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Public Administration and Information
Technology
Volume 10
Series Editor
Christopher G. Reddick
San Antonio, Texas, USA
More information about this series at http://www.springer.com/series/10796
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
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
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
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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 objectiveson employment, innovation,
education, social inclusion, and climate/energyto be reached by 2020. Along with
this, Europe 2020 has established four priority areassmart growth, sustainable
growth, inclusive growth, and later added: A strong and effective system of eco-
nomic governancedesigned 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
v
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 ScienceIntegrating Complex
Systems, Social Simulation, and Public Administration in Policy Research, is one
of the exciting results of the activities of eGovPoliNetfusing 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
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
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
ix
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 Krmer, Tina Balke, Marco
Gavanelli, Stefano Bragaglia, Federico Chesani, Michela Milano
and Jrn Kohlhammer
16 Analysis of Five Policy Cases in the Field of Energy Policy . . . . . . . . . 355
Dominik Br, 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
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 Br 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
xi
xii Contributors
Zamira Dzhusupova Department of Public Administration 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 &
TechnologyHellas, Thessaloniki, Greece
Bram Klievink Faculty of Technology, Policy and Management, Delft University
of Technology, Delft, The Netherlands
Jrn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD, Darmstadt, Germany
Christopher Koliba University of Vermont, Burlington, VT, USA
Michel Krmer 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
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 &
TechnologyHellas, 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 &
TechnologyHellas, Thessaloniki, Greece; University of Macedonia, Thessaloniki,
Greece
Konstantinos Tarabanis Information Technologies Institute, Centre for Research
& TechnologyHellas, Thessaloniki, Greece; University of Macedonia, Thessa-
loniki, Greece
Dmitrii Trutnev ITMO University, St. Petersburg, Russia
Gerben van der Vegt 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
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: [emailprotected]
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
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
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
citizen
s
Policy formulation
Policy
implementation
Policy
execution
Policy
enforcement and
evaluation
politicians
Policy-
makers
Administrative
organizations
b
u
sin
esses
Inspection and
enforcement agencies
experts
Fig. 1.1 Overview of policy cycle and stakeholders
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 (Jrgensen 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 Jrgensen
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.
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.
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 citizens 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 (
