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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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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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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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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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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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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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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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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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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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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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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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