Tyrant tunes 6.7 powerstroke review

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

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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: m.f.w.h.a.janssen@tudelft.nl

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.

w.jager@rug.nl

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.