Please find 5 research article related to the topics discussed in Chapter 5.
Topics could include:
- System dynamics
- Multi-actor systems modeling
- Complex adaptive systems modeling
or any other topic contained in the chapter.
After reading and finding the 5 research articles, create an annotated bibliography. The deliverable will be one MS Word file that you will submit here. Please make sure that there are at least 150 words per article, this does not include references or copies of the abstract or introduction, these must be what you come up with.
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UNFORMATTED ATTACHMENT PREVIEW
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 Faculty of Technology, Policy, and Management Delft University of Technology Delft The Netherlands Ameneh Deljoo Faculty of Technology, Policy, and Management Delft University of Technology Delft 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. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) 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 communication 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 economic 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 overcoming 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 cofunded an international support action called eGovPoliNet2 . The overall objective of eGovPoliNet was to create an international, cross-disciplinary community of researchers 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, eGovPoliNet 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, methods, 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 comparative 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 descriptions from all over the globe. Specifically, it demonstrates how the explosive growth in data, computational power, and social media creates new opportunities for policymaking 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 computer 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 technology, 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 objectives 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 stakeholders 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.policycommunity.eu Preface vii of how joint multidisciplinary research can bring more effective and resilient findings 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 . . . . . . . . . . . . . . . . . Marijn Janssen and Maria A. Wimmer 2 Educating Public Managers and Policy Analysts in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christopher Koliba and Asim Zia 15 The Quality of Social Simulation: An Example from Research Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Petra Ahrweiler and Nigel Gilbert 35 3 1 4 Policy Making and Modelling in a Complex World . . . . . . . . . . . . . . . . Wander Jager and Bruce Edmonds 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Erik Pruyt 75 Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making: A Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter Davis and Petra Ahrweiler 95 6 57 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 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 Contributors Tanko Ahmed National Institute for Policy and Strategic Studies (NIPSS), Jos, Nigeria Petra Ahrweiler EA European Academy of Technology and Innovation Assessment GmbH, Bad Neuenahr-Ahrweiler, Germany Tjeerd C. Andringa University College Groningen, Institute of Artificial Intelligence 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 xi xii Contributors Zamira Dzhusupova Department of Public Administration and Development Management, 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, Rotterdam, Rotterdam, The Netherlands Erasmus University 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 Contributors xiii Andreas Ligtvoet Faculty of Technology, Policy, and Management, Delft University 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, Thessaloniki, 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 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: email@example.com 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 EgovPoliNet project has created an international community in ICT solutions for governance and policy-modeling. The “policy-making 2.0” LinkedIn community has a large number of members from different disciplines and backgrounds representing practice and academia. This book is the product of this project in which a large number of persons from various disciplines 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 knowledge 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 social 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 research, 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 1 Introduction to Policy-Making in the Digital Age 9 governance processes. Policy informatics community has created Listserv called Policy Informatics Network (PIN-L). E-government policy-making is closely connected to “data science.” Data science is the ability to find answers from larger volumes of (un)structured data (Davenport and Patil 2012). Data scientists find and interpret rich data sources, manage large amounts of data, create visualizations to aid in understanding data, build mathematical 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 represent 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 combination 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 10 M. Janssen and M. A. Wimmer this chapter. They identify two types of public servants that need to be educated. The policy informatics include the savvy public manager and the policy informatics analyst. This chapter can be used as a basis to adopt interdisciplinary approaches and include policy informatics in the public administration curriculum. Petra Ahrweiler and Nigel Gilbert discuss the need for the quality of simulation modeling in their Chap. 3. Developing simulation is always based on certain assumptions 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 Majstorovic, 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 comparison 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, simulation, 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 modeling 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 1 Introduction to Policy-Making in the Digital Age 11 engagement in policy-making in their Chap. 9. Various engagement tools are discussed 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 containing 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 processes 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 policy 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 policymodeling are presented. In all the cases access for nonexperts to the computational models by information visualization technologies was realized. 12 M. Janssen and M. A. Wimmer Applications and Practices Different projects have been initiated to study the best suitable transition process towards renewable energy. In the Chap. 16 by Dominik Bär, Maria A. Wimmer, Jozef Glova, Anastasia Papazafeiropoulou,and Laurence Brooks five of these projects are analyzed and compared. They please for transferring models from one country to other countries to facilitate learning. Lyudmila Vidyasova, Andrei Chugunov, and Dmitrii Trutnev present experiences from Russia in their Chap. 17. They argue that informational, analytical, and forecasting 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 practices 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 disciplines 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, governance, policy-modeling, social simulation, and visualization are investigated. Policymaking 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 techniques (hybrid models) can provide much more insight and reliable outcomes, gaming in which all kind of stakeholders are involved open new ways of innovative policymaking. 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. 1 Introduction to Policy-Making in the Digital Age 13 References Banks J (1998) Handbook of simulation: principles, methodology, advances, applications, and practice. Wiley, New York Bertot JC, Jaeger PT, Hansen D (2012) The impact of polices on government social media usage: Issues, challenges, and recommendations. Gov Inform Q 29:30–40 Burt E (2011) Introduction to the freedom of information special edition: emerging perspectives, critical reflections, and the need for further research. Inform Polit 16(2):91–92. Coleman S, Moss G (2008) Governing at a distance—politicians in the blogosphere. Inform Polit 12(1–2):7–20. Davenport TH, Patil DJ (2012) Data scientist: the sexiest job of the 21st century. Harv Bus Rev 90(10):70–76 Dawes SS, Janssen M (2013) Policy informatics: addressing complex problems with rich data, computational tools, and stakeholder engagement. Paper presented at the 14th annual international conference on digital government research, Quebec City, Canada De Reuver M, Stein S, Hampe F (2013) From eparticipation to mobile participation: designing a service platform and business model for mobile participation. Inform Polit 18(1):57–73 European_Commission (2003) Directive 2003/98/EC of the European Parliament and of the council of 17 November 2003 on the re-use of public sector information. http://ec.europa.eu/ information_society/policy/psi/rules/eu/index_en.htm. Accessed 12 Dec 2012 Janssen M, Estevez E (2013) Lean government and platform-based governance—doing more with less. Gov Inform Quert 30(suppl 1):S1–S8 Janssen M, Charalabidis Y, Zuiderwijk A (2012) Benefits, adoption barriers and myths of open data and open government. Inform Syst Manage 29(4):258–268 Janssen M, Estevez E, Janowski T (2014) Interoperability in big, open, and linked data— organizational maturity, capabilities, and data portfolios. Computer 47(10):26–31 Jørgensen TB, Bozeman B (2007) Public values: an inventory. Adm Soc 39(3):354–381 Koliba C, Zia A (2012) Governance Informatics: using computer simulation models to deepen situational awareness and governance design considerations policy informatics. MIT Press, Cambridge. Krishnamurthy R, Bhagwatwar A, Johnston EW, Desouza KC (2013) A glimpse into policy informatics: the case of participatory platforms that generate synthetic empathy. Commun Assoc Inform Syst 33(Article 21):365–380. Law AM, Kelton WD (1991) Simulation modeling and analysis 2nd ed. McGraw-Hill, New York Moore MH (1995) Creating public value: strategic management in government. Harvard University Press, Cambridge Obama B (2009) Memorandum for the Heads of executive Departments and Agencies: transparency and open government. Retrieved February 21, 2013, from http://www.whitehouse.gov/ the_press_office/Transparency_and_Open_Government Pidd M (1992) Computer simulation in management science, 3rd ed. John Wiley, Chichester Slaviero C, Maciel C, Alencar F, Santana E, Souza P (2010) Designing a platform to facilitate the development of virtual e-participation environments. Paper presented at the ICEGOV ’10 proceedings of the 4th international conference on theory and practice of electronic governance, Beijing Stewart JJ, Hedge DM, Lester JP (2007) Public policy: an evolutionary approach 3rd edn. Cengage Learning, Wadsworth Stoker G (2006) Public value management: a new narrative for networked governance? Am Rev Public Adm 3(1):41–57 Surowiecki J (2004) The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business economies, societies and nations. Doubleday Welch EW (2012) The rise of participative technologies in government. In: Shareef MA, Archer N, Dwivedi YK, Mishra A, Pandey SK (eds) Transformational government through eGov practice: socioeconomic, cultural, and technological issues. Emerald Group Publishing Limited 14 M. Janssen and M. A. Wimmer Wimmer MA, Furdik K, Bicking M, Mach M, Sabol T, Butka P (2012a) Open collaboration in policy development: concept and architecture to integrate scenario development and formal policy modelling. In: Charalabidis Y, Koussouris S (eds) Empowering open and collaborative governance. Technologies and methods for online citizen engagement in public policy making. Springer, Berlin, pp 199–219 Wimmer MA, Scherer S, Moss S, Bicking M (2012b) Method and tools to support stakeholder engagement in policy development the OCOPOMO project. Int J Electron Gov Res (IJEGR) 8(3):98–119 Zuiderwijk A, Janssen M (2013) A coordination theory perspective to improve the use of open data in policy-making. Paper presented at the 12th conference on Electronic Government (EGOV), Koblenz Zuiderwijk A, Helbig N, Gil-García JR, Janssen M (2014) Innovation through open data—a review of the state-of-the-art and an emerging research agenda. J Theor Appl Electron Commer Res 9(2):I–XIII. Chapter 2 Educating Public Managers and Policy Analysts in an Era of Informatics Christopher Koliba and Asim Zia Abstract In this chapter, two ideal types of practitioners who may use or create policy informatics projects, programs, or platforms are introduced: the policy informatics-savvy public manager and the policy informatics analyst. Drawing from our experiences in teaching an informatics-friendly graduate curriculum, we discuss the range of learning competencies needed for traditional public managers and policy informatics-oriented analysts to thrive in an era of informatics. The chapter begins by describing the two different types of students who are, or can be touched by, policy informatics-friendly competencies, skills, and attitudes. Competencies ranging from those who may be users of policy informatics and sponsors of policy informatics projects and programs to those analysts designing and executing policy informatics projects and programs will be addressed. The chapter concludes with an illustration of how one Master of Public Administration (MPA) program with a policy informatics-friendly mission, a core curriculum that touches on policy informatics applications, and a series of program electives that allows students to develop analysis and modeling skills, designates its informatics-oriented competencies. 2.1 Introduction The range of policy informatics opportunities highlighted in this volume will require future generations of public managers and policy analysts to adapt to the opportunities and challenges posed by big data and increasing computational modeling capacities afforded by the rapid growth in information technologies. It will be up to the field’s Master of Public Administration (MPA) and Master of Public Policy (MPP) programs to provide this next generation with the tools needed to harness the wealth of data, information, and knowledge increasingly at the disposal of public C. Koliba () University of Vermont, 103 Morrill Hall, 05405 Burlington, VT, USA e-mail: firstname.lastname@example.org A. Zia University of Vermont, 205 Morrill Hall, 05405 Burlington, VT, USA e-mail: email@example.com © 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_2 15 16 C. Koliba and A. Zia administrators and policy analysts. In this chapter, we discuss the role of policy informatics in the development of present and future public managers and policy analysts. Drawing from our experiences in teaching an informatics-friendly graduate curriculum, we discuss the range of learning competencies needed for traditional public managers and policy informatics-oriented analysts to thrive in an era of informatics. The chapter begins by describing the two different types of students who are, or can be touched by, policy informatics-friendly competencies, skills, and attitudes. Competencies ranging from those who may be users of policy informatics and sponsors of policy informatics projects and programs to those analysts designing and executing policy informatics projects and programs will be addressed. The chapter concludes with an illustration of how one MPA program with a policy informatics-friendly mission, a core curriculum that touches on policy informatics applications, and a series of program electives that allows students to develop analysis and modeling skills, designates its informatics-oriented competencies. 2.2 Two Types of Practitioner Orientations to Policy Informatics Drawn from our experience, we find that there are two “ideal types” of policy informatics practitioner, each requiring greater and greater levels of technical mastery of analytics techniques and approaches. These ideal types are: policy informatics-savvy public managers and policy informatics analysts. A policy informatics-savvy public manager may take on one of two possible roles relative to policy informatics projects, programs, or platforms. They may play instrumental roles in catalyzing and implementing informatics initiatives on behalf of their organizations, agencies, or institutions. In the manner, they may work with technical experts (analysts) to envision possible uses for data, visualizations, simulations, and the like. Public managers may also be in the role of using policy informatics projects, programs, or platforms. They may be in positions to use these initiatives to ground decision making, allocate resources, and otherwise guide the performance of their organizations. A policy informatics analyst is a person who is positioned to actually execute a policy informatics initiative. They may be referred to as analysts, researchers, modelers, or programmers and provide the technical assistance needed to analyze databases, build and run models, simulations, and otherwise construct useful and effective policy informatics projects, programs, or platforms. To succeed in either and both roles, managers and analysts will require a certain set of skills, knowledge, or competencies. Drawing on some of the prevailing literature and our own experiences, we lay out an initial list of potential competencies for consideration. 2 Educating Public Managers and Policy Analysts in an Era of Informatics 2.2.1 17 Policy Informatics-Savvy Public Managers To successfully harness policy informatics, public managers will likely not need to know how to explicitly build models or manipulate big data. Instead, they will need to know what kinds of questions that policy informatics projects or programs can answer or not answer. They will need to know how to contract with and/or manage data managers, policy analysts, and modelers. They will need to be savvy consumers of data analysis and computational models, but not necessarily need to know how to technically execute them. Policy informatics projects, programs, and platforms are designed and executed in some ways, as any large-scale, complex project. In writing about the stages of informatics project development using “big data,” DeSouza lays out project development along three stages: planning, execution, and postimplementation. Throughout the project life cycle, he emphasizes the role of understanding the prevailing policy and legal environment, the need to venture into coalition building, the importance of communicating the broader opportunities afforded by the project, the need to develop performance indicators, and the importance of lining up adequate financial and human resources (2014). Framing what traditional public managers need to know and do to effectively interface with policy informatics projects and programs requires an ability to be a “systems thinker,” an effective evaluator, a capacity to integrate informatics into performance and financial management systems, effective communication skills, and a capacity to draw on social media, information technology, and e-governance approaches to achieve common objectives. We briefly review each of these capacities below. Systems Thinking Knowing the right kinds of questions that may be asked through policy informatics projects and programs requires public managers to possess a “systems” view. Much has been written about the importance of “systems thinking” for public managers (Katz and Kahn 1978; Stacey 2001; Senge 1990; Korton 2001). Taking a systems perspective allows public managers to understand the relationship between the “whole” and the “parts.” Systems-oriented public managers will possess a level of situational awareness (Endsley 1995) that allows them to see and understand patterns of interaction and anticipate future events and orientations. Situational awareness allows public mangers to understand and evaluate where data are coming from, how best data are interpreted, and the kinds of assumptions being used in specific interpretations (Koliba et al. 2011). The concept of system thinking laid out here can be associated with the notion of transition management (Loorbach 2007). Process Orientations to Public Policy The capacity to view the policy making and implementation process as a process that involves certain levels of coordination and conflict between policy actors is of critical importance for policy informaticssavvy public managers and analysts. Understanding how data are used to frame problems and policy solutions, how complex governance arrangements impact policy implementation (Koliba et al. 2010), and how data visualization can be used to 18 C. Koliba and A. Zia facilitate the setting of policy agendas and open policy windows (Kingdon 1984) is of critical importance for public management and policy analysts alike. Research Methodologies Another basic competency needed for any public manager using policy informatics is a foundational understanding of research methods, particularly quantitative reasoning and methodologies. A foundational understanding of data validity, analytical rigor and relevance, statistical significance, and the like are needed to be effective consumers of informatics. That said, traditional public managers should also be exposed to qualitative methods as well, refining their powers of observation, understanding how symbols, stories, and numbers are used to govern, and how data and data visualization and computer simulations play into these mental models. Performance Management A key feature of systems thinking as applied to policy informatics is the importance of understanding how data and analysis are to be used and who the intended users of the data are (Patton 2008). The integration of policy informatics into strategic planning (Bryson 2011), performance management systems (Moynihan 2008), and ultimately woven into an organization’s capacity to learn, adapt, and evolve (Argyis and Schön 1996) are critically important in this vein. As policy informatics trends evolve, public managers will likely need to be exposed to uses of decision support tools, dashboards, and other computationally driven models and visualizations to support organizational performance. Financial Management Since the first systemic budgeting systems were put in place, public managers have been urged to use the budgeting process as a planning and evaluation tool (Willoughby 1918). This approach was formally codified in the 1960s with the planning–programming–budgeting (PPB) system with its focus on planning, managerial, and operational control (Schick 1966) and later adopted into more contemporary approaches to budgeting (Caiden 1981). Using informative projects, programs, or platforms to make strategic resource allocation decisions is a necessary given and a capacity that effective public managers must master. Likewise, the policy analyst will likely need to integrate financial resource flows and costs into their projects. Collaborative and Cooperative Capacity Building The development and use of policy informatics projects, programs, or platforms is rarely, if ever, undertaken as an individual, isolated endeavor. It is more likely that such initiatives will require interagency, interorganizational, or intergroup coordination. It is also likely that content experts will need to be partnered with analysts and programmers to complete tasks and execute designs. The public manager and policy analyst must both possess the capacity to facilitate collaborative management functions (O’Leary and Bingham 2009). Basic Communication Skills This perhaps goes without saying, but the heart of any informatics project lies in the ability to effectively communicate findings and ideas through the analysis of data. 2 Educating Public Managers and Policy Analysts in an Era of Informatics 19 Social Media, Information Technology, and e-Governance Awareness A final competency concerns public managers’ capacity to deepen their understanding of how social media, Web-based tools, and related information technologies are being employed to foster various e-government, e-governance, and related initiatives (Mergel 2013). Placing policy informatics projects and programs within the context of these larger trends and uses is something that public managers must be exposed to. Within our MPA program, we have operationalized these capacities within a fourpoint rubric that outlines what a student needs to do to demonstrate meeting these standards. The rubric below highlights 8 of our program’s 18 capacities. All 18 of these capacities are situated under 1 of the 5 core competencies tied to the accreditation standards of the Network of Schools of Public Affairs and Administration (NASPAA), the professional accrediting association in the USA, and increasingly in other countries as well, for MPA and MPP programs. A complete list of these core competencies and the 18 capacities nested under them are provided in Appendix of this chapter. The eight capacities that we have singled out as being the most salient to the role of policy informatics in public administration are provided in Table 2.1. The rubric follows a four-point scale, ranging from “does not meet standard,” “approaches standard,” “meets standard,” and “exceeds standard.” 2.2.2 Policy Informatics Analysts A second type of practitioner to be considered is what we are referring to as a “policy informatics analyst.” When considering the kinds of competencies that policy informatics analysts need to be successful, we first assume that the basic competencies outlined in the prior section apply here as well. In other words, effective policy informatics analysts must be systems thinkers in order to place data and their analysis into context, be cognizant of current uses of decision support systems (and related platforms) to enable organizational learning, performance, and strategic planning, and possess an awareness of e-governance and e-government initiatives and how they are transforming contemporary public management and policy planning practices. In addition, policy analysts must possess a capacity to understand policy systems: How policies are made and implemented? This baseline understanding can then be used to consider the placement, purpose, and design of policy informatics projects or programs. We lay out more specific analyst capacities below. Advanced Research Methods of Information Technology Applications In many instances, policy informatics analysts will need to move beyond meeting the standard. This is particularly true in the area of exceeding the public manager standards for research methods and utilization of information technology. It is assumed that effective policy informatics analysts will have a strong foundation in quantitative methodologies and applications. To obtain these skills, policy analysts will need to move beyond basic surveys of research methods into more advanced research methods curriculum. Does not meet standard Does not understand the basic operations of systems and networks; cannot explain why understanding cases and contexts in terms of systems and networks is important Possesses limited capacity to utilize policy streams and policy stage heuristics model to describe observed phenomena. Can isolate simple problems from solutions, but has difficultly separating ill-structured problems from solutions Possesses a limited capacity to employ survey, interview, or other social research methods to a focus area. Can explain why it is important to undertake program or project evaluation, but possesses limited capacity to actually carrying it out Capacity Capacity to apply knowledge of system dynamics and network structures in public administration practices Capacity to apply policy streams, cycles, systems foci upon past, present, and future policy issues, and to understand how problem identification impacts public administration Capacity to employ quantitative and qualitative research methods for program evaluation and action research Table 2.1 Public manager policy informatics capacities Demonstrates a capacity to employ survey, interview, or other social research methods to a focus area and an understanding of how such data and analysis are useful in administrative practice. Can provide a rationale for undertaking program/project Possesses some capacity to utilize policy streams and to describe policy stage heuristics model observed phenomena. Possesses some capacity to define how problems are framed by different policy actors Can provide a basic overview of what system dynamics and network structures are and illustrate how they are evident in particular cases and contexts Approaches standard Can provide a piece of original analysis of an observed phenomenon employing one qualitative or quantitative methodology effectively. Possesses capacity to commission a piece of original research. Can provide a detailed account for how a Employs a policy streams or policy stage heuristics model approach to the study of observed phenomena. Can demonstrate how problem definition is defined within specific policy contexts and deconstruct the relationship between problem definitions and solutions Is able to undertake an analysis of a complex public administration issue, problem, or context using basic system dynamics and network frameworks Meets standard Demonstrates the capacity to undertake an independent research agenda through employing one or more social research methods around a topic of study of importance to public administration. Can demonstrate the successful execution of a program or Employs a policy streams or policy stage heuristics model approach to the diagnosis of a problem raised in real-life policy dilemmas. Can articulate how conflicts over problem definition contribute to wicked policy problems Can apply system dynamics and network frameworks to existing cases and contexts to derive working solutions or feasible alternatives to pressing administrative and policy problems Exceeds standard 20 C. Koliba and A. Zia Does not meet standard Can provide an explanation of why performance goals and measures are important in public administration, but cannot apply this reasoning to specific contexts Can identify why budgeting and sound fiscal management practices are important, but cannot analyze how and/or if such practices are being used within specific contexts Can explain why it is important for public administrators to be open and responsive practitioners in a vague or abstract way, but cannot provide specific explanations or justifications applied to particular contexts Capacity Capacity to apply sound performance measurement and management practices Capacity to apply sound financial planning and fiscal responsibility Capacity to achieve cooperation through participatory practices Table 2.1 (continued) Meets standard Can identify instances in specific cases or contexts where a public administrator demonstrated or failed to demonstrate inclusive practices Can identify fiscal planning and budgeting practices for a particular situation or context, but has limited capacity to evaluate the effectiveness of a financial management system Can identify the performance management considerations for a particular situation or context, but has limited capacity to evaluate the effectiveness of performance management systems Can demonstrate how inclusive practices and conflict management leads to cooperation for forming coalitions and collaborative practices Can identify and analyze financial management systems, needs, and emerging opportunities within a specific organization or network Can identify and analyze performance management systems, needs, and emerging opportunities within a specific organization or network evaluation and explain what the program or project evaluation possible goals and outcomes of project should be structured such an evaluation might be within the context of a specific program or project Approaches standard Can orchestrate any of the following: coalition building across units, organizations, or institutions, effective teamwork, and/or conflict management Can provide new insights into the financial management challenges facing an organization or network, and suggest alternative design and budgeting scenarios Can provide new insights into the performance management challenges facing an organization or network, and suggest alternative design and measurement scenarios project evaluation or the successful utilization of a program or project evaluation to improve administrative practice Exceeds standard 2 Educating Public Managers and Policy Analysts in an Era of Informatics 21 Can identify instances in specific cases or context where a public administrator successfully or unsuccessfully demonstrated a capacity to use IT to foster innovation, improve services, or deepen accountability. Analysis at this level is relegated to descriptions and thin analysis IT information technology Can explain why information technology is important to contemporary workplaces and public administration environments. Possesses direct experience with information technology, but little understanding for how IT informs professional practice Capacity to undertake high quality electronically mediated communication and utilize information systems and media to advance objectives Approaches standard Possesses the capacity to write documents that are free of grammatical errors and are organized in a clear and efficient manner. Possesses the capacity to present ideas in a professional manner. Suffers from a lack of consistency in the presentation of material and expression or original ideas and concepts Does not meet standard Capacity to undertake Demonstrates some ability to high quality oral, express ideas verbally and in written communication writing. Lacks consistent capacity to present and write Capacity Table 2.1 (continued) Can identify how IT impacts workplaces and public policy. Can diagnose problems associated with IT tools, procedures, and uses Is capable of consistently expressing ideas verbally and in writing in a professional manner that communicates messages to intended audiences Meets standard Demonstrates a capacity to view IT in terms of systems design. Is capable of working with IT professionals in identifying areas of need for IT upgrades, IT procedures, and IT uses in real setting Can demonstrate some instances in which verbal and written communication has persuaded others to take action Exceeds standard 22 C. Koliba and A. Zia 2 Educating Public Managers and Policy Analysts in an Era of Informatics 23 Competencies in advanced quantitative methods in which students learn to clean and manage large databases, perform advanced statistical tests, develop linear regression models to describe causal relationship, and the like are needed. Capacity to work across software platforms such as Excel, Statistical Package for the Social Sciences (SPSS), Analytica, and the like are important. Increasingly, the capacity to triangulate different methods, including qualitative approaches such as interviews, focus groups, participant observations is needed. Data Visualization and Design Not only must analysts be aware of how these methods and decision support platforms may be used by practitioners but also they must know how to design and implement them. Therefore, we suggest that policy informatics analysts be exposed to design principles and how they may be applied to decision support systems, big data projects, and the like. Policy informatics analysts will need to understand and appreciate how data visualization techniques are being employed to “tell a story” through data. Figure 2.1 provides an illustration of one student’s effort to visualize campaign donations to state legislatures from the gas-extraction (fracking) industry undertaken by a masters student, Jeffery Castle for a system analysis and strategic management class taught by Koliba. Castle’s project demonstrates the power of data visualization to convey a central message drawing from existing databases. With a solid research methods background and exposure to visualization and design principles in class, he was able to develop an insightful policy informatics project. Basic to Advanced Programming Language Skills Arguably, policy informatics analysts will possess a capacity to visualize and present data in a manner that is accessible. Increasingly, web-based tools are being used to design user interfaces. Knowledge of JAVA and HTML are likely most helpful in these regards. In some instances, original programs and models will need to be written through the use of programming languages such as Python, R, C++, etc. The extent to which existing software programs, be they open source or proprietary, provide enough utility to execute policy informatics projects, programs, or platforms is a continuing subject of debate within the policy informatics community. Exactly how much and to what extent specific programming languages and software programs are needing to be mastered is a standing question. For the purposes of writing this chapter, we rely on our current baseline observations and encourage more discussion and debate about the range of competencies needed by successful policy analysts. Basic to More Advanced Modeling Skills More advanced policy informatics analysts will employ computational modeling approaches that allow for the incorporation of more complex interactions between variables. These models may be used to capture systems as dynamic, emergent, and path dependent. The outputs of these models may allow for scenario testing through simulation (Koliba et al. 2011). With the advancement of modeling software, it is becoming easier for analysts to develop system dynamics models, agent-based models, and dynamic networks designed to simulate the features of complex adaptive systems. In addition, the ability to manage and store data and link or wrap databases is often necessary. 24 C. Koliba and A. Zia Fig. 2.1 Campaign contributions to the Pennsylvania State Senate and party membership. The goal of this analysis is to develop a visualization tool to translate publically available campaign contribution information into an easily accessible, visually appealing, and interactive format. While campaign contribution data are filed and available to the public through the Pennsylvania Department of State, it is not easily synthesized. This analysis uses a publically available database that has been published on marcellusmoney.org. In order to visualize the data, a tool was used that allows for the creation of a Sankey diagram that is able to be manipulated and interacted within an Internet browser. A Sankey diagram visualizes the magnitude of flow between the nodes of a network (Castle 2014) The ability of analysts to draw on a diverse array of methods and theoretical frameworks to envision and create models is of critical importance. Any potential policy informatics project, program, or platform will be enabled or constrained by the modeling logic in place. With a plurality of tools at one’s disposal, policy informatics analysts will be better positioned to design relevant and legitimate models. 2 Educating Public Managers and Policy Analysts in an Era of Informatics 25 Fig. 2.2 End-stage renal disease (ESRD) system dynamics population model. To provide clinicians and health care administrators with a greater understanding of the combined costs associated with the many critical care pathways associated with ESRD, a system dynamics model was designed to simulate the total expenses of ESRD treatment for the USA, as well as incidence and mortality rates associated with different critical care pathways: kidney transplant, hemodialysis, peritoneal dialysis, and conservative care. Calibrated to US Renal Data System (USRDS) 2013 Annual and Historical Data Report and the US Census Bureau for the years 2005–2010, encompassing all ESRD patients under treatment in the USA from 2005 to 2010, the ESRD population model predicts the growth and costs of ESRD treatment type populations using historical patterns. The model has been calibrated against the output of the USRDS’s own prediction for the year 2020 and also tested by running historic scenarios and comparing the output to existing data. Using a web interface designed to allow users to alter certain combinations of parameters, several scenarios are run to project future spending, incidence, and mortalities if certain combinations of critical care pathways are pursued. These scenarios include: a doubling of kidney donations and transplant rates, a marked increase in the offering of peritoneal dialysis, and an increase in conservative care routes for patients over 65. The results of these scenario runs are shared, demonstrating sizable cost savings and increased survival rates. Implications of clinical practice, public policy, and further research are drawn (Fernandez 2013) Figure 2.2 provides an illustration of Luca Fernandez’s system dynamics model of critical care pathways for end-stage renal disease (ESRD). Fernandez took Koliba’s system analysis and strategic management course and Zia’s decision-making modeling course. This model, constructed using the proprietary software, AnyLogic, was initially constructed as a project in Zia’s course. Castle and Fernandez’s projects illustrate how master’s-level students with an eye toward becoming policy informatics analysts can build skills and capacities to develop useful informatics projects that can guide policy and public management. They were guided to this point by taking advanced courses designed explicitly with policy informatics outcomes in mind. 26 C. Koliba and A. Zia Policy Informatics Analyst InformaticsSavvy Public •Advanced research methods •Data visualization and design techniques •Basic to advanced modeling software skills •Basic to advanced programming language(s) •Systems thinking •Basic understanding of research methods •Knowledge of how to integrate informatics within performance management •Knowledge of how to integrate inofrmatics within financial systems •Effecive written communication •Effective usese of social media / egovernance approaches Fig. 2.3 The nested capacities of informatics-savvy public managers and policy informatics analysts Figure 2.3 illustrates how the competencies of the two different ideal types of policy informatics practitioners are nested inside of one another. A more complete list of competencies that are needed for the more advanced forms of policy analysis will need to emerge through robust exchanges between the computer sciences, organizational sciences, and policy sciences. These views will likely hinge on assumptions about the sophistication of the models to be developed. A key question here concerning the types of models to be built is: Can adequate models be built using existing software or is original programming needed or desired? Ideally, advanced policy analysts undertaking policy informatics projects are “programmers with a public service motivation.” 2.3 Applications to Professional Masters Programs Professional graduate degree programs have steadily moved toward emphasizing the importance of the mission of particular graduate programs in determining the optimal curriculum to suit the learning needs of it students. As a result, clear definitions of the learning outcomes and the learning needs of particular student communities are defined. Some programs may seek to serve regional or local needs of the government and nonprofit sector, while others may have a broader reach, preparing students to work within federal or international level governments and nonprofits. In addition to geographic scope, accredited MPA and MPP programs may have specific areas of concentration. Some programs may focus on preparing public managers who are charged with managing resources, making operational, tactical, and 2 Educating Public Managers and Policy Analysts in an Era of Informatics 27 strategic decisions and, overall, administering to the day-to-day needs of a government or nonprofit organization. Programs may also focus on training policy analysts who are responsible for analyzing policies, policy alternatives, problem definition, and the like. Historically, the differences between public management and policy analysis have distinguished the MPA degree from the MPP degree. However, recent studies of NASPAA-accredited programs have found that the lines between MPA and MPP programs are increasingly blurred (Hur and Hackbart 2009). The relationship between public management and policy analysis matters to those interested in policy informatics because these distinctions drive what policy informatics competencies and capacities are covered within a core curriculum, and what competencies and capacities are covered within a suite of electives or concentrations. Competency-based assessments are increasingly being used to evaluate and design curriculum. Drawing on the core tenants of adult learning theory and practice, competency-based assessment involves the derivation of specific skills, knowledge, or attitudes that an adult learner must obtain in order to successfully complete a course of study or degree requirement. Effective competency-based graduate programs call on students to demonstrate a mastery of competencies through a variety of means. Portfolio development, test taking, and project completion are common applications. Best practices in competency-based education assert that curriculum be aligned with specific competencies as much as possible. By way of example, the University of Vermont’s MPA Program has had a “systems thinking” focus since it was first conceived in the middle 1980s. Within the last 10 years, the two chapter coauthors, along with several core faculty who have been associated with the program since its inception, have undertaken an effort to refine its mission based on its original systems-focused orientation. As of 2010, the program mission was refined to read: Our MPA program is a professional interdisciplinary degree that prepares pre and in-service leaders, managers and policy analysts by combining the theoretical and practical foundations of public administration focusing on the complexity of governance systems and the democratic, collaborative traditions that are a hallmark of Vermont communities. The mission was revised to include leaders and managers, as well as policy analysts. A theory-practice link was made explicit. The phrase, “complexity of governance systems” was selected to align with a commonly shared view of contemporary governance as a multisectoral and multijurisdictional context. Concepts such as bounded rationality, social complexity, the importance of systems feedback, and path dependency are stressed throughout the curriculum. The sense of place found within the State of Vermont was also recognized and used to highlight the high levels of engagement found within the program. The capacities laid out in Table 2.1 have been mapped to the program’s core curriculum. The program’s current core is a set of five courses: PA 301: Foundations of Public Administration; PA 302: Organizational Behavior and Change; PA 303: Research Methods; PA 305: Public and Nonprofit Budgeting and Finance and PA 306: Policy Systems. In addition, all students are required to undertake a threecredit internship and a three-credit Capstone experience in which they construct a 28 C. Koliba and A. Zia final learning portfolio. It is within this final portfolio that students are expected to provide evidence of meeting or exceeding the standard. An expanded rubric of all 18 capacities is used by the students to undertake their own self-assessment. These assessments are judged against the Capstone instructor’s evaluation. In 2009, the MPA faculty revised the core curriculum to align with the core competencies. Several course titles and content were revised to align with these competencies and the overall systems’ focus of our mission. The two core courses taught by the two coauthors, PA 301 and PA 306, are highlighted here. 2.4 PA 301: Foundations of Public Administration Designed as a survey of the prevailing public administration literature during the past 200 plus years, Foundations of Public Administration is arranged across a continuum of interconnected themes and topics that are to be addressed in more in-depth in other courses and is described in the syllabus in the following way: This class is designed to provide you with an overview of the field of public administration. You will explore the historical foundations, the major theoretical, organizational, and political breakthroughs, and the dynamic tensions inherent to public and nonprofit sector administration. Special attention will be given to problems arising from political imperatives generated within a democratic society. Each week a series of classic and contemporary texts are read and reviewed by the students. In part, to fill a noticeable void in the literature, the authors co-wrote, along with Jack Meek, a book on governance networks called: Governance Networks in Public Administration and Public Policy (Koliba et al. 2010). This book is required reading. Students are also asked to purchase Shafritz and Hyde’s edited volume, Classics of Public Administration. Current events assignments offered through blog posts are undertaken. Weekly themes include: the science and art of administration; citizens and the administrative state; nonprofit, private, and public sector differences; governance networks; accountability; and performance management. During the 2009 reforms of the core curriculum, discrete units on governance networks and performance management were added to this course. Throughout the entire course, a complex systems lens is employed to describe and analyze governance networks and the particular role that performance management systems play in providing feedback to governance actors. Students are exposed to social network and system dynamics theory, and asked to apply these lenses to several written cases taken from the Electronic Hallway. A unit on performance management systems and their role within fostering organizational learning are provided along with readings and examples of decision support tools and dashboard platforms currently in use by government agencies. Across many units, including units on trends and reforms, ethical and reflective leadership, citizens and the administrative state, and accountability, the increasing use of social media and other forms of information technology are discussed. Trends 2 Educating Public Managers and Policy Analysts in an Era of Informatics 29 shaping the “e-governance” and “e-government” movements serve as a major focus on current trends. In addition, students are exposed to current examples of data visualizations and open data platforms and asked to consider their uses. 2.5 PA 306: Policy Systems Policy Systems is an entry-level graduate policy course designed to give the MPA student an overview of the policy process. In 2009, the course was revised to reflect a more integrated systems focus. The following text provides an overview of the course: In particular, the emphasis is placed upon meso-, and macro-scale policy system frameworks and theories, such as Institutional Analysis and Development Framework, the Multiple Streams Framework; Social Construction and Policy Design; the Network Approach; Punctuated Equilibrium Theory; the Advocacy Coalition Framework; Innovation and Diffusion Models and Large-N Comparative Models. Further, students will apply these micro-, mesoand macro-scale theories to a substantive policy problem that is of interest to a community partner, which could be a government agency or a non-profit organization. These policy problems may span, or even cut across, a broad range of policy domains such as (included but not limited to) economic policy, food policy, environmental policy, defense and foreign policy, space policy, homeland security, disaster and emergency management, social policy, transportation policy, land-use policy and health policy. The core texts for this class are Elinor Ostrom’s, Understanding Institutional Diversity, Paul Sabatier’s edited volume, Theories of the Policy Process, and Deborah Stone’s Policy Paradox: The Art of Political Decision-Making. The course itself is staged following a micro, to meso, to macro level scale of policy systems framework. A service-learning element is incorporated. Students are taught to view the policy process through a systems lens. Zia employs examples of policy systems models using system dynamics (SD), agent-based modeling (ABM), social network analysis (SNA), and hybrid approaches throughout the class. By drawing on Ostrom, Sabatier, and other meso level policy processes as a basis, students are exposed to a number of “complexity-friendly” theoretical policy frameworks (Koliba and Zia 2013). Appreciating the value of these policy frameworks, students are provided with heuristics for understanding the flow of information across a system. In addition, students are shown examples of simulation models of different policy processes, streams, and systems. In addition to PA 301 and PA 306, students are also provided an in-depth exploration of organization theory in PA 302 Organizational Behavior and Change that is taught through an organizational psychology lens that emphasizes the role of organizational culture and learning. “Soft systems” approaches are applied. PA 303 Research Methods for Policy Analysis and Program Evaluation exposes students to a variety of research and program evaluation methodologies with a particular focus on quantitative analysis techniques. Within PA 305 Public and Nonprofit Budgeting and Finance, students are taught about evidence-based decision-making and data management. 30 C. Koliba and A. Zia By completing the core curriculum, students are exposed to some of the foundational competencies needed to use and shape policy informatics projects. However, it is not until students enroll in one of the several electives, that more explicit policy informatics concepts and applications are taught. Two of these elective courses are highlighted here. A third, PA 311 Policy Analysis, also exposes students to policy analyst capacities, but is not highlighted here. 2.6 PA 308: Decision-Making Models A course designated during the original founding of the University of Vermont (UVM)-MPA Program, PA 308: Decision-Making Models offers students with a more advanced look at decision-making theory and modeling. The course is described by Zia in the following manner: In this advanced graduate level seminar, we will explore and analyze a wide range of normative, descriptive and prescriptive decision making models. This course focuses on systems level thinking to impart problem-solving skills in complex decision-making contexts. Decision making problems in the real-world public policy, business and management arenas will be analyzed and modeled with different tools developed in the fields of Decision Analysis, Behavioral Sciences, Policy Sciences and Complex Systems. The emphasis will be placed on imparting cutting edge skills to enable students to design and implement multiple criteria decision analysis models, decision making models under risk and uncertainty and computer simulation models such as Monte Carlo simulation, s…
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