Public Administration and Information Technology


Public Administration and Information Technology

Volume 10

Series Editor Christopher G. Reddick San Antonio, Texas, USA

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More information about this series at http://www.springer.com/series/10796

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Marijn Janssen • Maria A. Wimmer Ameneh Deljoo Editors

Policy Practice and Digital Science

Integrating Complex Systems, Social Simulation and Public Administration in Policy Research

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Editors Marijn Janssen Ameneh Deljoo Faculty of Technology, Policy, and Faculty of Technology, Policy, and Management Management Delft University of Technology Delft University of Technology Delft Delft The Netherlands The Netherlands

Maria A. Wimmer Institute for Information Systems Research University of Koblenz-Landau Koblenz Germany

ISBN 978-3-319-12783-5 ISBN 978-3-319-12784-2 (eBook) Public Administration and Information Technology DOI 10.1007/978-3-319-12784-2

Library of Congress Control Number: 2014956771

Springer Cham Heidelberg New York London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.

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Preface

The last economic and financial crisis has heavily threatened European and other economies around the globe. Also, the Eurozone crisis, the energy and climate change crises, challenges of demographic change with high unemployment rates, and the most recent conflicts in the Ukraine and the near East or the Ebola virus disease in Africa threaten the wealth of our societies in different ways. The inability to predict or rapidly deal with dramatic changes and negative trends in our economies and societies can seriously hamper the wealth and prosperity of the European Union and its Member States as well as the global networks. These societal and economic challenges demonstrate an urgent need for more effective and efficient processes of governance and policymaking, therewith specifically addressing crisis management and economic/welfare impact reduction.

Therefore, investing in the exploitation of innovative information and commu- nication technology (ICT) in the support of good governance and policy modeling has become a major effort of the European Union to position itself and its Member States well in the global digital economy. In this realm, the European Union has laid out clear strategic policy objectives for 2020 in the Europe 2020 strategy1: In a changing world, we want the EU to become a smart, sustainable, and inclusive economy. These three mutually reinforcing priorities should help the EU and the Member States deliver high levels of employment, productivity, and social cohesion. Concretely, the Union has set five ambitious objectives—on employment, innovation, education, social inclusion, and climate/energy—to be reached by 2020. Along with this, Europe 2020 has established four priority areas—smart growth, sustainable growth, inclusive growth, and later added: A strong and effective system of eco- nomic governance—designed to help Europe emerge from the crisis stronger and to coordinate policy actions between the EU and national levels.

To specifically support European research in strengthening capacities, in overcom- ing fragmented research in the field of policymaking, and in advancing solutions for

1 Europe 2020 http://ec.europa.eu/europe2020/index_en.htm

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vi Preface

ICT supported governance and policy modeling, the European Commission has co- funded an international support action called eGovPoliNet2. The overall objective of eGovPoliNet was to create an international, cross-disciplinary community of re- searchers working on ICT solutions for governance and policy modeling. In turn, the aim of this community was to advance and sustain research and to share the insights gleaned from experiences in Europe and globally. To achieve this, eGovPo- liNet established a dialogue, brought together experts from distinct disciplines, and collected and analyzed knowledge assets (i.e., theories, concepts, solutions, findings, and lessons on ICT solutions in the field) from different research disciplines. It built on case material accumulated by leading actors coming from distinct disciplinary backgrounds and brought together the innovative knowledge in the field. Tools, meth- ods, and cases were drawn from the academic community, the ICT sector, specialized policy consulting firms as well as from policymakers and governance experts. These results were assembled in a knowledge base and analyzed in order to produce com- parative analyses and descriptions of cases, tools, and scientific approaches to enrich a common knowledge base accessible via www.policy-community.eu.

This book, entitled “Policy Practice and Digital Science—Integrating Complex Systems, Social Simulation, and Public Administration in Policy Research,” is one of the exciting results of the activities of eGovPoliNet—fusing community building activities and activities of knowledge analysis. It documents findings of comparative analyses and brings in experiences of experts from academia and from case descrip- tions from all over the globe. Specifically, it demonstrates how the explosive growth in data, computational power, and social media creates new opportunities for policy- making and research. The book provides a first comprehensive look on how to take advantage of the development in the digital world with new approaches, concepts, instruments, and methods to deal with societal and computational complexity. This requires the knowledge traditionally found in different disciplines including public administration, policy analyses, information systems, complex systems, and com- puter science to work together in a multidisciplinary fashion and to share approaches. This book provides the foundation for strongly multidisciplinary research, in which the various developments and disciplines work together from a comprehensive and holistic policymaking perspective. A wide range of aspects for social and professional networking and multidisciplinary constituency building along the axes of technol- ogy, participative processes, governance, policy modeling, social simulation, and visualization are tackled in the 19 papers.

With this book, the project makes an effective contribution to the overall objec- tives of the Europe 2020 strategy by providing a better understanding of different approaches to ICT enabled governance and policy modeling, and by overcoming the fragmented research of the past. This book provides impressive insights into various theories, concepts, and solutions of ICT supported policy modeling and how stake- holders can be more actively engaged in public policymaking. It draws conclusions

2 eGovPoliNet is cofunded under FP 7, Call identifier FP7-ICT-2011-7, URL: www.policy- community.eu

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Preface vii

of how joint multidisciplinary research can bring more effective and resilient find- ings for better predicting dramatic changes and negative trends in our economies and societies.

It is my great pleasure to provide the preface to the book resulting from the eGovPoliNet project. This book presents stimulating research by researchers coming from all over Europe and beyond. Congratulations to the project partners and to the authors!—Enjoy reading!

Thanassis Chrissafis Project officer of eGovPoliNet European Commission DG CNECT, Excellence in Science, Digital Science

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Contents

1 Introduction to Policy-Making in the Digital Age . . . . . . . . . . . . . . . . . 1 Marijn Janssen and Maria A. Wimmer

2 Educating Public Managers and Policy Analysts in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Christopher Koliba and Asim Zia

3 The Quality of Social Simulation: An Example from Research Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Petra Ahrweiler and Nigel Gilbert

4 Policy Making and Modelling in a Complex World . . . . . . . . . . . . . . . . 57 Wander Jager and Bruce Edmonds

5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Erik Pruyt

6 Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making: A Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter Davis and Petra Ahrweiler

7 A Comparative Analysis of Tools and Technologies for Policy Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee and David Price

8 Value Sensitive Design of Complex Product Systems . . . . . . . . . . . . . . . 157 Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers, Paulier Herder and Jeroen van den Hoven

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x Contents

9 Stakeholder Engagement in Policy Development: Observations and Lessons from International Experience . . . . . . . . . . . . . . . . . . . . . . 177 Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink and Catherine Gerald Mkude

10 Values in Computational Models Revalued . . . . . . . . . . . . . . . . . . . . . . . 205 Rebecca Moody and Lasse Gerrits

11 The Psychological Drivers of Bureaucracy: Protecting the Societal Goals of an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Tjeerd C. Andringa

12 Active and Passive Crowdsourcing in Government . . . . . . . . . . . . . . . . 261 Euripidis Loukis and Yannis Charalabidis

13 Management of Complex Systems: Toward Agent-Based Gaming for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Wander Jager and Gerben van der Vegt

14 The Role of Microsimulation in the Development of Public Policy . . . 305 Roy Lay-Yee and Gerry Cotterell

15 Visual Decision Support for Policy Making: Advancing Policy Analysis with Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli, Stefano Bragaglia, Federico Chesani, Michela Milano and Jörn Kohlhammer

16 Analysis of Five Policy Cases in the Field of Energy Policy . . . . . . . . . 355 Dominik Bär, Maria A.Wimmer, Jozef Glova, Anastasia Papazafeiropoulou and Laurence Brooks

17 Challenges to Policy-Making in Developing Countries and the Roles of Emerging Tools, Methods and Instruments: Experiences from Saint Petersburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Dmitrii Trutnev, Lyudmila Vidyasova and Andrei Chugunov

18 Sustainable Urban Development, Governance and Policy: A Comparative Overview of EU Policies and Projects . . . . . . . . . . . . . 393 Diego Navarra and Simona Milio

19 eParticipation, Simulation Exercise and Leadership Training in Nigeria: Bridging the Digital Divide . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Tanko Ahmed

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Contributors

Tanko Ahmed National Institute for Policy and Strategic Studies (NIPSS), Jos, Nigeria

Petra Ahrweiler EA European Academy of Technology and Innovation Assess- ment GmbH, Bad Neuenahr-Ahrweiler, Germany

Tjeerd C. Andringa University College Groningen, Institute of Artificial In- telligence and Cognitive Engineering (ALICE), University of Groningen, AB, Groningen, the Netherlands

Tina Balke University of Surrey, Surrey, UK

Dominik Bär University of Koblenz-Landau, Koblenz, Germany

Cees van Beers Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Stefano Bragaglia University of Bologna, Bologna, Italy

Laurence Brooks Brunel University, Uxbridge, UK

Yannis Charalabidis University of the Aegean, Samos, Greece

Federico Chesani University of Bologna, Bologna, Italy

Andrei Chugunov ITMO University, St. Petersburg, Russia

Gerry Cotterell Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Auckland, New Zealand

Jens Dambruch Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Peter Davis Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Auckland, New Zealand

Sharon Dawes Center for Technology in Government, University at Albany, Albany, New York, USA

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xii Contributors

Zamira Dzhusupova Department of PublicAdministration and Development Man- agement, United Nations Department of Economic and Social Affairs (UNDESA), NewYork, USA

Bruce Edmonds Manchester Metropolitan University, Manchester, UK

Theo Fens Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Marco Gavanelli University of Ferrara, Ferrara, Italy

Lasse Gerrits Department of Public Administration, Erasmus University Rotterdam, Rotterdam, The Netherlands

Nigel Gilbert University of Surrey, Guildford, UK

Jozef Glova Technical University Kosice, Kosice, Slovakia

Natalie Helbig Center for Technology in Government, University at Albany, Albany, New York, USA

Paulier Herder Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Jeroen van den Hoven Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Wander Jager Groningen Center of Social Complexity Studies, University of Groningen, Groningen, The Netherlands

Marijn Janssen Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Geerten van de Kaa Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands

Eleni Kamateri Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece

Bram Klievink Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands

Jörn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD, Darmstadt, Germany

Christopher Koliba University of Vermont, Burlington, VT, USA

Michel Krämer Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Roy Lay-Yee Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Auckland, New Zealand

Deirdre Lee INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland

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Contributors xiii

Andreas Ligtvoet Faculty of Technology, Policy, and Management, Delft Univer- sity of Technology, Delft, The Netherlands

Euripidis Loukis University of the Aegean, Samos, Greece

Dragana Majstorovic University of Koblenz-Landau, Koblenz, Germany

Michela Milano University of Bologna, Bologna, Italy

Simona Milio London School of Economics, Houghton Street, London, UK

Catherine Gerald Mkude Institute for IS Research, University of Koblenz-Landau, Koblenz, Germany

Rebecca Moody Department of Public Administration, Erasmus University Rotterdam, Rotterdam, The Netherlands

Diego Navarra Studio Navarra, London, UK

Adegboyega Ojo INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland

Eleni Panopoulou Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece

Anastasia Papazafeiropoulou Brunel University, Uxbridge, UK

David Price Thoughtgraph Ltd, Somerset, UK

Erik Pruyt Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands; Netherlands Institute for Advanced Study, Wassenaar, The Netherlands

Tobias Ruppert Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Efthimios Tambouris Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece; University of Macedonia, Thessaloniki, Greece

Konstantinos Tarabanis Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece; University of Macedonia, Thessa- loniki, Greece

Dmitrii Trutnev ITMO University, St. Petersburg, Russia

Gerben van derVegt Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands

Lyudmila Vidyasova ITMO University, St. Petersburg, Russia

Maria A. Wimmer University of Koblenz-Landau, Koblenz, Germany

Asim Zia University of Vermont, Burlington, VT, USA

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Chapter 1 Introduction to Policy-Making in the Digital Age

Marijn Janssen and Maria A. Wimmer

We are running the 21st century using 20th century systems on top of 19th century political structures. . . . John Pollock, contributing editor MIT technology review

Abstract The explosive growth in data, computational power, and social media creates new opportunities for innovating governance and policy-making. These in- formation and communications technology (ICT) developments affect all parts of the policy-making cycle and result in drastic changes in the way policies are devel- oped. To take advantage of these developments in the digital world, new approaches, concepts, instruments, and methods are needed, which are able to deal with so- cietal complexity and uncertainty. This field of research is sometimes depicted as e-government policy, e-policy, policy informatics, or data science. Advancing our knowledge demands that different scientific communities collaborate to create practice-driven knowledge. For policy-making in the digital age disciplines such as complex systems, social simulation, and public administration need to be combined.

1.1 Introduction

Policy-making and its subsequent implementation is necessary to deal with societal problems. Policy interventions can be costly, have long-term implications, affect groups of citizens or even the whole country and cannot be easily undone or are even irreversible. New information and communications technology (ICT) and models can help to improve the quality of policy-makers. In particular, the explosive growth in data, computational power, and social media creates new opportunities for in- novating the processes and solutions of ICT-based policy-making and research. To

M. Janssen (�) Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands e-mail: m.f.w.h.a.janssen@tudelft.nl

M. A. Wimmer University of Koblenz-Landau, Koblenz, Germany

© Springer International Publishing Switzerland 2015 1 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_1

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2 M. Janssen and M. A. Wimmer

take advantage of these developments in the digital world, new approaches, con- cepts, instruments, and methods are needed, which are able to deal with societal and computational complexity. This requires the use of knowledge which is traditionally found in different disciplines, including (but not limited to) public administration, policy analyses, information systems, complex systems, and computer science. All these knowledge areas are needed for policy-making in the digital age. The aim of this book is to provide a foundation for this new interdisciplinary field in which various traditional disciplines are blended.

Both policy-makers and those in charge of policy implementations acknowledge that ICT is becoming more and more important and is changing the policy-making process, resulting in a next generation policy-making based on ICT support. The field of policy-making is changing driven by developments such as open data, computa- tional methods for processing data, opinion mining, simulation, and visualization of rich data sets, all combined with public engagement, social media, and participatory tools. In this respect Web 2.0 and even Web 3.0 point to the specific applications of social networks and semantically enriched and linked data which are important for policy-making. In policy-making vast amount of data are used for making predictions and forecasts. This should result in improving the outcomes of policy-making.

Policy-making is confronted with an increasing complexity and uncertainty of the outcomes which results in a need for developing policy models that are able to deal with this. To improve the validity of the models policy-makers are harvesting data to generate evidence. Furthermore, they are improving their models to capture complex phenomena and dealing with uncertainty and limited and incomplete information. Despite all these efforts, there remains often uncertainty concerning the outcomes of policy interventions. Given the uncertainty, often multiple scenarios are developed to show alternative outcomes and impact. A condition for this is the visualization of policy alternatives and its impact. Visualization can ensure involvement of nonexpert and to communicate alternatives. Furthermore, games can be used to let people gain insight in what can happen, given a certain scenario. Games allow persons to interact and to experience what happens in the future based on their interventions.

Policy-makers are often faced with conflicting solutions to complex problems, thus making it necessary for them to test out their assumptions, interventions, and resolutions. For this reason policy-making organizations introduce platforms facili- tating policy-making and citizens engagements and enabling the processing of large volumes of data. There are various participative platforms developed by government agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010; Welch 2012). Platforms can be viewed as a kind of regulated environment that enable developers, users, and others to interact with each other, share data, services, and applications, enable gov- ernments to more easily monitor what is happening and facilitate the development of innovative solutions (Janssen and Estevez 2013). Platforms should provide not only support for complex policy deliberations with citizens but should also bring to- gether policy-modelers, developers, policy-makers, and other stakeholders involved in policy-making. In this way platforms provide an information-rich, interactive

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1 Introduction to Policy-Making in the Digital Age 3

environment that brings together relevant stakeholders and in which complex phe- nomena can be modeled, simulated, visualized, discussed, and even the playing of games can be facilitated.

1.2 Complexity and Uncertainty in Policy-Making

Policy-making is driven by the need to solve societal problems and should result in interventions to solve these societal problems. Examples of societal problems are unemployment, pollution, water quality, safety, criminality, well-being, health, and immigration. Policy-making is an ongoing process in which issues are recognized as a problem, alternative courses of actions are formulated, policies are affected, implemented, executed, and evaluated (Stewart et al. 2007). Figure 1.1 shows the typical stages of policy formulation, implementation, execution, enforcement, and evaluation. This process should not be viewed as linear as many interactions are necessary as well as interactions with all kind of stakeholders. In policy-making processes a vast amount of stakeholders are always involved, which makes policy- making complex.

Once a societal need is identified, a policy has to be formulated. Politicians, members of parliament, executive branches, courts, and interest groups may be involved in these formulations. Often contradictory proposals are made, and the impact of a proposal is difficult to determine as data is missing, models cannot

citizens

Policy formulation

Policy implementation

Policy execution

Policy enforcement and

evaluation

politicians

Policy- makers

Administrative organizations

businesses

Inspection and enforcement agencies

experts

Fig. 1.1 Overview of policy cycle and stakeholders

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4 M. Janssen and M. A. Wimmer

capture the complexity, and the results of policy models are difficult to interpret and even might be interpreted in an opposing way. This is further complicated as some proposals might be good but cannot be implemented or are too costly to implement. There is a large uncertainty concerning the outcomes.

Policy implementation is done by organizations other than those that formulated the policy. They often have to interpret the policy and have to make implemen- tation decisions. Sometimes IT can block quick implementation as systems have to be changed. Although policy-making is the domain of the government, private organizations can be involved to some extent, in particular in the execution of policies.

Once all things are ready and decisions are made, policies need to be executed. During the execution small changes are typically made to fine tune the policy formu- lation, implementation decisions might be more difficult to realize, policies might bring other benefits than intended, execution costs might be higher and so on. Typ- ically, execution is continually changing. Evaluation is part of the policy-making process as it is necessary to ensure that the policy-execution solved the initial so- cietal problem. Policies might become obsolete, might not work, have unintended affects (like creating bureaucracy) or might lose its support among elected officials, or other alternatives might pop up that are better.

Policy-making is a complex process in which many stakeholders play a role. In the various phases of policy-making different actors are dominant and play a role. Figure 1.1 shows only some actors that might be involved, and many of them are not included in this figure. The involvement of so many actors results in fragmentation and often actors are even not aware of the decisions made by other actors. This makes it difficult to manage a policy-making process as each actor has other goals and might be self-interested.

Public values (PVs) are a way to try to manage complexity and give some guidance. Most policies are made to adhere to certain values. Public value management (PVM) represents the paradigm of achieving PVs as being the primary objective (Stoker 2006). PVM refers to the continuous assessment of the actions performed by public officials to ensure that these actions result in the creation of PV (Moore 1995). Public servants are not only responsible for following the right procedure, but they also have to ensure that PVs are realized. For example, civil servants should ensure that garbage is collected. The procedure that one a week garbage is collected is secondary. If it is necessary to collect garbage more (or less) frequently to ensure a healthy environment then this should be done. The role of managers is not only to ensure that procedures are followed but they should be custodians of public assets and maximize a PV.

There exist a wide variety of PVs (Jørgensen and Bozeman 2007). PVs can be long-lasting or might be driven by contemporary politics. For example, equal access is a typical long-lasting value, whereas providing support for students at universities is contemporary, as politicians might give more, less, or no support to students. PVs differ over times, but also the emphasis on values is different in the policy-making cycle as shown in Fig. 1.2. In this figure some of the values presented by Jørgensen and Bozeman (2007) are mapped onto the four policy-making stages. Dependent on the problem at hand other values might play a role that is not included in this figure.

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1 Introduction to Policy-Making in the Digital Age 5

Policy formulation

Policy implementation

Policy execution

Policy enforcement

and evaluation

efficiency

efficiency

accountability

transparancy

responsiveness

public interest

will of the people

listening

citizen involvement

evidence-based

protection of individual rights

accountability

transparancy

evidence-based

equal access

balancing of interests

robust

honesty fair

timelessness

reliable

flexible

fair

Fig. 1.2 Public values in the policy cycle

Policy is often formulated by politicians in consultation with experts. In the PVM paradigm, public administrations aim at creating PVs for society and citizens. This suggests a shift from talking about what citizens expect in creating a PV. In this view public officials should focus on collaborating and creating a dialogue with citizens in order to determine what constitutes a PV.

1.3 Developments

There is an infusion of technology that changes policy processes at both the individual and group level. There are a number of developments that influence the traditional way of policy-making, including social media as a means to interact with the public (Bertot et al. 2012), blogs (Coleman and Moss 2008), open data (Janssen et al. 2012; Zuiderwijk and Janssen 2013), freedom of information (Burt 2011), the wisdom of the crowds (Surowiecki 2004), open collaboration and transparency in policy simulation (Wimmer et al. 2012a, b), agent-based simulation and hybrid modeling techniques (Koliba and Zia 2012) which open new ways of innovative policy-making. Whereas traditional policy-making is executed by experts, now the public is involved to fulfill requirements of good governance according to open government principles.

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6 M. Janssen and M. A. Wimmer

Also, the skills and capabilities of crowds can be explored and can lead to better and more transparent democratic policy decisions. All these developments can be used for enhancing citizen’s engagement and to involve citizens better in the policy-making process. We want to emphasize three important developments.

1.3.1 The Availability of Big and Open Linked Data (BOLD)

Policy-making heavily depends on data about existing policies and situations to make decisions. Both public and private organizations are opening their data for use by others. Although information could be requested for in the past, governments have changed their strategy toward actively publishing open data in formats that are readily and easily accessible (for example, European_Commission 2003; Obama 2009). Multiple perspectives are needed to make use of and stimulate new practices based on open data (Zuiderwijk et al. 2014). New applications and innovations can be based solely on open data, but often open data are enriched with data from other sources. As data can be generated and provided in huge amounts, specific needs for processing, curation, linking, visualization, and maintenance appear. The latter is often denoted with big data in which the value is generated by combining different datasets (Janssen et al. 2014). Current advances in processing power and memory allows for the processing of a huge amount of data. BOLD allows for analyzing policies and the use of these data in models to better predict the effect of new policies.

1.3.2 Rise of Hybrid Simulation Approaches

In policy implementation and execution, many actors are involved and there are a huge number of factors influencing the outcomes; this complicates the prediction of the policy outcomes. Simulation models are capable of capturing the interdepen- dencies between the many factors and can include stochastic elements to deal with the variations and uncertainties. Simulation is often used in policy-making as an instrument to gain insight in the impact of possible policies which often result in new ideas for policies. Simulation allows decision-makers to understand the essence of a policy, to identify opportunities for change, and to evaluate the effect of pro- posed changes in key performance indicators (Banks 1998; Law and Kelton 1991). Simulation heavily depends on data and as such can benefit from big and open data.

Simulation models should capture the essential aspects of reality. Simulation models do not rely heavily on mathematical abstraction and are therefore suitable for modeling complex systems (Pidd 1992). Already the development of a model can raise discussions about what to include and what factors are of influence, in this way contributing to a better understanding of the situation at hand. Furthermore, experimentation using models allows one to investigate different settings and the influence of different scenarios in time on the policy outcomes.

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1 Introduction to Policy-Making in the Digital Age 7

The effects of policies are hard to predict and dealing with uncertainty is a key aspect in policy modeling. Statistical representation of real-world uncertainties is an integral part of simulation models (Law and Kelton 1991). The dynamics asso- ciated with many factors affecting policy-making, the complexity associated with the interdependencies between individual parts, and the stochastic elements asso- ciated with the randomness and unpredictable behavior of transactions complicates the simulations. Computer simulations for examining, explaining, and predicting so- cial processes and relationships as well as measuring the possible impact of policies has become an important part of policy-making. Traditional models are not able to address all aspects of complex policy interactions, which indicates the need for the development of hybrid simulation models consisting of a combinatory set of models built on different modeling theories (Koliba and Zia 2012). In policy-making it can be that multiple models are developed, but it is also possible to combine various types of simulation in a single model. For this purpose agent-based modeling and simulation approaches can be used as these allow for combining different type of models in a single simulation.

1.3.3 Ubiquitous User Engagement

Efforts to design public policies are confronted with considerable complexity, in which (1) a large number of potentially relevant factors needs to be considered, (2) a vast amount of data needs to be processed, (3) a large degree of uncertainty may exist, and (4) rapidly changing circumstances need to be dealt with. Utilizing computational methods and various types of simulation and modeling methods is often key to solving these kinds of problems (Koliba and Zia 2012). The open data and social media movements are making large quantities of new data available. At the same time enhancements in computational power have expanded the repertoire of instruments and tools available for studying dynamic systems and their interdependencies. In addition, sophisticated techniques for data gathering, visualization, and analysis have expanded our ability to understand, display, and disseminate complex, temporal, and spatial information to diverse audiences. These problems can only be addressed from a complexity science perspective and with a multitude of views and contributions from different disciplines. Insights and methods of complexity science should be applied to assist policy-makers as they tackle societal problems in policy areas such as environmental protection, economics, energy, security, or public safety and health. This demands user involvement which is supported by visualization techniques and which can be actively involved by employing (serious) games. These methods can show what hypothetically will happen when certain policies are implemented.

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8 M. Janssen and M. A. Wimmer

1.4 Combining Disciplines in E-government Policy-Making

This new field has been shaped using various names, including e-policy-making, digital policy science, computational intelligence, digital sciences, data sciences, and policy informatics (Dawes and Janssen 2013). The essence of this field it that it is

1. Practice-driven 2. Employs modeling techniques 3. Needs the knowledge coming from various disciplines 4. It focused on governance and policy-making

This field is practice-driven by taking as a starting point the public policy problem and defining what information is relevant for addressing the problem under study. This requires understanding of public administration and policy-making processes. Next, it is a key to determine how to obtain, store, retrieve, process, model, and interpret the results. This is the field of e-participation, policy-modeling, social simulation, and complex systems. Finally, it should be agreed upon how to present and disseminate the results so that other researchers, decision-makers, and practitioners can use it. This requires in-depth knowledge of practice, of structures of public administration and constitutions, political cultures, processes and culture and policy-making.

Based on the ideas, the FP7 project EgovPoliNet project has created an inter- national community in ICT solutions for governance and policy-modeling. The “policy-making 2.0” LinkedIn community has a large number of members from dif- ferent disciplines and backgrounds representing practice and academia. This book is the product of this project in which a large number of persons from various dis- ciplines and representing a variety of communities were involved. The book shows experiences and advances in various areas of policy-making. Furthermore, it contains comparative analyses and descriptions of cases, tools, and scientific approaches from the knowledge base created in this project. Using this book, practices and knowl- edge in this field is shared among researchers. Furthermore, this book provides the foundations in this area. The covered expertise include a wide range of aspects for so- cial and professional networking and multidisciplinary constituency building along the axes of technology, participative processes, governance, policy-modeling, social simulation, and visualization. In this way eGovPoliNet has advanced the way re- search, development, and practice is performed worldwide in using ICT solutions for governance and policy-modeling.

Although in Europe the term “e-government policy” or “e-policy,” for short, is often used to refer to these types of phenomena, whereas in the USA often the term “policy informatics” is used. This is similar to that in the USA the term digital government is often used, whereas in Europe the term e-government is preferred. Policy informatics is defined as “the study of how information is leveraged and efforts are coordinated towards solving complex public policy problems” (Krishnamurthy et al. 2013, p. 367). These authors view policy informatics as an emerging research space to navigate through the challenges of complex layers of uncertainty within

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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 mathemat- ical models using the data, present and communicate the data insights/findings to specialists and scientists in their team, and if required to a nonexpert audience. These are activities which are at the heart of policy-making.

1.5 Overview of Chapters

In total 54 different authors were involved in the creation of this book. Some chapters have a single author, but most of the chapters have multiple authors. The authors rep- resent a wide range of disciplines as shown in Fig. 1.2. The focus has been on targeting five communities that make up the core field for ICT-enabled policy-making. These communities include e-government/e-participation, information systems, complex systems, public administration, and policy research and social simulation. The com- bination of these disciplines and communities are necessary to tackle policy problems in new ways. A sixth category was added for authors not belonging to any of these communities, such as philosophy and economics. Figure 1.3 shows that the authors are evenly distributed among the communities, although this is less with the chapter. Most of the authors can be classified as belonging to the e-government/e-participation community, which is by nature interdisciplinary.

Foundation The first part deals with the foundations of the book. In their Chap. 2 Chris Koliba and Asim Zia start with a best practice to be incorporated in public administration educational programs to embrace the new developments sketched in

EGOV

IS

Complex Systems

Public Administration and Policy Research

Social Simulation

other (philosophy, energy, economics, )

Fig. 1.3 Overview of the disciplinary background of the authors

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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 as- sumptions and a model is as good as the developer makes it. The user community is proposed to assess the quality of a policy-modeling exercise. Communicative skills, patience, willingness to compromise on both sides, and motivation to bridge the formal world of modelers and the narrative world of policy-makers are suggested as key competences. The authors argue that user involvement is necessary in all stages of model development.

Wander Jager and Bruce Edmonds argue that due to the complexity that many social systems are unpredictable by nature in their Chap. 4. They discuss how some insights and tools from complexity science can be used in policy-making. In particular they discuss the strengths and weaknesses of agent-based modeling as a way to gain insight in the complexity and uncertainty of policy-making.

In the Chap. 5, Erik Pruyt sketches the future in which different systems modeling schools and modeling methods are integrated. He shows that elements from policy analysis, data science, machine learning, and computer science need to be combined to deal with the uncertainty in policy-making. He demonstrates the integration of various modeling and simulation approaches and related disciplines using three cases.

Modeling approaches are compared in the Chap. 6 authored by Dragana Majs- torovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis,and Petra Ahrweiler. Like in the previous chapter they argue that none of the theories on its own is able to address all aspects of complex policy interactions, and the need for hybrid simulation models is advocated.

The next chapter is complimentary to the previous chapter and includes a com- parison of ICT tools and technologies. The Chap. 7 is authored by Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee, and David Price. This chapter can be used as a basis for tool selecting and includes visualization, argumentation, e-participation, opinion mining, simula- tion, persuasive, social network analysis, big data analytics, semantics, linked data tools, and serious games.

Social Aspects, Stakeholders and Values Although much emphasis is put on mod- eling efforts, the social aspects are key to effective policy-making. The role of values is discussed in the Chap. 8 authored by Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers, Paulien Herder, and Jeroen van den Hoven. Using the case of the design of smart meters in energy networks they argue that policy-makers would do well by not only addressing functional requirements but also by taking individual stakeholder and PVs into consideration.

In policy-making a wide range of stakeholders are involved in various stages of the policy-making process. Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink, and Catherine Gerald Mkude analyze five case studies of stakeholder

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1 Introduction to Policy-Making in the Digital Age 11

engagement in policy-making in their Chap. 9. Various engagement tools are dis- cussed and factors identified which support the effective use of particular tools and technologies.

The Chap. 10 investigates the role of values and trust in computational models in the policy process. This chapter is authored by Rebecca Moody and Lasse Gerrits. The authors found that a large diversity exists in values within the cases. By the authors important explanatory factors were found including (1) the role of the designer of the model, (2) the number of different actors (3) the level of trust already present, and (4) and the limited control of decision-makers over the models.

Bureaucratic organizations are often considered to be inefficient and not customer friendly. Tjeerd Andringa presents and discusses a multidisciplinary framework con- taining the drivers and causes of bureaucracy in the Chap. 11. He concludes that the reduction of the number of rules and regulations is important, but that motivating workers to understand their professional roles and to learn to oversee the impact of their activities is even more important.

Crowdsourcing has become an important policy instrument to gain access to expertise (“wisdom”) outside own boundaries. In the Chap. 12, Euripids Loukis and Yannis Charalabidis discuss Web 2.0 social media for crowdsourcing. Passive crowdsourcing exploits the content generated by users, whereas active crowdsourcing stimulates content postings and idea generation by users. Synergy can be created by combining both approaches. The results of passive crowdsourcing can be used for guiding active crowdsourcing to avoid asking users for similar types of input.

Policy, Collaboration and Games Agent-based gaming (ABG) is used as a tool to explore the possibilities to manage complex systems in the Chap. 13 by Wander Jager and Gerben van der Vegt. ABG allows for modeling a virtual and autonomous population in a computer game setting to exploit various management and leadership styles. In this way ABG contribute to the development of the required knowledge on how to manage social complex behaving systems.

Micro simulation focuses on modeling individual units and the micro-level pro- cesses that affect their development. The concepts of micro simulation are explained by Roy Lay-Yee and Gerry Cotterell in the Chap. 14. Micro simulation for pol- icy development is useful to combine multiple sources of information in a single contextualized model to answer “what if” questions on complex social phenomena.

Visualization is essential to communicate the model and the results to a variety of stakeholders. These aspects are discussed in the Chap. 15 by Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli, Stefano Bragaglia, Federico Chesani, Michela Milano, and Jörn Kohlhammer. They argue that despite the significance to use evidence in policy-making, this is seldom realized. Three case studies that have been conducted in two European research projects for policy- modeling are presented. In all the cases access for nonexperts to the computational models by information visualization technologies was realized.

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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 fore- casting activities for the processes of socioeconomic development are an important element in policy-making. The authors provide a brief overview of the history, the current state of the implementation of information processing techniques, and prac- tices for the purpose of public administration in the Russian Federation. Finally, they provide a range of recommendations to proceed.

Urban policy for sustainability is another important area which is directly linked to the first chapter in this section. In the Chap. 18, Diego Navarra and Simona Milio demonstrate a system dynamics model to show how urban policy and governance in the future can support ICT projects in order to reduce energy usage, rehabilitate the housing stock, and promote sustainability in the urban environment. This chapter contains examples of sustainable urban development policies as well as case studies.

In the Chap. 19, Tanko Ahmed discusses the digital divide which is blocking online participation in policy-making processes. Structuration, institutional and actor-network theories are used to analyze a case study of political zoning. The author recommends stronger institutionalization of ICT support and legislation for enhancing participation in policy-making and bridging the digital divide.

1.6 Conclusions

This book is the first comprehensive book in which the various development and disci- plines are covered from the policy-making perspective driven by ICT developments. A wide range of aspects for social and professional networking and multidisciplinary constituency building along the axes of technology, participative processes, gover- nance, policy-modeling, social simulation, and visualization are investigated. Policy- making is a complex process in which many stakeholders are involved. PVs can be used to guide policy-making efforts and to ensure that the many stakeholders have an understanding of the societal value that needs to be created. There is an infusion of technology resulting in changing policy processes and stakeholder involvement. Technologies like social media provides a means to interact with the public, blogs can be used to express opinions, big and open data provide input for evidence-based policy-making, the integration of various types of modeling and simulation tech- niques (hybrid models) can provide much more insight and reliable outcomes, gam- ing in which all kind of stakeholders are involved open new ways of innovative policy- making. In addition trends like the freedom of information, the wisdom of the crowds, and open collaboration changes the landscape further. The policy-making landscape is clearly changing and this demands a strong need for interdisciplinary research.

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1 Introduction to Policy-Making in the Digital Age 13

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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

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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, com- putational tools, and stakeholder engagement. Paper presented at the 14th annual international conference on digital government research, Quebec City, Canada

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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 cre- ate 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 dis- cuss 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 infor- matics 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 oppor- tunities 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: ckoliba@uvm.edu

A. Zia University of Vermont, 205 Morrill Hall, 05405 Burlington, VT, USA e-mail: azia@uvm.edu

© Springer International Publishing Switzerland 2015 15 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

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16 C. Koliba and A. Zia

administrators and policy analysts. In this chapter, we discuss the role of policy infor- matics in the development of present and future public managers and policy analysts. Drawing from our experiences in teaching an informatics-friendly graduate curricu- lum, 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. Com- petencies 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 infor- matics 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 instru- mental 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.

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 17

2.2.1 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 af- forded 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 “sys- tems” 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 under- stand 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 informatics- savvy 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

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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, par- ticularly 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 man- agers 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 eval- uation tool (Willoughby 1918). This approach was formally codified in the 1960s with the planning–programming–budgeting (PPB) system with its focus on plan- ning, 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 pol- icy analyst will likely need to integrate financial resource flows and costs into their projects.

Collaborative and Cooperative Capacity Building The development and use of pol- icy 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 com- plete 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.

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2 Educating Public Managers and Policy Analysts in an Era of Informatics 19

Social Media, Information Technology, and e-Governance Awareness A final com- petency concerns public managers’ capacity to deepen their understanding of how social media, Web-based tools, and related information technologies are being em- ployed 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 four- point 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 accred- itation 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 infor- matics 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 in- formatics 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 in- stances, 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 re- search methods and utilization of information technology. It is assumed that effective policy informatics analysts will have a strong foundation in quantitative methodolo- gies and applications. To obtain these skills, policy analysts will need to move beyond basic surveys of research methods into more advanced research methods curriculum.

w.jager@rug.nl

 

 

20 C. Koliba and A. Zia

Ta bl

e 2.

1 Pu

bl ic

m an

ag er

po lic

y in

fo rm

at ic

s ca

pa ci

tie s

C ap

ac ity

D oe

s no

tm ee

ts ta

nd ar

d A

pp ro

ac he

s st

an da

rd M

ee ts

st an

da rd

E xc

ee ds

st an

da rd

C ap

ac it

y to

ap pl

y kn

ow le

dg e

of sy

st em

dy na

m ic

s an

d ne

tw or

k st

ru ct

ur es

in pu

bl ic

ad m

in is

tr at

io n

pr ac

ti ce

s

D oe

s no

tu nd

er st

an d

th e

ba si

c op

er at

io ns

of sy

st em

s an

d ne

tw or

ks ;c

an no

te xp

la in

w hy

un de

rs ta

nd in

g ca

se s

an d

co nt

ex ts

in te

rm s

of sy

st em

s an

d ne

tw or

ks is

im po

rt an

t

C an

pr ov

id e

a ba

si c

ov er

vi ew

of w

ha ts

ys te

m dy

na m

ic s

an d

ne tw

or k

st ru

ct ur

es ar

e an

d ill

us tr

at e

ho w

th ey

ar e

ev id

en t

in pa

rt ic

ul ar

ca se

s an

d co

nt ex

ts

Is ab

le to

un de

rt ak

e an

an al

ys is

of a

co m

pl ex

pu bl

ic ad

m in

is tr

at io

n is

su e,

pr ob

le m

, or

co nt

ex tu

si ng

ba si

c sy

st em

dy na

m ic

s an

d ne

tw or

k fr

am ew

or ks

C an

ap pl

y sy

st em

dy na

m ic

s an

d ne

tw or

k fr

am ew

or ks

to ex

is tin

g ca

se s

an d

co nt

ex ts

to de

ri ve

w or

ki ng

so lu

tio ns

or fe

as ib

le al

te rn

at iv

es to

pr es

si ng

ad m

in is

tr at

iv e

an d

po lic

y pr

ob le

m s

C ap

ac it

y to

ap pl

y po

li cy

st re

am s,

cy cl

es ,

sy st

em s

fo ci

up on

pa st

, pr

es en

t, an

d fu

tu re

po li

cy is

su es

,a nd

to un

de rs

ta nd

ho w

pr ob

le m

id en

ti fic

at io

n im

pa ct

s pu

bl ic

ad m

in is

tr at

io n

Po ss

es se

s lim

ite d

ca pa

ci ty

to ut

ili ze

po lic

y st

re am

s an

d po

lic y

st ag

e he

ur is

tic s

m od

el to

de sc

ri be

ob se

rv ed

ph en

om en

a. C

an is

ol at

e si

m pl

e pr

ob le

m s

fr om

so lu

tio ns

,b ut

ha s

di ffi

cu ltl

y se

pa ra

tin g

ill -s

tr uc

tu re

d pr

ob le

m s

fr om

so lu

tio ns

Po ss

es se

s so

m e

ca pa

ci ty

to ut

ili ze

po lic

y st

re am

s an

d to

de sc

ri be

po lic

y st

ag e

he ur

is tic

s m

od el

ob se

rv ed

ph en

om en

a. Po

ss es

se s

so m

e ca

pa ci

ty to

de fin

e ho

w pr

ob le

m s

ar e

fr am

ed by

di ff

er en

tp ol

ic y

ac to

rs

E m

pl oy

s a

po lic

y st

re am

s or

po lic

y st

ag e

he ur

is tic

s m

od el

ap pr

oa ch

to th

e st

ud y

of ob

se rv

ed ph

en om

en a.

C an

de m

on st

ra te

ho w

pr ob

le m

de fin

iti on

is de

fin ed

w ith

in sp

ec ifi

c po

lic y

co nt

ex ts

an d

de co

ns tr

uc tt

he re

la tio

ns hi

p be

tw ee

n pr

ob le

m de

fin iti

on s

an d

so lu

tio ns

E m

pl oy

s a

po lic

y st

re am

s or

po lic

y st

ag e

he ur

is tic

s m

od el

ap pr

oa ch

to th

e di

ag no

si s

of a

pr ob

le m

ra is

ed in

re al

-l if

e po

lic y

di le

m m

as .C

an ar

tic ul

at e

ho w

co nfl

ic ts

ov er

pr ob

le m

de fin

iti on

co nt

ri bu

te to

w ic

ke d

po lic

y pr

ob le

m s

C ap

ac it

y to

em pl

oy qu

an ti

ta ti

ve an

d qu

al it

at iv

e re

se ar

ch m

et ho

ds fo

r pr

og ra

m ev

al ua

ti on

an d

ac ti

on re

se ar

ch

Po ss

es se

s a

lim ite

d ca

pa ci

ty to

em pl

oy su

rv ey

,i nt

er vi

ew ,o

r ot

he r

so ci

al re

se ar

ch m

et ho

ds to

a fo

cu s

ar ea

.C an

ex pl

ai n

w hy

it is

im po

rt an

tt o

un de

rt ak

e pr

og ra

m or

pr oj

ec t

ev al

ua tio

n, bu

tp os

se ss

es lim

ite d

ca pa

ci ty

to ac

tu al

ly ca

rr yi

ng it

ou t

D em

on st

ra te

s a

ca pa

ci ty

to em

pl oy

su rv

ey ,i

nt er

vi ew

,o r

ot he

r so

ci al

re se

ar ch

m et

ho ds

to a

fo cu

s ar

ea an

d an

un de

rs ta

nd in

g of

ho w

su ch

da ta

an d

an al

ys is

ar e

us ef

ul in

ad m

in is

tr at

iv e

pr ac

tic e.

C an

pr ov

id e

a ra

tio na

le fo

r un

de rt

ak in

g pr

og ra

m /p

ro je

ct

C an

pr ov

id e

a pi

ec e

of or

ig in

al an

al ys

is of

an ob

se rv

ed ph

en om

en on

em pl

oy in

g on

e qu

al ita

tiv e

or qu

an tit

at iv

e m

et ho

do lo

gy ef

fe ct

iv el

y. Po

ss es

se s

ca pa

ci ty

to co

m m

is si

on a

pi ec

e of

or ig

in al

re se

ar ch

.C an

pr ov

id e

a de

ta ile

d ac

co un

tf or

ho w

a

D em

on st

ra te

s th

e ca

pa ci

ty to

un de

rt ak

e an

in de

pe nd

en t

re se

ar ch

ag en

da th

ro ug

h em

pl oy

in g

on e

or m

or e

so ci

al re

se ar

ch m

et ho

ds ar

ou nd

a to

pi c

of st

ud y

of im

po rt

an ce

to pu

bl ic

ad m

in is

tr at

io n.

C an

de m

on st

ra te

th e

su cc

es sf

ul ex

ec ut

io n

of a

pr og

ra m

or

w.jager@rug.nl

 

 

2 Educating Public Managers and Policy Analysts in an Era of Informatics 21

Ta bl

e 2.

1 (c

on tin

ue d)

C ap

ac ity

D oe

s no

tm ee

ts ta

nd ar

d A

pp ro

ac he

s st

an da

rd M

ee ts

st an

da rd

E xc

ee ds

st an

da rd

ev al

ua tio

n an

d ex

pl ai

n w

ha tt

he po

ss ib

le go

al s

an d

ou tc

om es

of su

ch an

ev al

ua tio

n m

ig ht

be

pr og

ra m

or pr

oj ec

te va

lu at

io n

pr oj

ec ts

ho ul

d be

st ru

ct ur

ed w

ith in

th e

co nt

ex to

f a

sp ec

ifi c

pr og

ra m

or pr

oj ec

t

pr oj

ec te

va lu

at io

n or

th e

su cc

es sf

ul ut

ili za

tio n

of a

pr og

ra m

or pr

oj ec

te va

lu at

io n

to im

pr ov

e ad

m in

is tr

at iv

e pr

ac tic

e

C ap

ac it

y to

ap pl

y so

un d

pe rf

or m

an ce

m ea

su re

m en

ta nd

m an

ag em

en tp

ra ct

ic es

C an

pr ov

id e

an ex

pl an

at io

n of

w hy

pe rf

or m

an ce

go al

s an

d m

ea su

re s

ar e

im po

rt an

ti n

pu bl

ic ad

m in

is tr

at io

n, bu

t ca

nn ot

ap pl

y th

is re

as on

in g

to sp

ec ifi

c co

nt ex

ts

C an

id en

tif y

th e

pe rf

or m

an ce

m an

ag em

en tc

on si

de ra

tio ns

fo r

a pa

rt ic

ul ar

si tu

at io

n or

co nt

ex t,

bu th

as lim

ite d

ca pa

ci ty

to ev

al ua

te th

e ef

fe ct

iv en

es s

of pe

rf or

m an

ce m

an ag

em en

t sy

st em

s

C an

id en

tif y

an d

an al

yz e

pe rf

or m

an ce

m an

ag em

en t

sy st

em s,

ne ed

s, an

d em

er gi

ng op

po rt

un iti

es w

ith in

a sp

ec ifi

c or

ga ni

za tio

n or

ne tw

or k

C an

pr ov

id e

ne w

in si

gh ts

in to

th e

pe rf

or m

an ce

m an

ag em

en t

ch al

le ng

es fa

ci ng

an or

ga ni

za tio

n or

ne tw

or k,

an d

su gg

es ta

lte rn

at iv

e de

si gn

an d

m ea

su re

m en

ts ce

na ri

os

C ap

ac it

y to

ap pl

y so

un d

fin an

ci al

pl an

ni ng

an d

fis ca

l re

sp on

si bi

li ty

C an

id en

tif y

w hy

bu dg

et in

g an

d so

un d

fis ca

lm an

ag em

en t

pr ac

tic es

ar e

im po

rt an

t, bu

t ca

nn ot

an al

yz e

ho w

an d/

or if

su ch

pr ac

tic es

ar e

be in

g us

ed w

ith in

sp ec

ifi c

co nt

ex ts

C an

id en

tif y

fis ca

lp la

nn in

g an

d bu

dg et

in g

pr ac

tic es

fo r

a pa

rt ic

ul ar

si tu

at io

n or

co nt

ex t,

bu th

as lim

ite d

ca pa

ci ty

to ev

al ua

te th

e ef

fe ct

iv en

es s

of a

fin an

ci al

m an

ag em

en ts

ys te

m

C an

id en

tif y

an d

an al

yz e

fin an

ci al

m an

ag em

en t

sy st

em s,

ne ed

s, an

d em

er gi

ng op

po rt

un iti

es w

ith in

a sp

ec ifi

c or

ga ni

za tio

n or

ne tw

or k

C an

pr ov

id e

ne w

in si

gh ts

in to

th e

fin an

ci al

m an

ag em

en t

ch al

le ng

es fa

ci ng

an or

ga ni

za tio

n or

ne tw

or k,

an d

su gg

es ta

lte rn

at iv

e de

si gn

an d

bu dg

et in

g sc

en ar

io s

C ap

ac it

y to

ac hi

ev e

co op

er at

io n

th ro

ug h

pa rt

ic ip

at or

y pr

ac ti

ce s

C an

ex pl

ai n

w hy

it is

im po

rt an

tf or

pu bl

ic ad

m in

is tr

at or

s to

be op

en an

d re

sp on

si ve

pr ac

tit io

ne rs

in a

va gu

e or

ab st

ra ct

w ay

,b ut

ca nn

ot pr

ov id

e sp

ec ifi

c ex

pl an

at io

ns or

ju st

ifi ca

tio ns

ap pl

ie d

to pa

rt ic

ul ar

co nt

ex ts

C an

id en

tif y

in st

an ce

s in

sp ec

ifi c

ca se

s or

co nt

ex ts

w he

re a

pu bl

ic ad

m in

is tr

at or

de m

on st

ra te

d or

fa ile

d to

de m

on st

ra te

in cl

us iv

e pr

ac tic

es

C an

de m

on st

ra te

ho w

in cl

us iv

e pr

ac tic

es an

d co

nfl ic

t m

an ag

em en

tl ea

ds to

co op

er at

io n

fo r

fo rm

in g

co al

iti on

s an

d co

lla bo

ra tiv

e pr

ac tic

es

C an

or ch

es tr

at e

an y

of th

e fo

llo w

in g:

co al

iti on

bu ild

in g

ac ro

ss un

its ,o

rg an

iz at

io ns

,o r

in st

itu tio

ns ,e

ff ec

tiv e

te am

w or

k, an

d/ or

co nfl

ic t

m an

ag em

en t

w.jager@rug.nl

 

 

22 C. Koliba and A. Zia

Ta bl

e 2.

1 (c

on tin

ue d)

C ap

ac ity

D oe

s no

tm ee

ts ta

nd ar

d A

pp ro

ac he

s st

an da

rd M

ee ts

st an

da rd

E xc

ee ds

st an

da rd

C ap

ac it

y to

un de

rt ak

e hi

gh qu

al it

y or

al ,

w ri

tt en

co m

m un

ic at

io n

D em

on st

ra te

s so

m e

ab ili

ty to

ex pr

es s

id ea

s ve

rb al

ly an

d in

w ri

tin g.

L ac

ks co

ns is

te nt

ca pa

ci ty

to pr

es en

ta nd

w ri

te

Po ss

es se

s th

e ca

pa ci

ty to

w ri

te do

cu m

en ts

th at

ar e

fr ee

of gr

am m

at ic

al er

ro rs

an d

ar e

or ga

ni ze

d in

a cl

ea r

an d

ef fic

ie nt

m an

ne r.

Po ss

es se

s th

e ca

pa ci

ty to

pr es

en ti

de as

in a

pr of

es si

on al

m an

ne r.

Su ff

er s

fr om

a la

ck of

co ns

is te

nc y

in th

e pr

es en

ta tio

n of

m at

er ia

la nd

ex pr

es si

on or

or ig

in al

id ea

s an

d co

nc ep

ts

Is ca

pa bl

e of

co ns

is te

nt ly

ex pr

es si

ng id

ea s

ve rb

al ly

an d

in w

ri tin

g in

a pr

of es

si on

al m

an ne

r th

at co

m m

un ic

at es

m es

sa ge

s to

in te

nd ed

au di

en ce

s

C an

de m

on st

ra te

so m

e in

st an

ce s

in w

hi ch

ve rb

al an

d w

ri tte

n co

m m

un ic

at io

n ha

s pe

rs ua

de d

ot he

rs to

ta ke

ac tio

n

C ap

ac it

y to

un de

rt ak

e hi

gh qu

al it

y el

ec tr

on ic

al ly

m ed

ia te

d co

m m

un ic

at io

n an

d ut

il iz

e in

fo rm

at io

n sy

st em

s an

d m

ed ia

to ad

va nc

e ob

je ct

iv es

C an

ex pl

ai n

w hy

in fo

rm at

io n

te ch

no lo

gy is

im po

rt an

tt o

co nt

em po

ra ry

w or

kp la

ce s

an d

pu bl

ic ad

m in

is tr

at io

n en

vi ro

nm en

ts .P

os se

ss es

di re

ct ex

pe ri

en ce

w ith

in fo

rm at

io n

te ch

no lo

gy ,b

ut lit

tle un

de rs

ta nd

in g

fo r

ho w

IT in

fo rm

s pr

of es

si on

al pr

ac tic

e

C an

id en

tif y

in st

an ce

s in

sp ec

ifi c

ca se

s or

co nt

ex tw

he re

a pu

bl ic

ad m

in is

tr at

or su

cc es

sf ul

ly or

un su

cc es

sf ul

ly de

m on

st ra

te d

a ca

pa ci

ty to

us e

IT to

fo st

er in

no va

tio n,

im pr

ov e

se rv

ic es

,o r

de ep

en ac

co un

ta bi

lit y.

A na

ly si

s at

th is

le ve

li s

re le

ga te

d to

de sc

ri pt

io ns

an d

th in

an al

ys is

C an

id en

tif y

ho w

IT im

pa ct

s w

or kp

la ce

s an

d pu

bl ic

po lic

y. C

an di

ag no

se pr

ob le

m s

as so

ci at

ed w

ith IT

to ol

s, pr

oc ed

ur es

,a nd

us es

D em

on st

ra te

s a

ca pa

ci ty

to vi

ew IT

in te

rm s

of sy

st em

s de

si gn

.I s

ca pa

bl e

of w

or ki

ng w

ith IT

pr of

es si

on al

s in

id en

tif yi

ng ar

ea s

of ne

ed fo

r IT

up gr

ad es

,I T

pr oc

ed ur

es ,

an d

IT us

es in

re al

se tti

ng

IT in

fo rm

at io

n te

ch no

lo gy

w.jager@rug.nl

 

 

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 triangu- late 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 meth- ods 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 infor- matics 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 ana- lysts 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 program- ming 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 pol- icy informatics projects, programs, or platforms is a continuing subject of debate within the policy informatics community. Exactly how much and to what extent spe- cific 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.

w.jager@rug.nl

 

 

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.

w.jager@rug.nl

 

 

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 his- toric 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 sce- narios 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 model- ing 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.

w.jager@rug.nl

 

 

26 C. Koliba and A. Zia

Policy Informatics Analyst Informatics-Savvy 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 / e-governance 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 analy- sis will need to emerge through robust exchanges between the computer sciences, organizational sciences, and policy sciences. These views will likely hinge on as- sumptions 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, ad- vanced policy analysts undertaking policy informatics projects are “programmers with a public service motivation.”

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