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Please read the attached chapter to answer below question. Please write at least 550 words.The author of chapter 1 discusses several developments that influence policy-making. Select one of the developments in chapter 1 and describe how that development can influence policy to solve a specific problem. policy_practice_and_digital_science_2015_edition_chapter_1.pdf Unformatted Attachment Preview 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 information and communications technology (ICT) developments affect all parts of the policy-making cycle and result in drastic changes in the way policies are developed. To take advantage of these developments in the digital world, new approaches, concepts, instruments, and methods are needed, which are able to deal with societal 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 innovating 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 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 1 2 M. Janssen and M. A. Wimmer take advantage of these developments in the digital world, new approaches, concepts, 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, computational 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 facilitating 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 governments 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 together 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 phenomena 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 policymaking 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 politicians Policy formulation Policymakers experts Policy implementation Policy enforcement and evaluation Inspection and enforcement agencies Fig. 1.1 Overview of policy cycle and stakeholders citizens Policy execution businesses Administrative organizations 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 implementation 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 formulation, implementation decisions might be more difficult to realize, policies might bring other benefits than intended, execution costs might be higher and so on. Typically, 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 societal 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. 1 Introduction to Policy-Making in the Digital Age evidence-based 5 will of the people public interest fair balancing of interests listening accountability citizen involvement transparancy Policy enforcement and evaluation Policy formulation Policy execution Policy implementation protection of individual rights transparancy evidence-based accountability timelessness equal access reliable efficiency flexible fair honesty responsiveness efficiency robust 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 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 interdependencies 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 proposed 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. 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 associated with many factors affecting policy-making, the complexity associated with the interdependencies between individual parts, and the stochastic elements associated with the randomness and unpredictable behavior of transactions complicates the simulations. Computer simulations for examining, explaining, and predicting social 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. 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. 2. 3. 4. Practice-driven Employs modeling techniques Needs the knowledge coming from various disciplines 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 EgovPoli ... Purchase answer to see full attachment