society many more people feel adequate because they are adequate social actors. The ensu- ing equality in personal adequacy ensures that most are in the pervasive optimization mode. This equality enhances overall wellbeing (Wilkinson 2006; Wilkinson and Pickett 2009) and it minimizes the probability of a concerted action by authoritar- ians to overthrow the (morally depraved) status quo in favor of a simpler, but also more regimented and less free society.

11.3.6 The Bureaucratic Dynamic

I will now come to the core and title of this chapter. How to formulate the psycho- logical enablers of bureaucracy most succinctly? If bureaucracy is a manifestation of authoritarianism, i.e., the prevalence of the coping mode of thought, within professional organizations, something similar to the Authoritarian Dynamic or the “Ignorance Dynamic” should hold. Of course it must be adapted to the particular context of professional organizations.

My proposal, as variant of the Ignorance and Authoritarian Dynamic, for a “Bureaucratic Dynamic” is as follows:

Incentive for coercive formalization = Bureaucracy incentive = Institutional ignorance × Worker cost of failure

In this “formula” the role of “intolerance” and “motivation to restore personal adequacy” is played by either the “Incentive for coercive formalization” or the “Bu- reaucracy incentive” as described by Adler and Borys (1996) and summarized in the left column of Table 11.2. The role of “Authoritarianism” and “Ignorance” is played by “Institutional ignorance.” This is a measure of how well workers understand the consequences of their own actions, both within the organization and on the wider society. Directly associated is their need (often a demand) for guidance in every non- standard activity. The role of “Threat” and “Cost of failure to act appropriately” is played by “Worker cost of failure.” In the case of bureaucracy, the threat is not moral decline, but failing at the job and publicly being revealed as professionally inade- quate. This threat pertains as much to the worker making the mistake, as it does to the superior who will be shamed because (s)he did not have the department under control.

Here, again, we have a combination of two factors: (1) institutional ignorance leads to an abundance of opportunities to fail and (2) (high) cost of failure. The

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11 The Psychological Drivers of Bureaucracy 243

prevalence and seriousness of failure now becomes the measure of personal inade- quacy. As in the “Ignorance Dynamic” the right side of the Bureaucratic Dynamic corresponds to deep feelings of personal inadequacy that “can only be improved through the realization of a more manageable world,” which, according to the logic of the coping mode, is through coercive formalization.

The Bureaucratic Dynamic, formulated like this, explains both the basic attitude and all the key properties of coercive formalization so characteristic of bureaucracy (see Table 11.2). Workers are seen as sources of problems to be eliminated, and opportunism of (“fools” as) workers is to be feared. A formal system of complex procedure and guidelines—all strengths of the coping mode of cognition—replaces worker’s intelligence, skills, and improvisation ability. Deviations from protocol become suspect. To prevent the natural tendency of workers to use their “good sense and sound judgment” the whole organization is made nontransparent and whatever global transparency exists is made highly asymmetrical so that superiors at any moment can, but not necessarily do, monitor workers so that workers self-impose limits on their behavior.

In this process capable workers loose their intrinsic motivation (“The job is no longer fulfilling and it impedes my personal development”) and identified motivation (“The job is no longer important and its results not satisfying”). These motivations are replaced by introjected motivation (“I’d better do it otherwise I’ll face unpleasant consequences”) and external motivation (“I have no choice,” “The protocol says so,” “The computer says so,” Befehl ist Befehl). Quickly enough this state of being be- comes habitual. The result is an individual that while at work, has shut down half of its intellectual potential, is stuck in a situation with minimal personal growth potential, and is reduced to an automaton-like shadow of a fully functioning human being.

11.3.7 The Psychological Effects on the Bureaucrat

While bureaucracy is annoying and frustrating for the client and costly for society, its effects might be worse for the bureaucrat. Compared to a worker in a non-bureaucratic organization, the bureaucrat misses many opportunities to engage in inherently fulfill- ing activities, to enjoy meaningful activities, to help others, to contribute undeniably to a better society, and in general to give meaning and significance to life.

What happens to the bureaucrat if these high-level human needs cannot be compen- sated in the rest of life? What level of life quality will result? Somewhat alarmingly, the pattern of these effects resembles those of torture. For example, torture victim therapist Leanh Nguyen (2007) concludes the following:

The most terrible, and intractable, legacy of torture is the killing of desire—that is, of curiosity, of the impulse for connection and meaning making, of the capacity for mutuality, of the tolerance for ambiguity and ambivalence.

This description sounds eerily similar to the description of someone indefinitely locked in the coping mode of cognition. This quote describes a complete inability to experience curiosity, joy, play, and interpersonal contact. An inability for playful

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244 T. C. Andringa

sensemaking associated with intrinsic motivation. This is replaced by a constant need for the certainty and formal clarity. It is as if the pervasive optimization mode has become inaccessible. Does not this resemble the automaton like bureaucrat in the introductory example?

This comparison between bureaucracy and torture might, at first glance seem a bit over the top, but remember that both are about subduing the individual to external authority (just as slavery by the way). Individual autonomy may well be a defining characteristic for health (seeAndringa and Lanser (2013) for the role of freedom over mind-states in sound annoyance). From that perspective it makes sense to consider bureaucracy as a low, but prolonged, level of psychological torture that like full- blown torture, may have a profound and long lasting influence on bureaucrats and by extension on society. As far as I know, this topic has not deserved the attention it should have.

11.3.8 Summary of the Psychological Roots of Bureaucracy

In Sect. 2, the psychological enablers of bureaucracy, I have progressively developed the psychological foundations of bureaucracy by addressing a number of comple- mentary perspectives from different psychological specialisms. I will summarize its main results here.

Step one involved the notion of habits. During habitual behavior it is the envi- ronment that drives behavior. Habits free the higher faculties of mind during routine tasks and have as such great benefits. If, however, the use of the higher faculties of mind is discouraged at work, the result is something of an automaton: a half- empty human shell performing routine tasks, but devoid of compassion, empathy, and understanding.

In the section called “Two modes of thought” I showed that (proto)typical bu- reaucratic behaviors fit perfectly with the coping mode of cognition (Table 11.3). The coping mode is characterized by intelligently solving self-contained problems, while the pervasive optimization mode is characterized by ever-improving one’s un- derstanding of the diversity of the world. This leads to two different attitudes toward “authority.” For the coping mode some (typically external) authority must limit and constrain the world so that one’s existing solution repertoire can be applied. In the pervasive optimization mode the individual internalizes the role of authority and be- comes progressively more self-deciding and autonomous as understanding becomes more pervasive and deep.

To study the interplay between authority and understanding, I discussed the phenomenon of authoritarianism as defined by Stenner (2005). This led to the identi- fication of two attitudes toward the world: the libertarian attitude in which the world is full of possibilities and an authoritarian attitude in which a lack of understanding of the world leads to anxiety and feelings of personal inadequacy of which liber- tarians are generally unaware. These feelings unify and motivate authoritarians to

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11 The Psychological Drivers of Bureaucracy 245

oppose all sources of complexity, unpredictability, novelty, and growth that complex- ify, confuse, and destabilize a predictable state of affairs. This drives encroaching bureaucracy.

The emergence of intolerance to (ill-understood) diversity has been summarized in the “Authoritarian Dynamic” in which “intolerance” scales with the level of ig- norance (authoritarianism) and “threat-level” as a measure of the significance of not understanding one’s world. Together these lead to a sense of personal inadequacy. The strength of the coping mode’s “intelligent” ways of treating all problems as self-contained (such as the problems in an IQ-test) leads authoritarians to redefine or ignore reality until it fits with their current solution repertoire.

The digression into political psychology allowed the formulation of a “Bureau- cratic Dynamic.” The role of intolerance to diversity is apparent as the prominence of coercive formalization. Conform the Authoritarian Dynamic, this scales with the product of “institutional ignorance” and “worker cost of failure.” Public shaming in case of failure is a measure of worker’s inadequacy as a professional and the man- ager’s inadequacy both as a leader and as a person. This leads, again according to the logic of the coping mode, to the worker accepting (or demanding) and the manager instilling more coercive formalization.

However, in this process workers lose their intrinsic motivation and become grad- ually more extrinsically motivated and the work becomes more and more habitual. The workers have shut down half of their intellectual potential and are stuck in a situation with minimal personal growth potential.

This then, finally, leads me to question whether the psychological effects on bureaucracy on bureaucrats might be an ignored, yet imminently important, psy- chological and societal problem. The third and last section of this chapter will not focus on this problem, but on how the societal goals of organizations can be pro- tected from bureaucracy. Fortunately this may also protect workers from the (likely) adverse effects of bureaucracy.

11.4 Protecting the Societal Goals of an Organization

The subtitle of this chapter is “Protecting the societal goals of an organization.” This section addresses this topic for nonprofit organizations because these have a social mission. In the introductory example the original societal role of the lost-and- found department was replaced by a new goal: procedural correctness, irrespective of the state of the world and the implications of following the procedure. As I have outlined in the previous section this is the result of the coping mode running amok in an organization conform the “Bureaucratic Dynamic.” This entails that this section will firstly address a number of management paradigms in relation to their societal goals and bureaucracy, secondly it describes core features of non-bureaucratic or libertarian organizations, and thirdly it formulates safeguards against encroaching bureaucracy. This chapter ends with some reflections and conclusions.

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11.4.1 Management Paradigms for Nonprofits

As summarized at the end of the previous section, the psychology of not understand- ing one’s world and in particular ignorance about one’s working environment and not overseeing the consequences (both adverse and beneficial) of one’s activities leads to encroaching bureaucracy through the generation of more self-centered goals of complexity reduction that progressively erode the focus on the original societal goals of an organization. For nonprofit organizations, of which the mission aims at the achievement of social purposes rather than in generating revenues, this entails that they gradually delegitimize themselves through making their own stability and survival more important than their original social raison d’être (Moore 2000). Yet depending on the management paradigm, nonprofits run this risk to varying degrees.

Stoker (2006) describes and summarizes three management paradigms that neatly fit a progression from organizations based on coping mode rationality to the rationality of the pervasive optimization mode. I will describe all three.

11.4.1.1 Traditional Public Management

Traditional public management follows the typical Weberian early twentieth-century template (Weber 1978) in which bureaucracy delivers organizational effectiveness through four features that Stoker (2006) summarizes as follows:

The first is the placing of officials in a defined hierarchical division of labor. The central feature of bureaucracy is the systematic division of labor whereby complex administrative problems are broken down into manageable and repetitive tasks, each the province of a particular office. A second core feature is that officials are employed within a full-time career structure in which continuity and long-term advancement is emphasized. Third, the work of bureaucrats is conducted according to prescribed rules without arbitrariness or favoritism and preferably with a written record. Finally, officials are appointed on merit. Indeed they become experts by training for their function and in turn control access, information, and knowledge in their defined area of responsibility.

The italic emphasis has been added to indicate concepts arising from the logic of the coping mode.

11.4.1.2 New Public Management

New public management arose as an alternative to the observation “that public ser- vice organizations tend to be neither efficient in terms of saving public money nor responsive to consumer needs” (Stoker 2006). As a result it did not arise from posi- tive motivations, but as a solution to the problems of bureaucracy. Stoker describes this as follows.

The solution is to fragment monopolistic public service structures and develop incentives and tools to influence the way that they operate. Key reforms include the introduction of a purchaser-provider divide within organizations and the development of performance targets

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and incentives. The aim is to create an organizational home for the client or consumer voice within the system to challenge the power of producers. Consumers or their surrogate representatives, commissioners, would have the power to purchase the services they required and measure performance. The achievement of better performance would be aided by arms- length systems of inspection and regulation to check not only the spending of public money but also the delivery of public services against demanding targets.

New public management then seeks to dismantle the bureaucratic pillar of the Weberian model of traditional public administration. Out with the large, multipurpose hierarchical bu- reaucracies, new public management proclaims, and in with lean, flat, autonomous organiza- tions drawn from the public and private sectors and steered by a tight central leadership corps

So the key improvement compared to the traditional model is the explicit role and importance of the societal function of the organization, but in this case limited to specific performance targets to be delivered by lean, flat, and autonomous organiza- tions of which the performance indicators are still fully under control of some sort of central leadership that is supposed to represent public and private sector interests.

New public management is clearly aware of important drawbacks of Weberian bureaucracy, yet it is still guided by the logic of the coping mode. However, it has some indicators of the pervasive optimization mode such as greater worker autonomy (within the tight constraints of performance indicators) and some, albeit indirect, representation of consumers and other beneficiaries of the delivered services.

11.4.1.3 Public Value Management

Public value management (Moore 2000) is an emerging new management paradigm that is not so much a response to an existing paradigm but a formulation of the role of nonprofits in modern society (Stoker 2006). Public value management is succinctly formulated as a public value scorecard (Moore 2003) in which an organization should balance (1) the public value produced by the organization, (2) the legitimacy and support enjoyed by the organization, and (3) the operational capacity to achieve its results. In the public value scorecard the performance indicators are translated as measures of performance. Moore (2003) describes these as follows.

Some of the measures are those we associate with the public value produced by the organization—the extent to which it achieves its mission, the benefits it delivers to clients, and the social outcomes it achieves.

Others are associated with the legitimacy and support enjoyed by the organization—the extent to which “authorizers” and “contributors” beyond those who benefit from the organization remain willing to license and support the enterprise. These measures can, to some degree, be viewed as important because they indicate the capacity of the organization to stay in operation over time. But these measures can also be viewed to some degree as measures of value creation in themselves. This is particularly true if we recognize that some part of the value created by nonprofit organizations lies in the opportunities it affords to public spirited individuals to contribute to causes they care about, and another part lies in the capacity of the nonprofit organization to link contributing individuals to one another in a common effort to realized shared social goals.

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Still others are associated with the operational capacity the nonprofit organization is relying on to achieve its results. This includes not only measures of organizational output, but also of organizational efficiency and fiscal integrity. It also includes measures of staff morale and capacity, and the quality of the working relationships with partner organizations. And, it includes the capacity of the organization to learn and adapt and innovate over time.

Where the Weberian bureaucracy follows the logic of the coping mode, these mea- sures read as the pervasive optimization mode specified to the context of nonprofit organizations.

The public value scorecard was a response to an earlier score card: Kaplan’s (Kaplan and Norton 1996) Balanced Scorecard, for the new public management paradigm through its focus on financial and efficiency measures. The Public Value Scorecard differs in a number of central aspects that are characteristic of the pervasive optimization mode. Moore (2000) formulates these differences as follows.

First, in the public value scorecard, the ultimate value to be produced by the organization is measured in non-financial terms. Financial performance is understood as the means to an end rather than an end itself. The end in itself is denominated in non-financial social terms. It also notes that the value produced by the organization may not lie simply in the satisfaction of individual clients. It can lie, instead, in the achievement of desired aggregate social outcomes of one kind or another.

Second, the public value scorecard focuses attention not just on those customers who pay for the service, or the clients who benefit from the organization’s operations; it focuses as well on the third party payers and other authorizers and legitimators of the nonprofit enterprise. These people are important because it is they who provide some of the wherewithal that the organization needs to achieve its results, and whose satisfaction lies in the achievement of aggregate social states as well as in the benefits delivered to individual clients.

Third, the public value scorecard focuses attention on productive capabilities for achieving large social results outside the boundary of the organization itself. Other organizations ex- isting in a particular industry are viewed not as competitors for market share, but instead as partners and co-producers whose efforts should be combined with the effort of the nonprofit enterprise to produce the largest combined effect on the problem that they are jointly trying to solve. In short, a nonprofit organization should measure its performance not only by its ability to increase its market share, but also by its ability to strengthen the industry as a whole.

Again I have added emphasis in italic to stress some the core concepts of this ap- proach. The reader can combine these with the italic remarks in the right column of Table 11.3 that interprets the strong points of the pervasive optimization mode in organizational terms. It will be clear that this description matches the properties of the pervasive optimization mode.

Stoker (2006) concludes that public value management rests “on a fuller and rounder vision of humanity than does either traditional public administration or new public management.” He identifies a key difference, namely the role of motivation, when he concludes:

Ultimately, the strength of public value management is seen to rest on its ability to point to a motivational force that does not solely rely on rules or incentives to drive public service practice and reform. People are, it suggests, motivated by their involvement in networks and partnerships, that is, their relationships with others formed in the context of mutual respect and shared learning. Building successful relationships is the key to networked governance and the core objective of the management needed to support it.

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In terms of motivation, the public value management relies on the power of identified (“I find it important”) and intrinsic (“I enjoy doing it”) motivation. And this can be contrasted to the bureaucratic extreme in which the motivations are mainly extrin- sic (“I have no choice”) or introjected (“I’d better do it otherwise I face negative consequences”). The positive motivations are associated with (not only) experiential learning (Andringa et al. 2013; Vygotskiı̆ 1978) and the growth of organization un- derstanding, which is, conform the Bureaucratic Dynamic, the key protector against institutional ignorance.

11.4.1.4 Summarizing Key Properties of the Three Management Paradigms

Table 11.5 provides a summary, adapted from Stoker (2006), to which I have added six rows describing properties in terms of the strengths of the coping and the pervasive optimization mode.

11.4.2 Libertarian Organizations

Until now I have focused mostly on bureaucracy and the personal, organizational, and societal manifestations of the coping mode. But how does the pervasive optimization mode manifest itself in the context of organizations? Stoker (2006) notes that for public value management to work the motivation of workers needs to be “intrinsic” or “identified,” which complies with the organic organization type identified by Adler and Borys (1996). Alternatively one might call organizations that realize this “Libertarian organizations” because the members are dominated by intrinsic and identified motivation, understand what they are doing, are autonomous self-deciders, and, in summary, rely mostly on the pervasive optimization mode of cognition.

Organizational structures that effectively contribute to an ever-changing real world of dangers and opportunities need flexible access to the available competence and en- thusiasm. Libertarian organizations must therefore match the available competences and institutional understanding to whatever the world demands of the organization. Where authoritarian organizations realize (at best) proscribed results and predictable mediocrity, libertarian organizations can realize personal growth, institutional ex- cellence, and with that effective contributions to the wider society. They are truly optimizing pervasively.

In libertarian organizations the formal hierarchy is as important as in a bureau- cracy, but its role is quite different: it has to manage autonomy instead of enforcing compliance. For superiors who know how to manage motivations and how to con- vey the role of the organization in society, this is not at all demanding because the very autonomy and commitment of a healthy libertarian organization ensures that it can deal with stability (where efficiency and organizational optimization are priori- ties) and change (where protection of quality and the realization of opportunities are prominent).

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Table 11.5 Management paradigms. (Adapted from Stoker (2006), which is based on Kelly et al. (2002). The lowest 6 rows have been added as interpretations of the original table in terms of the discourse of this chapter.)

Key objectives Traditional public ad- ministration

New public manage- ment

Public value manage- ment

Role of managers Politically provided in- puts; services moni- tored through bureau- cratic oversight

Managing inputs and out- puts in a way that ensures economy and responsiveness to con- sumers

The overarching goal is achieving public value that in turn involves greater effectiveness in tackling the problems that the public most cares about; stretches from service delivery to system maintenance

Definition of public interest

To ensure that rules and appropriate procedures are followed

To help define and meet agreed performance tar- gets

To play an active role in steering networks of de- liberation and delivery and maintain the overall capacity of the system

Approach to public service ethos

By politicians or ex- perts; little in the way of public input

Aggregation of individ- ual preferences, in prac- tice captured by senior politicians or managers supported by evidence about customer choice

Individual and public preferences produced through a complex pro- cess of interaction that involves deliberative reflection over inputs and opportunity costs

Preferred system for service delivery

Public sector has monopoly on service ethos, and all public bodies have it

Skeptical of public sec- tor ethos (leads to in- efficiency and empire building); favors cus- tomer service

No one sector has a monopoly on public service ethos; main- taining relationships through shared values is seen as essential

Contribution of the democratic process

Hierarchical depart- ment or self-regulating profession

Private sector or tightly defined arms-length public agency

Menu of alternatives selected pragmatically and a reflexive ap- proach to intervention mechanisms to achieve outputs

Interpretation in terms of cognitive modes

Typical of the coping mode

Aware of limitations of the coping mode

Transition to the perva- sive optimization mode

Role of worker Skilled obedience Responsible for as- signed tasks and maintaining skills. Customer oriented

Co-responsible for societal role execution and the adaptation of the organization’s changing societal demands

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Table 11.5 (continued)

Key objectives Traditional public ad- ministration

New public manage- ment

Public value manage- ment

Skills of worker Precision in role exe- cution (aimed at error prevention)

Deep understanding of tasks and role skills

Deep understanding of role skills and broad un- derstanding of impact of own activities on public value

Motivations Extrinsic and introjected

Extrinsic, introjected, identified, and intrinsic. Role of motivation not central

Identified and intrinsic. Essential role of moti- vation

Attitude to work Obedient and unengaged

Professional development

Personal development

In healthy libertarian organizations everyone develops in terms of (institutional) understanding. This entails that eventually everyone can “play” a diversity of formal and functional roles. Basically the only real requirements for a healthy libertarian organization is that everyone in the organization has roles that are often intrinsically motivating, are generally satisfying, and that do not exceed understanding capacity. An organization that satisfies these conditions will remain in a pervasive optimization mode, even in the face of great organizational or societal challenges.

Table 11.6 provides a selection of properties of libertarian organizations formu- lated to promote the pervasive optimization mode in organizations.

11.4.3 The Dynamics of Encroaching Bureaucracy

We have probably all been members of a team that functioned amazingly well for a time, but then started to dysfunction and eventually disintegrated. This is because excellence is fragile: it not only delivers pervasive optimization, but also depends on it. In his analysis of how twentieth-century (American) bureaucrats took over educa- tion from teachers, Labaree (2011) describes how the “pedagogically progressive” vision of education—child-centered, inquiry based, and personally engaging—is a fragile hot-house flower because it depends on broadly realized favorable conditions (i.e., successful pervasive optimization). In contrast, the “administrative progres- sive” vision of education is a weed because it grows under difficult conditions such as erratic funding, poorly prepared teachers, high turnover, dated textbooks, etc. It is robust “because its primary goal is to be useful in the narrowest sense of the term: It aims for survival rather than beauty.”

Labaree accounts a “battle” between the philosopher John Dewey and educational reformer David Snedden. As proponent of the pedagogically progressive vision, John Dewey formulated a complex and nuanced narrative of education as a means to make “workers the masters of their own industrial fate.” In contrast, David Snedden as the champion of the administrative progressive approach, saw education as vocational

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Table 11.6 Properties of libertarian organizations. Intended to stimulate the pervasive optimization mode of cognition

Topic Property

Vision A “lived” vision of the goals and roles of the organization is widely shared. It allows everyone in the organization to contribute to its realization via well-formulated procedures and competent improvisation alike

Approach the organization holistically: optimize everything in context of the whole; prevent at all cost strict compartmentalization of responsibilities and information, because specialism and other forms of close-mindedness are seeds of stagnation and corruption

Motivation Promote and ensure a predominance of intrinsic and identified motivations

Allow people to be happy or enthusiastic about what they have done well and allow them to repair and learn from mistakes

Competences Focus on pervasive competence development

Promote a deep insight in the societal effects of individual work and the organization as a whole

Stimulate overlapping competences to ensure organizational redundancy, optimization opportunities, more timely services, and enhanced work satisfaction

Distribute responsibilities according to available competences, interests, ambitions, and enthusiasm. Ignore hierarchical considerations

Be alert of indications of low competence, stagnated development, insensi- tivity to adverse consequences of (in)action, low inherent motivation, low commitment to the organization and the services it should provide (e.g., 9-to-5 mentality), and indicators of lack of enthusiasm

Autonomy The task of management is to manage worker autonomy

Competent autonomy of workers is success indicator

Put real responsibility in every job description and allow a diversification or responsibilities as competence grows

Stimulate expertise, but prevent specialization

Information Develop an open information infrastructure

Allow for ample opportunities for unstructured information sharing

The Scottish proverb “When the heart is full the tongue will speak” will ensure that really important information will be shared

training in preparation for a life of servitude. As a narrow-minded authoritarian, he understood the world in dualisms and countered nuanced arguments by ignoring them and by repeating reasonable sounding dogma. Labaree (2011) concludes:

Therefore, the administrative progressive movement was able to become firmly established and positioned for growth because of Snedden’s flame throwing. Put another way, a useful idiot, who says things that resonate with the emerging ideas of his era and helps clear the ideological way for the rhetorical reframing of a major institution, can have vastly more influence than a great thinker, who makes a nuanced and prescient argument that is out of tune with his times and too complex to fit on a battle standard.

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This is how authoritarians gain control. Not by the quality of argument, but by fo- cusing the discussion, by subtly reinterpreting the goals of an organization in a less rich manner, by ignoring nuances or replacing them by similar sounding opposi- tions, and by gradually marginalizing and deriding opposition. When authoritarians have gained control they start simplifying, harmonizing, focusing, and reorganizing the organization according to Billington’s (1980) observations on revolutions. The rhetoric is a convenient tool. But the real objective, albeit rarely acknowledged, is a simpler, more controlled, and better understood world. Authoritarians bring the complexity of the world, or in this case national education, down to their level of understanding of it.

This process matches the Bureaucratic Dynamic that we have formulated.

Incentive for coercive formalization = Bureaucracy incentive = Institutional ignorance × Worker cost of failure

The true drivers of the bureaucratization process are feelings of personal inadequacy among workers. In the case of educators like Snedden, these feelings arose from being lost in the complex world of education in which responsibilities are unclear and the means to realize them even more. The resulting personal anxiety motivates workers to reestablish their sense of adequacy whenever possible: at work they are now in an authoritarian mode. Their colleagues who do understand their responsibilities and know how to realize them feel no anxiety. They are and remain in a libertarian mode and are generally unaware of the severity of the anxiety in their (now) authoritarian colleagues.

The authoritarians gravitate toward each other and start to formulate and promote a simplified understanding of the roles and aims of the organization. The libertar- ian opposition against this simplified understanding is of course based on a fuller understanding of the roles and aims of the organization. But these arguments have no impact on the authoritarians because, in their eyes, the arguments are addressing irrelevancies with no relation of their new, simplified, and more tangible understand- ing of the organization’s scope and aims. While the libertarians waste their time and energy with progressively more nuanced arguments, the authoritarians find each other and may at some point take control over the organization.

When they do, they make their level of “institutional ignorance” the norm. And because they are in the coping mode they will realize this norm according to the logic of the coping mode (Table 11.3, left side). This will, according to the Bureaucratic Dynamic, lead to the introduction of more “coercive formalization” and a shift from being as professional as possible to producing tangible measureable outcomes and preventing errors in realizing these. Preventing worker failure and publicly displayed inadequacy becomes more important than professional success.

At the same time the libertarians in the organization discover that many of the things they used to do—and which still make sense given the logic of the pervasive optimization mode—are no longer officially endorsed because they are incompatible with the new simplified norm. In fact the old way of working has become a liability if it hinders the realization of the new, more tangible, performance measures. What used to be the highest indicators of professionalism, are now costly ways to fail as a worker. The new professionalism is rule compliance and not organizational excellence.

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Much of what the libertarian worker motivated, is no longer officially or practically part of the organization’s core business. The moment the new management initiates a reorganization, of course according to the logic of the coping mode, the libertarians are faced with a dilemma: leave with professional dignity or succumb to the new normal and deskill and comply. Whatever the libertarian chooses, the result is the same: increased institutional ignorance.

11.4.4 Preventing Bureaucracy

I had the doubtful honor to witness such a process in my university. Only two individ- uals at key positions in the hierarchy drove the process. Fortunately, it was followed by repair measures when the whole process overshot and the organizational costs became too high. This happened after some highly skilled and motivated colleagues had left and others were on sick-leave. At that point workers simply refused to take further responsibility and the department almost stopped functioning. This paper is informed by witnessing this process. Without understanding bureaucracy as well as I do now, the unfolding process was very difficult to counter. Yet it is possible to devise effective protective measures. In Table 11.7 I have formulated a number of “Red Flags” as indicators of encroaching bureaucracy that may be helpful to stop a bureaucratization process before it becomes self-reinforcing.

According to the Bureaucratic Dynamic, the best protection against bureaucratiza- tion is preventing worker (including management) ignorance and promoting worker professionalism instead of preventing worker error. A truly healthy and resilient organization maintains a sufficient level of institutional understanding and worker autonomy so that no one feels inadequate and every one contributes to the realization of the organizations full societal goals and not only to a single or a few “key perfor- mance objectives.” Yet as the analysis of the three management paradigms shows, institutional understanding improves over time. For example, the new public value management paradigm starts from the logic of the pervasive optimization mode in- stead of the logic of the coping mode as would have been the natural Weberian option a century ago.

A future informed public might not accept the products of a bureaucratic organiza- tion because it demonstrates, for all to witness, that its management and workers do not quite understand what they are doing. In addition, if my expectation is substanti- ated that bureaucracy leads to high personal and societal costs for bureaucrats, future societies might simply not accept bureaucracy because it signifies a pathological state of affairs of which the immediate costs are apparent as reduced quality and efficiency, while the full personal and societal costs are deferred to future generations. In fact sustainability arguments might drive this.

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Table 11.7 Red flags. Early indicators disrupting the pervasive optimization mode

Red Flags

Mission The absence of a shared, living vision about the organization’s goals in a larger societal context

The advance of a simplified and more focused interpretation of the organizations mission, typically as a limited number of “key performance objectives”

Leaders Leaders insensitive to reasoned and nuanced arguments by competent individuals at any position in the organization

Leaders only sensitive to arguments related to goal achievement or procedure. Realizable goals are preferred over desirable goals

Leaders preferring obedience over autonomy and who curtail work-floor auton- omy

Bureaucrats promoted to key positions

Competences Neglect of work-floor competences

Demotivation of highly autonomous, competent and committed co-workers

Gradual deterioration of quality of the working environment and worker motivation

The most competent and committed coworkers leave

Standardization at the cost of curtailing of essential/useful diversity

Uniformization Strong focus on formalities while neglecting (or indefinitely) postponing content

Compartmentalization of information and plans

Mediocracy facilitated

11.4.5 Conclusion and Reflection

In some sense this chapter is about the difference between intelligence and under- standing as manifestations of, respectively, the coping and the pervasive optimization mode of cognition. Understanding proofs itself as the ability to set up, in a statistical sense, the conditions for an unproblematic future and an interesting and fulfilling life. Failure to do so leads to problematic situations to be solved intelligently. While understanding shines in an open world, intelligence assumes a closed world of self- contained problems to be addressed with an existing solution repertoire. Anything in the way of the solution will be ignored or coercively made irrelevant. While un- derstanding manifests itself through fostering empathic relations, intelligence, as a last line of defense, is self-protective, impersonal, and ruthless.

Without understanding the consequences of one’s activities, work is bureaucratic. Since no one is bureaucratic while not at work and especially not among friends, it is the work environment that activates bureaucratic behavior. In this chapter, I have shown that bureaucracy in all it facets can be understood from basic psychology. Bureaucratic behavior is habitual or intelligent rule following. The bureaucrat obe- diently performs activities that it understands superficially and values marginally, but that it does not endorse or feels responsible for. As such the bureaucrat appears and acts as a dehumanized automaton. It is a pitiful state of being.

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The psychological enabler of bureaucracy is a sense of personal inadequacy among workers resulting from not understanding their work and its consequences. This ac- tivates the coping mode of cognition and with that an urge to bring the complexity of the working environment down to more manageable levels through promoting coercive formalization. This process can be summarized as the Bureaucratic Dy- namic, which states that the prevalence of coercive formalization depends on the combination of “Institutional Ignorance” and “Worker cost of failure.”

Fortunately, in the last century society became gradually more aware of the effects and dangers of bureaucracy. New public management arose as a response to curtail the adverse effects of Weberian bureaucracy as defining aspect of traditional public management. Because it is still based on the coping mode of cognition it will not become a bureaucracy-free alternative. New value management however arises from the logic of the pervasive optimization mode and it has the potential to achieve organizational excellence without bureaucracy.

Anti-bureaucratic measures should not only focus on the reduction of the number of rules and regulations because this still follows the logic of the coping mode. It should instead focus on motivating workers to understand their professional roles and to learn to oversee the impact of their activities; not only on the organization, but also on the wider society. This understanding will lead to a reevaluation of the role of formalization and will erode the need for coercive formalization. The organization will no longer focus on preventing errors, but on optimizing the multifaceted societal roles of the organization in ways that are experienced as important, worthwhile, and intrinsically motivating for its workers. Yet organizations that function like this are somewhat fragile and may be eroded from the inside by a fraction of workers that still have an impoverished understanding of the organization and its societal roles. It will be important to develop safeguards to prevent this.

Current anti-bureaucratic awareness stems from the observation that bureaucratic organizations are neither efficient in terms of saving public money nor responsive to consumer needs. Future research may however proof important adverse effects of bureaucracy on bureaucrats and on society as a whole. This may expose bureaucracy for what I think it is: a pathological state of human organization, with equally serious adverse consequences for the bureaucrat and society as a whole.

In the course of writing this chapter I was struck by the consistency and comple- mentarity of disparate scientific results. Science produces wonderful observations and generates deep insights, but it has difficulty in combining these if they originate from different domains. The transdisciplinary framework presented in this chap- ter allowed far reaching conclusions through the combination of a number of these observations and insights.

For example the work of Adler and Borys (1996) and especially their conceptual- ization of bureaucracy, in terms of the degree and type of formalization (enabling or coercive), gained theoretical support. The stability of bureaucracies can be explained through the link between bureaucracy and habitual behavior, since bureaucrats feel a self-imposed responsibility to maintain the condition in which their habitual func- tioning is guaranteed. Furthermore, McGilchrist’s (2010) description of the way the two brain hemispheres understand the world and our conceptualization of the

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pervasive optimization mode and the coping mode (Andringa et al. 2013), seems to predict how non-bureaucratic and bureaucratic organizations micromanage. This was a serendipitous finding that I consider highly relevant. In addition Stenner’s (2005) conceptualization of authoritarianism—as having a problem with a complex world (and not with complex thinking)—helped to understand the psychological motiva- tors of bureaucracy in terms of feelings of personal inadequacy. Finally, Billington’s (1980) observations about revolutions always aiming for simplicity, helped to under- stand why well-functioning non-bureaucratic organizations might be eroded from the inside and turn into a bureaucracy.

All in all, it seems to me that bureaucracy is not just a phenomenon that occurs in professional organizations. Instead it is just one of many manifestations of the interplay between understanding and intelligence that are important for every aspect of live.

Appendix

Some core properties of the bureaucratic syndrome (authoritarian dominated) and the non-bureaucratic syndrome (libertarian dominated organizations).

Topic Bureaucratic syndrome Non-bureaucratic syndrome

Key properties

Organizational goals Societal goals of the orga- nization are only adhered in name, but neither understood nor clearly implemented

Development of a broadly shared vision about the societal reason d’être of the organization and the way to realize it

Overall strategy Stimulating sameness and one- ness through standardization and obedience

Continual skilled improvisation on the basis of a shared vision and well-chosen procedures

Competence Ignoring, discouraging, and de- moralizing competent “subordi- nates.” Deskilling

Relying on and fostering all proven and budding competencies in the organization

Autonomy Subordinate autonomy is not an option. Obedience is more im- portant than competence

Autonomy and competence devel- opment of subordinates expected

Content Complete disregard of content while favoring form

Content is leading, form a means

Organizational development

Structures and procedures adapt to the lowest competence level

Everyone is expected to learn and grow towards autonomous roles in organization

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Topic Bureaucratic syndrome Non-bureaucratic syndrome

Main conflicts

Stability versus develop- ment

Stability and other forms of high predictability leading. This de- fines the organization

The workers in the organization are constantly developing their skills in order to improve all as- pects of the societal role of the organization (i.e., quality and ef- ficiency)

Form versus optimization Obsessed with form and for- malisms. Centralized optimiza- tion of standardized and nar- rowly defined responsibilities

Actively eliciting creative and de- centralized optimization of or- ganizational goals. Disregard of form when counter-productive

Standardization versus di- versity

Obsession with standardization and curtailing diversity, at the cost of quality if quality entails diversity

Concerned with the overall opti- mization of all work processes in context, of which both standard- ization and increasing diversity are options

Error versus learning Obsessed with preventing errors and mistakes. The organization redefines itself to produce what it can, not what it should; “race to the bottom”

Error and correction after error part of continual creative opti- mization of work processes

Short versus long term Exclusively short-term (form) oriented, neither care for nor un- derstanding of mid of long term goals. However, what is short- or mid-terms depends on the role in the organization

Optimization, by all workers. on all time-scales and all dimensions of success

Structural properties

Role of hierarchy Hierarchy formalized and in- flexible, based on assumed (but never fully checked) compe- tence of superiors

Hierarchy task dependent, and therefore flexible and competence-based

Perception of authorities Authorities never fundamentally questioned

Incompetent authorities not ac- cepted, but coached or dismissed

Locus of control Formation of stable authoritar- ian cliques, who take control over the institutional change processes to prevent further complexity

Loosely and varyingly linked lib- ertarians at control positions.

Measures of success Performance measures rede- fined to what is delivered

Performance measure based on what should be delivered (given reason d’être)

Accountability Suppression of all forms of ac- countability at the higher levels and prevention of errors and ret- ribution in case of error at the lower levels

Accountability part of normal in- stitutional learning and compe- tence building

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Topic Bureaucratic syndrome Non-bureaucratic syndrome

Emotions

Overall role Rationality and “objectivity” leading. Emotions treated as ir- relevant source of variation, to be suppressed

Central role of positive emotions (compassion, enthusiasm, inter- est) as key motivators; prominent negative emotions indicative of organizational failure

Emotion of workers Motivating emotion negative: activities guided by the fear of losing control or being shamed publically

Motivating emotion positive: ac- tivities aimed at realizing shared benefits including personal devel- opment

Emotions of co-workers Utter disregard of the feel- ings and emotional wellbeing of coworkers

Strong focus on the creation of op- timal working condition in which coworkers feel optimally moti- vated to give their best

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Moore MH (2003) The public value scorecard: a rejoinder and an alternative to “strategic per- formance measurement and management in non-profit organizations” by Robert Kaplan, 23. doi:10.2139/ssrn.402880

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doi:10.1080/10478400903028615 Stenner K (2009b) “Conservatism,” context-dependence, and cognitive incapacity. Psychol Inq

20(2):189–195 Stoker G (2006) Public value management a new narrative for networked governance? Am Rev

Public Adm 36(1):41–57. doi:10.1177/0275074005282583 Vygotskiı̆ LLS (1978) Mind in society: The development of higher psychological processes. In:

Cole M, John-Steiner V, Scribner S, Souberman E (Eds) Harvard University Press, London Weber M (1978) Economy and society. University of California Press, Berkeley Wilkinson R (2006) Why is violence more common where inequality is greater? Ann N Y Acad Sci

1036(1):1–12. doi:10.1196/annals.1330.001 Wilkinson R, Pickett K (2009) The spirit level: why more equal societies almost always do better.

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Routledge, Hillsdale, New Jersey

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Chapter 12 Active and Passive Crowdsourcing in Government

Euripidis Loukis and Yannis Charalabidis

Abstract Crowdsourcing ideas have been developed and initially applied in the private sector, first in the creative and design industries, and subsequently in many other industries, aiming to exploit the ‘collective wisdom’in order to perform difficult problem solving and design activities. It was much later that government agencies started experimenting with crowdsourcing, aiming to collect from citizens infor- mation, knowledge, opinions and ideas concerning difficult social problems, and important public policies they were designing for addressing them. Therefore, it is necessary to develop approaches, and knowledge in general concerning the efficient and effective application of crowdsourcing ideas in government, taking into account its special needs and specificities. This chapter contributes to filling this research gap, by presenting two novel approaches in this direction, which have been developed through extensive previous relevant research of the authors: a first one for ‘active crowdsourcing’, and a second one for ‘passive crowdsourcing’ by government agen- cies. Both of them are based on innovative ways of using the recently emerged and highly popular Web 2.0 social media in a highly automated manner through their application programming interfaces (API). For each of these approaches, the basic idea is initially described, followed by the architecture of the required information and communications technology (ICT) infrastructure, and finally a process model for its practical application.

12.1 Introduction

The capability of a large network of people, termed as ‘crowd’, networked through web technologies, to perform difficult problem solving and design activities, which were previously performed exclusively by professionals, has been initially recog- nized by private sector management researchers and practitioners, leading to the development of crowdsourcing (Brabham 2008; Howe 2008). Crowdsourcing ideas

E. Loukis (�) · Y. Charalabidis University of the Aegean, Samos, Greece e-mail: [email protected]

Y. Charalabidis e-mail: [email protected]

© Springer International Publishing Switzerland 2015 261 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_12

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262 E. Loukis and Y. Charalabidis

have been initially applied in the private sector, first in the creative and design in- dustries, and subsequently in many other industries, aiming to exploit the ‘collective wisdom’ (Surowiecki 2004) in order to perform difficult problem solving and design activities. This has resulted in the development of a considerable body of knowledge on how crowdsourcing can be efficiently and effectively performed in the private sector (comprehensive reviews are provided by Rouse 2010; Hetmank 2013; Ped- ersen et al. 2013; Tarrell et al. 2013). It was much later that government agencies started experimenting with crowdsourcing, aiming to collect from citizens informa- tion, knowledge, opinions and ideas concerning difficult problems they were facing, and important public policies they were designing, through some first ‘citizensourc- ing’ initiatives (Hilgers and Ihl 2010; Nam 2012). So there is still limited knowledge on how crowdsourcing can be efficiently and effectively performed in the special context of the public sector, much less than in the private sector. Therefore, exten- sive research is required for the development of approaches and methodologies for the efficient and effective application of crowdsourcing ideas in government for sup- porting problem solving and policy making, taking into account its special needs and specificities. This is quite important, taking into account that social problems have become highly complex and ‘wicked’, with multiple and heterogeneous stakehold- ers having different problem views, values and objectives (Rittel and Weber 1973; Kunzand Rittel 1979); previous research has concluded that information and com- munications technology (ICT) can be very useful for gaining a better understanding of the main elements of such problems (e.g. issues, alternatives, advantages and dis- advantages perceived by various stakeholder groups; Conklin and Begeman 1989; Conklin 2003; Loukis and Wimmer 2012).

This chapter contributes to filling this research gap, by presenting two approaches in this direction, which have been developed through extensive previous relevant research of the authors: a first approach for ‘active crowdsourcing’ (in which govern- ment has an active role, posing a particular social problem or public policy direction, and soliciting relevant information, knowledge, opinions and ideas from citizens), and a second one for ‘passive crowdsourcing’ (in which government has a more pas- sive role, collecting and analyzing content on a specific topic or public policy that has been freely generated by citizens in various sources, which is then subjected to sophisticated processing). Both of them are based on innovative ways of using the recently emerged and highly popular Web 2.0 social media in a highly automated manner through their application programming interfaces (API) (which are libraries provided by all social media, including specifications for routines, data structures, object classes, and variables, in order to access parts of their functionalities and incorporate them in other applications).

In particular, the first of them is based on a central ICT platform, which can pub- lish various types of discussion stimulating content concerning a social problem or a public policy under formulation to multiple social media simultaneously, and also collect from them data on citizens’ interactions with this content (e.g. views, ratings, votes, comments, etc.), both using the API of the utilized social media. Finally, these interaction data undergo various types of advanced processing (e.g. calculation of analytics, opinion mining, and simulation modelling) in this central system, in order to exploit them to support drawing conclusions from them. This approach has been

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developed mainly as part of the research project PADGETS (‘Policy Gadgets Mash- ing Underlying Group Knowledge in Web 2.0 Media’—www.padgets.eu), which has been partially funded by the European Commission.

The second passive crowdsourcing approach is based on a different type of cen- tral ICT platform, which can automatically search in numerous predefined Web 2.0 sources (e.g. blogs and microblogs, news sharing sites, online forums, etc.), using their API, for content on a domain of government activity or a public policy under formulation, which has been created by citizens freely, without any initiation, stim- ulation or moderation through government postings. Through advanced processing and analysis of this content in the above platform (using opinion and argument ex- traction, sentiment analysis and argument summarization techniques), conclusions can be drawn concerning the needs, issues, opinions, proposals and arguments of cit- izens on this domain of government activity or public policy under formulation. This approach is developed as part of the research project NOMAD (‘Policy Formulation andValidation through Non-moderated Crowdsourcing’—www.nomad-project.eu/), which is partially funded by the European Commission.

The two approaches presented in this chapter combine elements from management sciences (concerning crowdsourcing approaches), political sciences (concerning wicked social problems) and technological sciences (concerning social media ca- pabilities and API), in order to support problem solving and policy-making activities of government agencies. We expect that the findings of this research will be in- teresting and useful to both researchers and practitioners of these three disciplines who are dealing with the public sector. It should be noted that governments have been traditionally collecting content created by various social actors about domains of government activity, social problems or public policies under formulation using various traditional (offline) practices (e.g. collecting relevant extracts from newspa- pers); furthermore, they actively solicited relevant opinions and ideas from citizens (through various offline and online citizens’ consultation channels). However, the proposed approaches allow government agencies to perform such activities more extensively and intensively at a lower cost, reaching easily wider and more diverse and geographically dispersed groups of citizens’ (e.g. collecting relevant content not only from a small number of top newspapers but also from numerous bigger or smaller newspapers, blogs, Facebook accounts, etc.; also, interacting actively with many more citizens than the few ones participating in government consultations),so that they can gradually achieve mature levels of crowdsourcing. Furthermore, the proposed approaches allow overcoming the usual ‘information overload’ problems of the traditional practices, as they include sophisticated processing of the collected content that extracts the main points of it.

This chapter is organized in seven sections. In ‘Background’ our background is presented, and then in ‘Research Method’ the research methodology is outlined. Next, the two proposed approaches for passive and active crowdsourcing by govern- ment agencies are described in ‘An Active Crowdsourcing Approach’ and ‘A Passive Crowdsourcing Approach’, respectively. A comparison of them, also with the ‘clas- sical’ is presented in ‘Comparisons’, while in the final ‘Conclusion’ our conclusions are summarized.

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264 E. Loukis and Y. Charalabidis

12.2 Background

12.2.1 Crowdsourcing

The great potential of the ‘collective intelligence’, defined as a ‘form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills’, (Levy 1997), to contribute to difficult prob- lem solving and design activities has lead to the emergence of crowdsourcing and its adoption, initially in the private sector, and subsequently (still experimentally) in the public sector as well. Crowdsourcing is defined as ‘the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call’ (Howe 2006), or as ‘a new web-based business model that harnesses the creative solutions of a distributed network of individuals’, in order to exploit ‘collective wisdom’ and mine fresh ideas from large numbers of individuals (Brabham 2008). While the use of the collective intelligence of a large group of people as a help for solving difficult problems is an approach that has been used for long time (Surowiecki 2004; Howe 2008), it is only recently that crowdsourcing started being widely adopted as a means of obtaining external expertise, accessing the collective wisdom and creativities res- ident in the virtual crowd. The capabilities provided by the development and wide dissemination of ICT seem to have played an important role for this, as they allow the efficient participation and interaction of numerous and geographically dispersed individuals, and also the analysis of their contributions (Geiger 2012; Zhao and Zhu 2012; Majchrzak and Malhotra 2013). Brabham (2008), based on the analysis of sev- eral cases of crowd wisdom at work, which resulted in successful solutions emerging from a large body of solvers, concludes that ‘under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them’, due to the diversity of opinion, independence, decentralization and aggregation that characterize such a crowd.

Crowdsourcing started being applied initially in the creative and design industries, and then it expanded into other private sector industries, for solving both mundane and highly complex tasks. It gradually becomes a useful method for attracting an interested and motivated group of individuals, which can provide solutions superior in quality and quantity to those produced by highly knowledgeable professionals. Such a crowd can solve scientific problems that big corporate R&D groups cannot solve, outperform in-house experienced geophysicists of mining companies, design original t-shirts resulting in very high sales, and produce highly successful commercials and fresh stock photography against a strong competition from professional firms (Surowiecki 2004; Howe 2006, 2008; Brabham 2008, 2012). This can result in a paradigm shift and new design and problem solving practices in many industries.

For these reasons there has been significant research interest on crowdsourcing, which has resulted in a considerable body of knowledge on how crowdsourcing can be efficiently and effectively performed in the private sector; reviews of this literature are provided by Rouse (2010), Hetmank (2013), Pedersen et al. (2013) and Tarrell et al. (2013). Initially this research focused on analyzing successful cases, while later

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it started generalizing, based on the experience of multiple cases, in order to identify patterns and trends in this area and also to develop effective crowdsourcing practices. A typical example in this direction is the study by Brabham (2012), which, based on the analysis of several case studies, identifies four dominant crowdsourcing ap- proaches: (i) the knowledge discovery and management approach (= an organization tasks crowd with finding and reporting information and knowledge on a particular topic), (ii) the broadcast search approach (= an organization tries to find somebody who has experience with solving a rather narrow and rare empirical problem), (iii) the peer-vetted creative production approach (= an organization tasks crowd with creat- ing and selecting creative ideas), and (iv) the distributed human intelligence tasking (= an organization tasks crowd with analyzing large amounts of information). Het- mank (2013), based on a review of crowdsourcing literature, identifies a basic process model of it, which consists of ten activities: define task, set time period, state reward, recruit participants, assign tasks, accept crowd contributions, combine submissions, select solution, evaluate submissions and finally grant rewards. Also, he identifies a basic pattern with respect to the structure of crowdsourcing Information System (IS), which includes four main components that perform user management (pro- viding capabilities for user registration, user evaluation, user group formation and coordination), task management (providing capabilities for task design and assign- ment), contribution management (providing capabilities for contributions evaluation and selection) and workflow management (providing capabilities for defining and managing workflows), respectively. Furthermore, there are some studies that attempt to generalize the experience gained from successful applications of crowdsourcing ideas in order to develop effective practices for motivating individuals to participate (Brabham 2009; Stewart et al. 2009).

Rouse (2010), based on a review of relevant literature, distinguishes between two types of crowdsourcing with respect to participants’ motivation: (i) individualistic (aiming to provide benefits to specific persons and firms), (ii) community oriented (aiming to benefit a community of some kind, through ideas and proposals), and (iii) mixed (combinations of the above). Furthermore, she proceeds with identifying seven more detailed types of participant motivations: learning, direct compensation, self-marketing, social status, instrumental motivation (= motivation to solve a per- sonal or firm problem, or to address a personal/firm need), altruism (= motivation to help the community without personal benefit) and token compensation (= earning a small monetary prize or gift). Also, the same publication concludes that many of the benefits of crowdsourcing described in the literature are similar to those of the ‘main- stream’ outsourcing: cost savings, contracts and payments that are outcome based (rather than paid ‘per hour’), and access to capabilities not held in-house; an addi- tional benefit of crowdsourcing, which is not provided by outsourcing, is the capacity to exploit knowledge and skills of volunteers who might not, otherwise, contribute. However, at the same time it is emphasized that—as with all outsourcing—the de- cision to crowdsource should only be made after considering all the production, coordination and transaction costs, and the potential risks. Many of the highly publi- cized crowdsourcing successes have been achieved by organizations with substantial project management and new product/services development systems and capabilities, which lead to low levels of crowdsourcing coordination and transaction costs.

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12.2.2 Public Sector Application

Crowdsourcing ideas, as mentioned above, have been initially developed and applied in the private sector, however later some government agencies started experimenting with them. Highly influential for this have been central top-down initiatives in several countries, such as the ‘Open Government Directive’ in the USA (Executive Office of the President 2009). It defines transparency, participation and collaboration as the main pillars of open government:

a. Transparency promotes accountability by providing the public with information about what the government is doing.

b. Participation allows members of the public to contribute ideas and expertise so that their government can benefit from information and knowledge that is widely dispersed in society, in order to design better policies.

c. Collaboration improves the effectiveness of government by encouraging partner- ships and cooperation within the federal government, across levels of government, and between the government and private institutions.

Crowdsourcing can be quite valuable for promoting and developing two of these three main pillars of open government: participation and collaboration. This has lead government organizations, initially, in the USA and later in other countries as well, to proceed to some first crowdsourcing initiatives, having various forms of ‘citizensourcing’ for collecting information on citizens’ needs and for the solution of difficult problems. These initiatives motivated some first research in this area, which aims to analyze these initiatives in order to learn from them, and to identify common patterns and trends (Lukensmeyer and Torres 2008; Hilgers and Ihl 2010; Nam 2012). Lukensmeyer and Torres (2008) conclude that citizen sourcing may become a new source of policy advice, enabling policy makers to bring together divergent ideas that would not come from traditional sources of policy advice; furthermore, it may change the government’s perspective on the public from an understanding of citizens as ‘users and choosers’of government programs and services to ‘makers and shapers’ of policies and decisions. Hilgers and Ihl (2010) developed a high-level framework for the application of citizen sourcing by government agencies, which consists of three tiers:

1. Citizen ideation and innovation: this first tier focuses on the exploitation of the general potential of knowledge and creativity within the citizenry to enhance the quality of government decisions and policies, through various methods, such as consultations and idea and innovation contests.

2. Collaborative administration: the second tier explicitly addresses the integration of citizens for enhancing existing public administrative processes.

3. Collaborative democracy: this tier includes new ways of collaboration to improve and expand public participation within the policy process, including the incorpo- ration of public values into decisions, improving the quality of decisions, building trust in institutions and educating citizens.

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Nam (2012), based on the study of citizen-sourcing initiatives in the USA, developed a framework for the description and analysis of such initiatives, which consists of three dimensions: purpose (it can be for image making, information creation, service co-production, problem solving and policy-making advice), collective intelligence type (professionals’ knowledge or nonprofessionals’ innovative ideas), and govern- ment 2.0 strategy (it can be contest, wiki, social networking, or social rating and voting).

However, since public-sector crowdsourcing is still in its infancy, having much less maturity than private-sector crowdsourcing, further research is required in this area; its main priority should be the development of approaches and methodologies for the efficient and effective application of crowdsourcing ideas in government for supporting problem solving and policy making, taking into account its special needs and specificities. They should focus on addressing the inherent difficulties of modern policy making, which are caused by the complex and ‘wicked’ nature of social problems (Rittel and Weber 1973; Kunz and Rittel 1979), enabling a better and deeper understanding of the main elements of them (e.g. issues, alternatives, advantages and disadvantages perceived by various stakeholder groups; Conklin and Begeman 1989; Conklin 2003; Loukis and Wimmer 2012).

12.3 Research Method

The development of the two proposed approaches for active and passive crowd- sourcing, respectively, was performed through close cooperation with public sector employees experienced in public policy making, using both qualitative and quanti- tative techniques: semi-structured focus group discussions, scenarios development and questionnaire surveys.

12.3.1 Active Crowdsourcing

The development of our active crowdsourcing approach (described in ‘An Active Crowdsourcing Approach’) included the following six phases (for more details on them see DeliverableD2.1 ‘Padget Design and Decision Model for Policy Making’ of the PADGETS project accessible in its website www.padgets.eu):

a. Initially three semi-structured focus group discussions were conducted in the three government agencies participating in the PADGETS project (mentioned in the introductory section) as user partners (Center for eGovernance Development (Slovenia), ICT Observatory (Greece), Piedmont Regional Government (Italy)), which aimed at obtaining an understanding of their policy-making processes, the degree and form of public participation in them, and also their needs for and interest in ICT support.

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268 E. Loukis and Y. Charalabidis

b. The main themes of the above semi-structured focus group discussions were used for the design of a questionnaire, which was filled in and returned to us through e-mail by another four government agencies (City of Regensburg (Germany), World Heritage Coordination (Germany), North Lincolnshire Council (UK), IT Inkubator Ostbayern GmbH (Germany)), which have some form of close coop- eration with the above three user partners of PADGETS project. This allowed us to obtain the above information from a wider group of government agencies, and cover a variety of government levels (national, regional and local).

c. Based on the information collected in the above first two phases the main idea of the active crowdsourcing approach was formulated: combined use of multiple social media for consultation with citizens on a social problem or public policy of interest, and sophisticated processing of relevant content generated by citizens.

d. Three application scenarios were developed in cooperation with the above three user partners of PADGETS project concerning the application of the above main idea for a specific problem/policy of high interest. Each of these scenarios de- scribed which social media should be used and how, what content should be posted to them, and also how various types of citizens’ interactions with it (e.g. views, likes, comments, retweets, etc.) should be monitored and exploited, and what analytics would be useful to be computed from them.

e. Finally, a survey was conducted, using a shorter online questionnaire, concerning the required functionality from an ICT tool supporting the use of social media for such multiple social media consultation. It was distributed by personnel of the three user partners involved in the PADGETS project to colleagues from the same or other government agencies, who have working experience in public policy making, and finally was filled in by 60 persons.

f. Based on the outcomes of the above phases C, D and E, we designed this govern- ment active crowdsourcing approach in more detail, and then the required ICT infrastructure and its application process model (described in ‘Description’, ‘ICT Infrastructure’ and ‘Application Process Model’, respectively).

12.3.2 Passive Crowdsourcing

The development of our passive crowdsourcing approach (described in ‘A Passive Crowdsourcing Approach’) included the following seven phases (for more details on them see Deliverable D2.1 ‘Padget Report on User Requirements’ of the NOMAD project in its website www.nomad-project.eu/):

1. Initially the main idea was developed, in cooperation with the user partners of the NOMAD project (Greek Parliament, Austrian Parliament, European Academy of Allergy and Clinical Immunology), based on the digital reputation and brand management ideas from the private sector (e.g. see Ziegler and Skubacz 2006): passive retrieval of content that has been generated by citizens freely (without any initiation, stimulation or moderation through government postings) in nu- merous Web 2.0 sources (e.g. blogs and microblogs, news sharing sites, online

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forums, etc.) on a specific topic, problem or public policy, and then sophisticated processing of this content using opinion mining techniques.

2. Four application scenarios of this idea were developed by the above user partners of the NOMAD project. Each application scenario constitutes a detailed realistic example of how this passive croudsourcingidea could be applied for supporting the formulation of a particular public policy, and describes how various types of users involved in this might use an ICT platform that implements this idea.

3. A questionnaire was distributed electronically to a sample population of potential users, which included questions concerning: (a) respondent’s personal informa- tion, (b) general citizens’ participation information (in his/her organization), (c) current use of social media in policy-making processes, (d) general assessment of this ideaand and (e) specific relevant requirements.

4. Organization of focus groups and workshops with the participation of potential users. This allowed in-depth discussion among people experienced in the design of public policies, with different backgrounds and mentalities, about this new idea, and also ways and processes of its practical application, required relevant ICT functionalities and at the same time possible problems and barriers.

5. Organization of in-depth interviews based of a series of fixed questions concerning attitudes towards this new idea, its usefulness and applicability.

6. A review of systems that offer at least a part of the above ICT functionalities (e.g. for content retrieval, opinion mining, etc.).

7. Based on the outcomes of the above phases we designed this government passive crowdsourcing approach in more detail, then its application process model and finally the required ICT infrastructure (as described in ‘Description’, ‘Application Process Model’ and ‘ICT Infrastructure’, respectively).

12.4 An Active Crowdsourcing Approach

12.4.1 Description

The proposed active crowdsourcing approach is based on the centralized automated publishing of multimedia content (e.g. a short text, a longer description, images, videos, etc.) concerning a social problem of interest or a public policy under formu- lation to the accounts of a government agency in multiple social media (e.g. Facebook, Twitter, YouTube, Picasa and Blogger), in order to actively stimulate discussions on it. As mentioned in ‘Introduction’ and ‘Background’ social problems have become highly complex and ‘wicked’, with multiple and heterogeneous stakeholders having different problem views, values and objectives (Rittel and Weber 1973; Kunz and Rittel 1979; Conklin 2003), so in order to address this inherent difficulty our method- ology uses multiple social media, with each of them attracting different groups of citizens. Throughout these social media consultations we continuously retrieve and monitor various types of citizens’ interactions with the content we have posted (e.g. views, likes, ratings, comments and retweets), and finally we process these interac- tions in order to support drawing conclusions from them. Both content posting and

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interactions’ continuous retrieval are performed in a highly automated manner using the API of these social media from a central ICT platform, in which also processing and results presentation takes place.

In particular, a government agency policy maker, through a web-based dashboard or a mobile phone application, initiates a campaign concerning a specific topic, problem or policy in multiple social media. For this purpose, he/she creates relevant multimedia content (e.g. short and longer topic description, images, videos, etc.), which are then automatically published in the corresponding social media (e.g. in the Twitter the short-topic description, in Blogger the longer one, in YouTube the video, in Picasa the images, etc.) by a central platform. The citizens will view this content, and interact with it (in all the ways that each social media platform allows), either through these social media, or through a mobile phone application. Then, these interactions will be automatically retrieved and shown continuously to the policy maker, through the above web-based dashboard or mobile phone application, so that appropriate interventions can be made (i.e. new content can be published) if necessary. Finally, after the end of the campaign, sophisticated processing of all citizens’ interactions with the above content will be performed in this central ICT platform, using a variety of techniques (e.g. calculation of web analytics and opinion mining), in order to provide useful analytics that support government decision and policy making. In Fig. 12.1, this active crowdsourcing approach is illustrated.

The practical application of the above approach will lead to a collection of large amounts of content generated by citizens in various Web 2.0 social media concerning the particular topic, problem or policy we have defined through our initial postings. So it will be of critical importance to use highly sophisticated methods of automated processing this content, in order to offer substantial support to government agencies policy makers in drawing conclusions from it . Part of this citizens-generated content is numeric (e.g. numbers of views, likes, retweets, comments, ratings, etc.), so it can be used for the calculation of various analytics. However, a large part of this content is in textual form, so opinion mining, defined as the advanced processing of text in order to extract sentiments, feelings, opinions and emotions (for a review of them see Maragoudakis et al. 2011), will be a critical technology for processing it and maximizing knowledge extraction from it. The development and use of opinion mining first started in the private sector, as firms wanted to analyze comments and reviews about their products, which had been entered by their customers in various websites, in order to draw conclusions as to whether customers like the specific products or not (through sentiment analysis techniques), the particular features of the products that have been commented (through issues extraction techniques) and the orientations (positive, negative or neutral) of these comments (through sentiment analysis techniques). These ideas can be applied in the public sector as well, since citizens’ comments are a valuable source of information that can be quite useful for government decision and policy making: it is important to identify the main issues posed by citizens (through issues extraction) on a particular topic, problem or policy making we are interested in, and also the corresponding sentiments or feelings (positive, neutral or negative—through sentiment analysis). More details about this active crowdsourcing approach are provided by Charalabidis and Loukis (2012), Ferro et al. (2013) and Charalabidis et al. (2014a).

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Fig. 12.1 An approach for active crowdsourcing in government

12.4.2 ICT Infrastructure

An ICT platform has been developed for the practical application of the above ap- proach, which provides all required functionalities to two main types of users of it: government agencies’ policy makers and citizens. In particular, a ‘policy makers dashboard’ (accessible through a web-based or a mobile interface (Android mobile application)) enables government agencies’ policy makers:

1. To create a multiple social media campaign, by defining its topic, the starting and ending date/time, the social media accounts to be used, and the relevant messages and multimedia content to be posted to them

2. To monitor continuously citizens’ comments on the messages; in Fig. 12.2, we can see this part of the web-based policy-makers’ interface, which is structured

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Fig. 12.2 Policy-makers’ interface for viewing active campaigns, messages and citizens’ feedback

in three columns: in the first column, the active campaigns are shown, while by selecting one of them in the second column are shown the corresponding messages posted by the policy maker (the initial, and the subsequent ones), and finally by selecting one of these messages in the third column are shown citizens’comments on it (textual feedback stream)

3. And after the end of the campaign to view (as graphics and visualizations) a set of analytics and opinion mining results, which are produced by the decision support component of the platform (described later in this section) for the whole campaign.

The citizens can see the initial content of each campaign, and also other citizens’ interactions with it (e.g. textual comments), either through the interfaces of the cor- responding social media, or through a mobile interface (Android mobile application) or a widget, which enables citizens to view active campaigns, and by selecting one of them to view all policy maker and citizens’ comments on it, or add a new comment.

The technological architecture of this ICT platform is shown in Fig. 12.3. We can see that it consists of two main areas:

1. The front-end area, which provides the abovementioned web interface to the policy makers, and also the mobile application and widget interfaces to both policy makers and citizens.

2. The back-end area, which includes three components: the first of them perfoms publishing of various content types in multiple social media through the second component, which consists of connectors with the utilized social media, while the third component performs aggregation/analysis of citizens interactions with

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Fig. 12.3 Active crowdsourcing ICT platform technological architecture

the above-published content in these social media, retrieved through the second component; it consists of one subcomponent that allows continuous monitoring of these citizens interactions, and several subcomponents that provide analytics for government policy-makers’ decision support.

One of these subcomponents collects and processes the ‘raw analytics’ provided by the analytics engines of the utilized social media. Another subcomponent provides more advanced analytics, which concern citizens’ textual inputs (e.g. blog post- ings, comments, opinions, etc.), processing them using opinion mining techniques (Maragoudakis et al. 2011). In particular, it performs the following three types of tasks:

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• Classification of an opinionated text (e.g. a blog post) as expressing a posi- tive, negative or neutral opinion (this is referred to as document-level sentiment analysis).

• Classification of each sentence in a such a text, first as subjective or objective (i.e. determination of whether it expresses an opinion or not), and for each subjective sentence (i.e. expressing an opinion) classification as positive, negative or neutral (this is known as sentence-level sentiment analysis).

• Extraction of specific issues commented by the author of a text, and for each issue to identify its orientation as positive, negative or neutral (this is referred to as feature-level sentiment analysis).

Another subcomponent performs simulation modelling (Charalabidis et al. 2011), having mainly two objectives: estimation of the outcomes of various citizens’propos- als on the public policies under discussion, and also forecasting the future levels of citizens’interest in and awareness of these policies. The simulation modelling takes as input various indicators produced by the other two aforementioned subcomponents.

12.4.3 Application Process Model

Furthermore, an application process model for this active crowdsourcing approach has been developed. It provides a model of the process to be followed by government agencies for the practical application of it, which includes a sequence of specific activities to be executed:

1. The policy maker initially setsup a policy campaign, using the capabilities of the central ICT platform described above, through a graphical user interface

2. Then he/she creates textual content for this campaign (both short and longer policy statements), and also can add various types of multimedia content to it (e.g. policy images, video, etc.)

3. And finally defines the multiple social media accounts to be used in this campaign 4. And views a preview of the campaign in each of them 5. The campaign is launched by publishing the above content (in each of these

multiple social media will be automatically published the appropriate part of the above content, e.g. in the Twitter will be published the short policy statement, in Blogger the longer one, in YouTube the video, in Picasa the images, etc.).

6. Citizens interact with the published content in various ways in these social media (in the particular ways each of them allows): they access and see this content, rate it and make some comments on it, retransmit it in their networks, etc

7. The above citizens’ interactions are automatically retrieved continuously from all the used social media in the central ICT platform, and after the end of the campaign are processed there using various advanced techniques (as described above), in order to calculate useful analytics that provide assistance and support to the policy maker.

8. The results are sent immediately to the policy maker, by e-mail or SMS message.

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Fig. 12.4 A typical application scenario of the active crowdsourcing ICT approach

In Fig. 12.4, we can see a typical application scenario of this active crowdsourcing approach.

12.5 A Passive Crowdsourcing Approach

12.5.1 Description

The proposed passive crowdsourcing approach is based on the exploitation of the extensive political content created in multiple Web 2.0 sources (e.g. blogs and mi- croblogs, news sharing sites, and online forums) by citizens freely (= without active stimulation through some government posting) concerning various domains of gov- ernment activity and public policies. An ICT platform automatically retrieves this content from these Web 2.0 sources using their API, and then processes it using so- phisticated linguistic processing techniques in order to extract from it relevant issues, proposals and arguments. So in this approach government is not active in conduct- ing crowdsourcing (as it is in the active crowdsourcing approach presented in the previous section, by posing to citizens particular discussion topics, problems or poli- cies), but it remains passive (just ‘listening’ to what citizens discuss, and analyzing the content they freely produce in order to extract knowledge from it). Taking into account the highly complex and ‘wicked’ nature of modern social problems, which usually have multiple and heterogeneous stakeholders with different problem views, values and objectives (Rittel and Weber 1973; Kunz and Rittel 1979; Conklin 2003), our passive crowdsourcing approach uses multiple Web 2.0 content sources, with diverse political perspectives and orientations.

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In particular, this passive crowdsourcing approach includes three stages, which are illustrated in Fig. 12.5. The first stage, called ‘Listen’, includes listening and mon- itoring what citizens say concerning a domain of government activity (e.g. higher education) or a public policy under formulation (e.g. a new policy on higher educa- tion) in a large set of Web 2.0 sources S1, S2,. . . , SN defined by the policy maker. For this purpose a ‘focused crawler’ is used, which is a program that browses the above sources in an automated and organized manner, and retrieves solely content that is relevant to the specific topic of interest.

The second stage, called ‘Analyse’, includes advanced processing and analysis of the retrieved content, from which are identified relevant issues, proposals and arguments expressed by citizens. As the majority of this content is in textual form, this stage makes use of advanced linguistic processing techniques (for a review of them, see Maragoudakis et al. (2011)). In particular, each content unit retrieved by the crawler will go through a series of automated processing steps:

• Language detection, which will recognize the language used in it. • Opinion and argument extraction, using appropriate semantic similarity measures

and inference mechanisms that allow the identification of elements of the analyzed content which are pertinent to the particular domain or policy.

• Sentiment analysis, using smart sentiment classifiers that recognize the polarity (positive, neutral, and negative) of the elements identified above.

• Argument summarization, using appropriate algorithms for generating qualitative information about opposing arguments, in the form of anonymity-preserving and automatically generated summaries.

The third stage, called ‘Receive’, aims to present to the end-user (policy maker) the knowledge acquired from the previous stages in a complete, coherent and us- able manner. The platform will provide an aggregated view of the results of the above processing, their polarity, their association with various policy concepts and statements, and also statistical indications of their significance and impact. For this purpose visual analytics (Wong and Thomas 2004; Thomas and Cook 2005; Keim et al. 2010) will be used, so that policy makers can view visualizations of the results of previous stages, and easily understand them with minimal cognitive effort (e.g. in a familiar word cloud form), which is quite important due to the high information overload the policy makers usually experience.

The knowledge gained through this passive crowdsourcing (e.g. issues, propos- als and arguments concerning a domain of government activity or a policy under formulation) can be used in order to formulate more specific questions, positions or proposals about the particular policy and then solicit citizens’ feedback and contribu- tions on them through more ‘active’ forms of communication. This can be achieved through ‘active crowdsourcing’, i.e. by making relevant stimulating postings (based on the findings from passive crowdsourcing) to various social media (e.g. blogs, Twitter, Facebook, YouTube, etc.), and also to official government e-participation websites, in order to collect citizens’ interactions with this content (e.g. ratings, votes, comments, etc.). Therefore, the proposed ‘passive crowdsourcing’ approach

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S1 S2 .... SN

Listen Analyse Receive

Fig. 12.5 The three stages of the government passive crowdsourcing approach

can be combined with the ‘active crowdsourcing’ approach described in the previ- ous section, in order to increase its effectiveness. More details about this passive crowdsourcing approach are provided by Charalabidis et al. (2014b).

12.5.2 Application Process Model

Extensive effort was required in order to design how the above passive crowdsourcing concept can be practically applied by government agencies and work efficiently, and formulate an apropriate process model for its application. So we will describe first this aspect of it, and then the required ICT infrastructure in the following ‘ICT Infrastructure’ (since the latter has been to a large extent based on the former). There was wide agreement that since the domains of government activity and the public policies for them are quite complex and multidimensional entities, it is not possible to search for content on them in the predefined Web 2.0 sources using just a small number of keyworks. So it was concluded that the best solution for addressing this complexity is to develop a model of the specific domain, for which a policy is intended, which will consist of the main terms of it and the relations among them (a kind of ‘structured thesaurus’ of this domain). An example of such a domain model for the energy domain, which has been developed based on the documents of the ‘Greek Strategy for Energy Planning’, is shown below in Fig. 12.6.

Based on such a domain model we can then build a policy model, by adding to the nodes of the former: (a) the ‘policy statements’ (= the specific policy objectives and actions/interventions that a policy includes) and also (b) positive and negative arguments in favour or against them, respectively. An example of such a policy model for the energy domain is shown in Fig. 12.7 (including three policy objectives, one concerning the whole national energy planning, and two concerning the renewable energy sources, six positive arguments and nine negative ones).

These two models (domain and policy ones) can be used for searching for and retrieving relevant content concerning the main terms of a domain, or the policy statements and the arguments of a policy. This search has to be performed at regular time intervals in order to keep the retrieved content updated, and the results should be stored in a database, and then undergo the advanced processing mentioned in the previous section (in the ‘Analyse’ stage), the results of which will be also stored in the same database. The authorized policy makers will have the capability at any time to explore the results of this advanced processing stored in the above database, and

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Fig. 12.6 Energy domain model

view various visualization of them, e.g. the most frequently mentioned terms-topics with respect to a particular domain or policy model (e.g. in a tag cloud form).

Also, most of the potential users we interviewed mentioned that it is important to view citizens’ sentiment with respect to these frequently mentioned terms-topics (i.e. whether citizens regard each of them as positive, negative or neutral), or even with respect to the individual policy statements and arguments of a policy model. Furthermore, our interviewees noted that all the above (i.e. frequently mentioned terms-topics and sentiments) may differ significantly between different citizens groups (e.g. between age, gender, education and region groups), so policy mak- ers should have the capability to view them for particular citizens’ groups, or to view comparisons between different citizens’ groups. Furthermore, since public stance changes rapidly, it was mentioned that policy makers should have the capability to view all the above information for particular user-defined time periods, or to compare between different time periods, while future forecasts of them would be quite useful.

Based on the above, a model of the process to be followed by government agencies for the practical application of this passive crowdsourcing approach was developed. It includes the following nine activities:

1. Development of a domain model 2. Development of a policy model 3. Definition of Web 2.0 content sources 4. Search of these content sources at regular time intervals 5. Process retrieved content and store results in a database 6. Policy maker views polarized tag glouds with the most frequently mentioned

terms-topics with respect to a particular domain or policy model and the corresponding sentiments for a predefined time period.

7. Policy maker views the sentiments with respect to the individual policy statements and arguments of a policy model.

8. Policy maker views the above for particular citizens’ groups, and then makes comparisons between different citizens’ groups, or with other time periods.

9. Policy maker views short-term future forecasts of the above.

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Fig. 12.7 Energy policy model based on the above energy domain model (including policy statements and arguments)

Finally, we identified four roles which are required for the practical application of this process model:

• Domain models author: this role will create domain models and also modify existing ones.

• Policy models author: this role will create policy models based on existing domain models (= add to their nodes policy statements and argumentations) and also modify existing ones.

• End user/policy maker: this role will view the results of processing the content retrieved from the Web 2.0 sources in all the abovementioned forms.

• Platform administrator: this role will have full access to all platform functionali- ties, monitor platform operation, manage the set of users accessing the platform and their access rights to the offered services and functionalities.

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12.5.3 ICT Infrastructure

Based on the above application process model, we proceeded to the design of the functional architecture of the required ICT platform. In particular, we defined in more detail the functionality to be provided to each of the above four roles:

1. Domain models author – Creation of new domain models (= definition of main terms of the domain and

the relations among them). – Modification of existing domain models. – Import of external domain models (e.g. having the form of ontology files in

OWL). – Export of domain models (e.g. in the form of ontology files in OWL).

2. Policy models author – Access to domain models. – Creation of new policy models (using existing domain models, by adding

policy statements and arguments to their nodes). – Modification of existing policy models. – Import of external policy models (e.g. having the form of ontology files in

OWL). – Export of policy models (e.g. in the form of ontology files in OWL).

3. End user/policy maker – View the most frequently mentioned terms-topics with respect to a particular

domain or policy model for a predefined time period, citizens’ group and sources subset (see Fig. 12.8 for a first design of the corresponding screen).

– View sentiment for these terms-topics. – View sentiment for each policy statement and argument of a particular model. – View differentiations of the above over time. – View differentiations of the above across citizens’ groups. – View differentiations of the above across sources subsets. – View short-term future projections of the above.

4. Platform administrator – Users and roles management. – Domain and policy roles management. – Monitoring and administration of all platform services.

Based on the above functional architecture of the platform, its technological archi- tecture was designed. The objective of this design was to provide this functionality with an acceptable response time. Since this could not be achieved through online retrieval of content from a large number of sources (e.g. numerous blogs, news web- sites, Facebook, Youtube and Twitter accounts) and processing of it at the time a user initiates a search, the only solution was to perform a scan of the predefined sources at some regular time intervals (e.g. every 6 h) in order to retrieve new content, store it in a database and then process it and store the results in the same database. Whenever the user performs a search, the results will be produced in a very short time, using this database. This separation between sources scanning and content processing on one

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Fig. 12.8 View of the most frequently mentioned terms-topics with respect to a particular domain or policy model for a predefined time period, citizens’ group and sources subset

hand, and users’ searches processing on the other, allows a low response time and at the same time sufficiently ‘fresh’ content for policy makers (i.e. allows addressing these two conflicting requirements).

The above design leads to a three layers’technological architecture of the platform, which consists of a storage layer, a processing layer and a presentation layer, and is shown in Fig. 12.9. Each of them includes a number of components, performing different tasks, which act as services coordinated by an orchestration component.

In particular, the data storage layer includes the repositories where the raw and processed content is stored:

• The content repository: it stores the raw content retrieved from the Web 2.0 sources, the cleaned content derived from the raw data, the content uploaded by users and the results of the linguistic analysis associated with each content unit.

• The model repository: it stores in a structured form the domain and policy models entered by users with domain expert and policy advisor roles.

• The metadata repository: it stores the metadata retrieved or calculated for our sources.

• The thematic catalogues: it stores a representation of the thematic categories used by the platform in order to characterize each content unit.

• The users repository: it contains information about the roles and the users of the platform.

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Presenta on Layer (User Interfaces)

Model AuthoringSystem Interface

Processing Layer

Data Acquisi on

Data Classifica on & Argument Summariza on

Keyword Selec on

Rela on Defini on

Argument Building

Thema c Classifier

Thema c Catalogue

Dynamic Content Crawlers

Sta c Content Crawlers

Policy Model Sharing

Visualisa on & Analysis

Storage Layer

Thema c Catalogues

Content Domain/

Policy Models

Content Cleaner

Metadata

Opinion Mining & Argument Extrac on

Sen ment Analyser

Segment Extractor

Argument Extractor

Linguis c Demographic

Extractor

Tag Cloud Generator

Content Inser on

Argument Summarizer

Users

Administra ve Interface

Fig. 12.9 Passive crowdsourcing ICT platform technological architecture

The processing layer includes all the components that retrieve and process the content from the predefined sources, which are organized in three sub-layers:

• The data acquisition layer, which includes the crawling components for fetching content from the sources, using their APIs, as well as the modules responsible for cleaning the fetched content and obtaining the actual textual information from it (static content crawlers, dynamic content crawlers and content cleaner).

• The data classification and argument summarization layer, which includes (a) the thematic classifier, which processes the available content and associates it

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with one more of the defined thematic categories in the thematic catalogues, and (b) the result summarizer, which processes the available results and provides a summarization that allows their presentation in a condensed manner.

• The argument extraction and opinion mining layer, which includes all the com- ponents that process the available content and extract segments, arguments and sentiments (segment extractor, argument extractor, sentiment analyzer, linguistic demographic extractor, tag cloud generator).

The presentation layer includes all the components that either require input from the user or present to him/her the results:

• The thematic catalogue interface, for entering or updating the available thematic categories and also terms associated with each category.

• The keyword selection interface, which allows entering keywords/terms for creating domain models.

• The relation definition interface, which allows the user to introduce relations between the above keywords/terms for the definition of domain models.

• The argument building interface, which allows the user to insert in natural lan- guage statements and arguments supporting or objecting to policy statements of policy models.

• The policy model sharing interface, which provides a catalogue of the policy models created by the user and allows defining them as visible to others.

• The admin interface, which provides the means to an administrator to manage the configurable aspects of the system.

• The visualisation and analysis module, which utilizes the results of the processing layer in order to provide the user with a view of domain and policy models, and also various visualizations of the results of users’ searches, enabling also the selection of sources, demographic characteristics and time periods.

The domain and policy modelling components of the presentation layer (thematic cat- alogue, keyword selection, relation definition, argument building and policy model sharing interfaces) will be based on the ELEON Ontology Authoring and Enrich- ment Environment (http://www.iit.demokritos.gr/ eleon), developed by the National Center for Scientific Research ‘Demokritos’, which participates as a partner in the NOMAD project. It supports editing ontologies and relating such ontologies with lin- guistic resources that can be used to extract structured ontological information from text, and also supports the author with a number of innovative methods for ontology checking (Bilidas et al. 2007) and autocompletion (Konstantopoulos et al. 2011). The sentiment analyser will be based on existing tools developed by ‘Demokritos’ as well (Rentoumi et al. 2009; Rentoumi et al. 2010), which are based on algorithms that take into account various intricacies of the language forms commonly used in the context of user-generated web content, such as metaphors, nuances, irony, etc. For the summarization task the ‘n-gram graph framework’ (Giannakopoulos et al. 2008; Giannakopoulos and Karkaletsis 2009) will be used, which is a statistical, domain agnostic and language-independent framework that allows the analysis of texts as character n-gram graphs.

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12.6 Comparisons

In this section, we make a comparison between the two proposed crowdsourcing ap- proaches, and also with the private and public sector crowdsourcing patterns reported in the literature (outlined in ‘Background’), identifying similarities and differences.

Both approaches adopt two of the four crowdsourcing approaches identified by Brabham (2012): mainly ‘knowledge discovery’ and secondarily ‘creative produc- tion’. From the four public sector specific crowdsourcing purposes identified by Nam (2012) they focus mainly on ‘information creation’, and secondarily on ‘prob- lem solving’ and ‘policy making advice’; also from the two types of collective intelligence mentioned in the same study both approaches aim at ‘nonprofession- als innovative ideas’ and much less at ‘professionals knowledge’. With respect to participants’ motivation, from the two main motivation types identified by Rouse (2010) both approaches are based mainly on citizens’ ‘community oriented’ motiva- tions and much less on ‘individualistic’ ones (since none of the two approaches is based on the monetary or other types of rewards used in private sector crowdsourc- ing); also, from the seven more detailed participants’ motivations identified in the same study the ‘altruism’, ‘instrumental motivation’ and ‘social status’ seem to be ones our approaches mainly rely on. Finally from the four organizer benefits identi- fied in the same study, both methodologies aim to provide to adopting government agencies ‘access to capabilities not held in-house’and ‘capacity to exploit knowledge and skills of volunteers who might not otherwise contribute’, but not ‘cost savings’ or ‘contracts and payments that are outcome based’.

With respect to the required ICT infrastructures it should be noted that the one of our active crowdsourcing approach—described in ‘ICT Infrastructure’—has some similarities with the typical crowdsourcing IS (which according to Hetmank (2013)) includes user, task, contribution and workflow management components), but also important differences as well. In particular, this active crowdsourcing ICT platform includes ‘task management’ components (that enable setting-up a campaign and cre- ating/adding multimedia content to it) and ‘contribution management’ components (processing citizens’ interactions with the above content in the utilized social media). However, it does not include ‘user management’ components (as the management of the citizens participating in our campaigns is conducted through our social me- dia accounts) and ‘workflow management’ ones. Also the process model we have developed for the application of this active crowdsourcing approach—described in ‘Application Process Model’—has some similarities with the typical crowdsourcing process model (according to Hetmank (2013)), but also important differences as well. In particular, this application process model includes four out of the ten activities of this typical crowdsourcing process model (define task, set time period, accept crowd contributions, and combine submissions), however most of them in a quite different form. However, the former does not include the remaining six activities of the latter (state reward, recruit participants, assign tasks, select solution, evaluate submissions and finally grant rewards), due to inherent differences of our active crowdsourcing approach from the mainstream crowdsourcing (e.g. lack of reward and specific task

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assignments, participants management through our accounts in the utilized social media, lack of individual submissions evaluation, etc.).

On the contrary, both the application process model of our passive crowdsourcing approach and also the structure and components of the required ICT platform are quite different from the one of the typical crowdsourcing approaches, which has been identified by Hetmank (2013). In particular, our passive crowdsourcing approach does not include any of the main tasks of the mainstream crowdsourcing (problem definition, open call for contributions, search for and motivation of contributors, evaluation of contributions, and finally reward of the most successful of them), but has a quite different task structure (including domain and policy modelling, definition of the Web 2.0 sources to be used, automated content retrieval and sophisticated processing of the retrieved content, which do not exist in mainstream crowdsourcing). For this reason, its application process model—described in 5.2—is quite different from the one of the typical crowdsourcing. Also, the passive crowdsourcing ICT platform we have designed—described in 5.3—includes ‘contribution management’ components (allowing advanced linguistic processing of the textual content retrieved from multiple Web 2.0 sources), but not ‘task management’, ‘user management’ and ‘workflow management’ ones. This new passive crowdsourcing approach requires more extensive and complex ICT infrastructures than the existing crowdsourcing approaches, which are based on the use of API of numerous Web 2.0 sources, in combination with advanced linguistic processing techniques.

12.7 Conclusions

Crowdsourcing has been initially developed and applied in the private sector, and later introduced in the public sector (still in experimental mode). Therefore, there is limited knowledge concerning the efficient and effective application of crowdsourcing ideas in government, taking into account its special needs and specificities, much less than in the private sector. This chapter contributes to filling this gap, presenting two approaches for this purpose: a first one for ‘active crowdsourcing’, and a second one for ‘passive crowdsourcing’ by government agencies. The foundations of both come from management sciences (crowdsourcing research), political sciences (wicked social problems research) and technological sciences (social media capabilities and API). For each of these approaches has been presented the basic idea, the architecture of the required ICT infrastructure, and its application process model.

A common characteristic of the two proposed government crowdsourcing ap- proaches is that they do not include competitive contest among the participants and monetary or other types of rewards, as in private sector crowdsourcing, but mainly collaboration among citizens for knowledge and innovative ideas creation. Also they both rely mainly on community-oriented motivations of the participants and not on individualistic ones. They aim to provide to adopting government agencies not benefits associated with ‘cost savings’ or ‘contracts and payments that are outcome based’ (as in the mainstream private sector crowdsourcing), but benefits concerning

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Table 12.1 Similarities and differences between the proposed active and passive crowdsourcing approaches

Similarities

Both approaches exploit multiple Web 2.0 social media simultaneously

In a centrally managed manner based on a central platform

Fully automatically using their API

And then both make sophisticated processing of the collected content, in order to extract the main points from it, in order to reduce the ‘information overload’ of government decision makers

They both aim to provide to government agencies access to resources (e.g. information, knowledge, ideas, and skills) not available in-house

But without competitive contests and monetary rewards (which are quite usual in private sector crowdsourcing)

Relying both on community oriented motivations of the participants and not on individualistic ones

Differences

The active crowdsourcing approach uses the accounts of the particular government agency in several social, while the passive crowdsourcing approach goes beyond them, using other accounts, blogs, websites, etc., not belonging to government agencies

Also the former actively stimulates discussions and content generation by citizens on specific topics (through government postings and content), while the latter does not: it passively collects content created by citizens freely, without any initiation, stimulation or moderation through government postings

The initial preparation—content generation requirements for the application of the passive crowd- sourcing approach (= creation of domain and policy models) are much higher than the ones of active crowdsourcing

The processing of the collected content has to undergo much more sophisticated processing in the case of the passive crowdsourcing approach than in the active crowdsourcing one

And also the required ICT infrastructure for the active crowdsourcing approach, and its application model are more similar to the ones of the mainstream private sector crowdsourcing than the passive crowdsourcing approach

‘access to capabilities not held in-house’ and ‘capacity to exploit knowledge and skills of volunteers who might not otherwise contribute’. However, while for our active crowdsourcing approach the required ICT infrastructure and its application process model have some similarities with the ones of the mainstream private sector crowdsourcing (also important differences as well), our passive crowdsourcing ap- proach requires quite different forms of ICT infrastructure and application process model from the ones of the mainstream crowdsourcing. The similarities and differ- ences between the two proposed approaches are summarized below in Table 12.1. However, it should be noted that these two approaches are not mutually exclusive, but can be combined: the results of passive crowdsourcing can be used for guiding active crowdsourcing on the most important of the identified issues and problems, or even for organizing relevant discussions in government e-consultation spaces.

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From a first evaluation we have conducted for the active crowdsourcing approach based on pilot applications (see Ferro et al. 2013; Charalabidis et al. 2014a), it has been concluded that it constitutes a time and cost efficient mechanism of reaching wide and diverse audiences, and stimulating and motivating them to think about social problems and public policies under formulation, and provide relevant infor- mation, knowledge, ideas and opinions. Furthermore, it enables identifying the main issues perceived by citizens with respect to a particular social problem or domain of government activity, and collecting from them interesting ideas on possible so- lutions and directions of government activity. However, our pilot applications have shown that the above information generated from such multiple social media crowd- sourcing might be not be at the level of depth and detail required by government agencies. So in order to achieve a higher level of detail, and more discussion depth in general, a series of such multiple social media consultations might be required, each of them focused on particular subtopics and/or participants. Another risk of this active crowdsourcing approach is that it can lead to unproductive discussions among like-minded individuals belonging to the network of the government policy maker who initiated the consultation; such discussions are characterized by low diversity of opinions and perspectives, low productivity of knowledge and ideas, and in general limited creativity. Therefore, for the effective application of this crowdsourcing ap- proach it is of critical importance to build large and diverse networks for these social media consultations; for his purpose, we can combine networks of several govern- ment agencies, and also politicians, preferably from different political parties and orientations, and also invite additional interested and knowledgeable individuals and civil society organizations. Our passive crowdsourcing approach is currently under evaluation based on pilot applications.

The research presented in this chapter has interesting implications for research and practice. It opens up new directions of multidisciplinary research concerning the ap- plication of crowdsourcing ideas in government, taking into account its special needs and specificities, and also for the development of advanced ICT infrastructures for this purpose, and appropriate application process models. With respect to govern- ment practice, it provides to government agencies advanced, efficient and effective methods and ICT tools, in order to conduct ‘citizen sourcing’, and collect useful information, knowledge, ideas and opinions from citizen, and the society in general, so that it can finally design better, more socially rooted, balanced and realistic public policies for addressing the growing problems of modern societies. Such tools can be for government policy makers valuable ‘sensors’, allowing the early identification of new problems, needs, ideas and trends in the society, so that appropriate policy responses can be developed. It is important that such approaches are gradually intro- duced and integrated in the policy formulation processes and practices, which can lead to a significant ‘renewal’ of them.

Further research is required concerning the multidimensional evaluation of the two proposed government crowdsourcing methodologies, through various ‘real-life’ applications (aiming at conducting crowdsourcing for various types of problems and public policies), and using various theoretical foundations and lenses from multiple

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288 E. Loukis and Y. Charalabidis

disciplines. Also, it would be interesting to conduct research towards the develop- ment of contest oriented government crowdsourcing methodologies, which include definition of a more specific task to be performed, competition among participants and monetary or other types of rewards.

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Chapter 13 Management of Complex Systems: Toward Agent-Based Gaming for Policy

Wander Jager and Gerben van der Vegt

Abstract In this chapter, we discuss the implications of complexities in societal sys- tems for management. After discussing some essential features of complex systems, we discuss the current focus of managers and management theory on prediction and the problems arising from this perspective. A short overview is given of the leadership and management literature, identifying what information is lacking concerning the management of complex systems. Next agent-based gaming, which allows for model- ing a virtual and autonomous population in a computer-game setting, is introduced as a tool to explore the possibilities to manage complex systems. The chapter concludes with a research agenda for management and leadership in complex systems.

13.1 Introduction

The Dexia bank run, which started with a tweet, and Project X Haren, that started with an open invitation on Facebook, demonstrate that social interactions may give rise to developments that spin out of control. In many different areas, managers in both the public and private sectors have to deal with the management of such com- plex behaving systems, e.g., the transition in the energy system, the development toward sustainability of our society, the developments in our health care system and the robustness of our financial–economic system, to name a few. Complexity the- ory applied to social systems contributes to our understanding of the mechanisms driving the sometimes turbulent developments in such social systems. It explains how the interactions between many individual agents may result in sometimes sur- prising processes of self-organization. In Chap. 4, Jager and Edmonds explained the principles of social complexity in more detail. A relevant contribution of the so- cial complexity perspective is that it explains under what conditions a social system is rather predictable, and under what conditions it may start behaving turbulently,

W. Jager (�) Groningen Center of Social Complexity Studies, University of Groningen, Groningen, The Netherlands e-mail: [email protected]

G. van der Vegt Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands

© Springer International Publishing Switzerland 2015 291 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_13

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making prediction in a classical sense impossible. For example, the car market has been a relatively predictable market for many years. For many brands and models, estimations of sales were being made that often lived up to their expectations. How- ever, the introduction of hybrid and electric cars in the existing market resulted in turbulences. One example would be that in 2013 virtually all produced Mitsubishi Outlander PHEV (plug-in hybrid electric vehicle) models were shipped to the Nether- lands due to a beneficial financial regime in that country. In 2014, the sales of this model dropped significantly1. It can be imagined that with the introduction of newer, more radical designs, such as the Google driverless car and new systems of car- sharing that utilize web based sharing tools new uncertainties are introduced in the car-market that may give rise to large turbulences and unpredictability in the market. In a way this reflects the uncertainties of a century ago when steam and gasoline were two viable and competing sources of propulsion.

Besides technological aspects, social aspects are also critical in the success or failure of introducing a new product or technology. Uncertainty, social norms, the spreading of rumors through social networks, and the behavior and opinions of role models all have significant effects on the success or failure and contribute to the turbulence during the introduction of new technology. Although social mechanisms underlying social complex phenomena have been identified in many social–scientific studies, the management of developments in turbulent systems remains problematic. At the same time, the more social interaction takes place in a system, which is usually the case with the introduction of radical new technology, the more turbulently it can behave, and the more important effective management of the system becomes. Yet little is known about the effective management of complex systems’ behavior. It is precisely in such turbulent situations where good management can result in favorable outcomes. However, bad management may result in disasters hitting the news, such as failed evacuation plans, civil war, or power shortages. Hence, the question—if we can develop a tool to identify managerial leadership styles that help better manage complex social systems—seems a highly relevant one.

13.2 Simulating Social Complex Phenomena

In improving our understanding of how social complex systems can be managed, the first step is getting a better understanding of how interactions between people may give rise to social complex phenomena. Due to the large scale of many social systems and the often unique events that happen, experimentation with real populations is not possible. However, it is possible to experiment with computer simulated populations of artificial people, through so-called agent-based modeling (ABM, see e.g., Chap. 4). This methodology has proven to be a suitable approach in exploring the dynamics of social complex systems and is gaining momentum in many disciplines (e.g., Gilbert

1 In December 2013 4988 Mitsubishi Outlander PHEV were registered, against 83 in January 2014 (Kane 2014).

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and Troitzsch 2005). In ABMs, agents are connected in a network and follow simple rules that are programmed at the individual level. In a model to investigate a particular social system, these rules can be derived from a more general behavioral theory as well as specific data originating from the field.

An example is the model of Van Eck et al. (2011), where the role of opinion leaders on the diffusion of a new product was explored. In an empirical study, they found that opinion leaders had a more central network position, possess more ac- curate knowledge about a product, and tend to be less susceptible to norms and more innovative. Implementing this in an agent-based model opened the possibility of introducing new products in a simulated market and comparing the effects of the presence versus absence of such opinion leaders in the system. The simulation experi- ments demonstrated that opinion leaders increase the speed of the information stream and the adoption process itself. Furthermore, they increase the maximum adoption percentage. The simulation model thus suggests that targeting these opinion leaders might be a viable marketing strategy.

ABM makes it possible to conduct many experiments and explore the conditions under which social systems start behaving turbulent, which implies that the social system gets into a state where fast and unforeseen developments take place, such as in fashion dynamics or social conflicts. ABM also allows for exploring how individuals change their behavior over time due to social interactions and allows for the identification of the key individuals in a social network. Interestingly, it opens the possibility to explore how certain management strategies would perform in different conditions of turbulence.

13.3 Managing Social Complex Phenomena

From a social scientific experimental perspective, one would suggest running exper- imental designs as to identify the effects of different strategies. Comparing different interventions in a simulation model would yield information on what interventions are most effective. However, whereas empirically validated ABMs clearly provide a relevant perspective on identifying the social complexities in many social systems, their application in experimentally testing the effects of operational management strategies remains problematic in turbulent conditions. Two key reasons cause that experimentation with predefined leadership interventions are problematic.

First, in a turbulent system the effects of a specific management strategy may vary considerably. This is because in one simulation run such a strategy may be on spot with the developments that take place in the simulation, whereas in another simulation run the same timing of that specific strategy may be very inconvenient. As a result, specific management strategies may have different consequences, making it difficult to draw conclusions about their effectiveness.

Relaxing the experimental rigor of adopting certain management strategies at an identical moment in the simulation would allow for tracking the developments in the simulation and adopting the strategy at a moment that seems most effective. This

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implies that the experimentation bears an adaptive character, responding with the manipulation on developments in the simulation run as they evolve. However, here we run into the problem that in understanding effective management of a system it is not sufficient to study specific leadership behaviors in isolation. Rather, the management of social complex phenomena often implies that a sequence of behaviors takes place, and that (unforeseen) responses of other stakeholders have to be addressed as well. In regular experimental designs as used in the social sciences, this would result in an exponential growth of possible strategies to be tested. For example, given a simulated simple market with ten competing products, a manager trying to stimulate the sales of an “innovative green” product may decide on pricing, quality of the product, and type of marketing. Each of these elements already implies a choice from a wide array of possibilities. In selecting one possibility, the other product managers have an equal number of possibilities that even interact with each other. Many decision- making contexts are much more complex than this example, as they involve many stakeholders with different and sometimes conflicting goals, different valuations, and perspectives on outcomes, and different responsibilities and influencing power. And realizing that many complex social processes may span longer periods of time (e.g., years), it is clear that testing the effect of particular strategies in managing turbulent behaving social systems is not feasible in ABMs.

However, on a more aggregate level we may identify consistent patterns in man- agerial behavior, such as being adaptive to change, collecting information, and having a long-term perspective. These can be understood as a management or leadership style, and we hypothesize that these styles are far less divergent than operational man- agement strategies, so that their effectiveness may be observed from the interaction between managers and a complex social system.

13.4 Leadership and Management in Complex Systems

The dominant paradigm in leadership research has been to examine the relation- ships between leadership styles, such as task- and relationship-oriented behavior (Bass 1990), and the outcomes of these behaviors, including follower attitudes (sat- isfaction, commitment, trust), behaviors (extra effort, cooperation, organizational citizenship behavior), and performance or unit level outcomes, like group cohesion, collective efficacy, and unit performance. Within this paradigm, the vast majority of studies has examined such relationships at a single period in time and has ignored the dynamic character of most of these relationships. In the field of leadership studies, this state of affairs is unfortunate because: (1) the effectiveness of specific leader- ship behaviors may depend on their timing, and (2) because leadership essentially represents a dynamic influence process between leader and followers that unfolds over time (Uhl-Bien and Marion 2009). Scholars have, therefore, recently started to develop theoretical frameworks that address these shortcomings, and now focused on team leadership as a dynamic process necessitating adaptive changes in leader behavior.

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Kozlowski et al. (2008) proposed that the effects of certain leadership styles and actions may depend on their timing. They developed a team leadership framework that portrays team development as a cyclical and dynamic process, which requires leaders to adapt their leadership style to the various phases of team development and to the different adaptation needs of the team at each phase. This means that certain leadership actions and interventions may lead to desirable outcomes at a certain phase of the relationship but not in another phase. The theoretical framework proposes that for leaders to be adaptive, they must be aware of the key contingencies that necessitate shifts in leadership behavior, and they must possess the underlying skills needed to help the team resolve challenges. In these models, the leader has two major responsibilities or functions.

One leadership function is instructional and regulatory in nature. By responding to variations in team tasks by goal setting, performance monitoring, diagnosis, and feed- back, the leader may help or stimulate team members to develop the knowledge and skills that contribute to team effectiveness. Leadership behaviors associated with this leadership function are transactional, structure-initiating, monitoring, authoritative, and directive leadership.

The second leadership function is developmental. As teams acquire the necessary knowledge and skills, the leader role shifts to help the team develop progressively more complex skills and capabilities (Kozlowski et al. 2008). Leadership behav- iors associated with this leadership function are transformational, consideration, coaching, empowerment, facilitative, and participative leadership. Over time, this dual-pronged leadership process is hypothesized to yield team-level regulation and adaptive teams.

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