[SOLVED] Infotech Import in A Global Economy Discussion – Engineering

Question write a short research paper for a peer-reviewed research paper that pertains to the STAKEHOLDER ENGAGEMENT IN POLICY DEVELOPMENT: OBSERVATIONS AND LESSONS FROM INTERNATIONAL EXPERIENCE or VALUES IN COMPUTATIONAL MODELS REVALUED INFORMATION TECHNOLOGY IN A GLOBAL ECONOMY. This will be a detailed summary of the research paper and what you gained from the research. NOTE: Google Scholar is a wonderful location to find these types of articles: https://scholar.google.com/ Requirement: 1.Text book – attached (Chapter 9 and Chapter 10) 2. Refer to the Attachment – Topic-chapter 9 and chapter 10.docx, for the topic in selecting a peer reviewied article. 3. APA 6th edition 4. No plagiarism 5. APA Format 6. 2 pages and less than 5 pages. Make sure you properly cover the peer reviewd article and how it relates to the STAKEHOLDER ENGAGEMENT IN POLICY DEVELOPMENT: OBSERVATIONS AND LESSONS FROM INTERNATIONAL EXPERIENCE or VALUES IN COMPUTATIONAL MODELS REVALUED INFORMATION TECHNOLOGY IN A GLOBAL ECONOMYcontent. 7. References – 4 peer reviewed scholarly article.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
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The publisher, the authors and the editors are safe to assume that the advice and information in this book
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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: m.f.w.h.a.janssen@tudelft.nl
M. A. Wimmer
University of Koblenz-Landau, Koblenz, Germany
© Springer International Publishing Switzerland 2015
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_1
1
2
M. Janssen and M. A. Wimmer
take advantage of these developments in the digital world, new approaches, concepts, instruments, and methods are needed, which are able to deal with societal and
computational complexity. This requires the use of knowledge which is traditionally
found in different disciplines, including (but not limited to) public administration,
policy analyses, information systems, complex systems, and computer science. All
these knowledge areas are needed for policy-making in the digital age. The aim of
this book is to provide a foundation for this new interdisciplinary field in which
various traditional disciplines are blended.
Both policy-makers and those in charge of policy implementations acknowledge
that ICT is becoming more and more important and is changing the policy-making
process, resulting in a next generation policy-making based on ICT support. The field
of policy-making is changing driven by developments such as open data, computational methods for processing data, opinion mining, simulation, and visualization of
rich data sets, all combined with public engagement, social media, and participatory
tools. In this respect Web 2.0 and even Web 3.0 point to the specific applications of
social networks and semantically enriched and linked data which are important for
policy-making. In policy-making vast amount of data are used for making predictions
and forecasts. This should result in improving the outcomes of policy-making.
Policy-making is confronted with an increasing complexity and uncertainty of the
outcomes which results in a need for developing policy models that are able to deal
with this. To improve the validity of the models policy-makers are harvesting data to
generate evidence. Furthermore, they are improving their models to capture complex
phenomena and dealing with uncertainty and limited and incomplete information.
Despite all these efforts, there remains often uncertainty concerning the outcomes of
policy interventions. Given the uncertainty, often multiple scenarios are developed
to show alternative outcomes and impact. A condition for this is the visualization of
policy alternatives and its impact. Visualization can ensure involvement of nonexpert
and to communicate alternatives. Furthermore, games can be used to let people gain
insight in what can happen, given a certain scenario. Games allow persons to interact
and to experience what happens in the future based on their interventions.
Policy-makers are often faced with conflicting solutions to complex problems,
thus making it necessary for them to test out their assumptions, interventions, and
resolutions. For this reason policy-making organizations introduce platforms facilitating policy-making and citizens engagements and enabling the processing of large
volumes of data. There are various participative platforms developed by government
agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010; Welch 2012). Platforms
can be viewed as a kind of regulated environment that enable developers, users, and
others to interact with each other, share data, services, and applications, enable governments to more easily monitor what is happening and facilitate the development
of innovative solutions (Janssen and Estevez 2013). Platforms should provide not
only support for complex policy deliberations with citizens but should also bring together policy-modelers, developers, policy-makers, and other stakeholders involved
in policy-making. In this way platforms provide an information-rich, interactive
1
Introduction to Policy-Making in the Digital Age
3
environment that brings together relevant stakeholders and in which complex phenomena can be modeled, simulated, visualized, discussed, and even the playing of
games can be facilitated.
1.2
Complexity and Uncertainty in Policy-Making
Policy-making is driven by the need to solve societal problems and should result in
interventions to solve these societal problems. Examples of societal problems are
unemployment, pollution, water quality, safety, criminality, well-being, health, and
immigration. Policy-making is an ongoing process in which issues are recognized
as a problem, alternative courses of actions are formulated, policies are affected,
implemented, executed, and evaluated (Stewart et al. 2007). Figure 1.1 shows the
typical stages of policy formulation, implementation, execution, enforcement, and
evaluation. This process should not be viewed as linear as many interactions are
necessary as well as interactions with all kind of stakeholders. In policy-making
processes a vast amount of stakeholders are always involved, which makes policymaking complex.
Once a societal need is identified, a policy has to be formulated. Politicians,
members of parliament, executive branches, courts, and interest groups may be
involved in these formulations. Often contradictory proposals are made, and the
impact of a proposal is difficult to determine as data is missing, models cannot
politicians
Policy formulation
Policymakers
experts
Policy
implementation
Policy
enforcement and
evaluation
Inspection and
enforcement agencies
Fig. 1.1 Overview of policy cycle and stakeholders
citizens
Policy
execution
businesses
Administrative
organizations
4
M. Janssen and M. A. Wimmer
capture the complexity, and the results of policy models are difficult to interpret and
even might be interpreted in an opposing way. This is further complicated as some
proposals might be good but cannot be implemented or are too costly to implement.
There is a large uncertainty concerning the outcomes.
Policy implementation is done by organizations other than those that formulated
the policy. They often have to interpret the policy and have to make implementation decisions. Sometimes IT can block quick implementation as systems have
to be changed. Although policy-making is the domain of the government, private
organizations can be involved to some extent, in particular in the execution of policies.
Once all things are ready and decisions are made, policies need to be executed.
During the execution small changes are typically made to fine tune the policy formulation, implementation decisions might be more difficult to realize, policies might
bring other benefits than intended, execution costs might be higher and so on. Typically, execution is continually changing. Evaluation is part of the policy-making
process as it is necessary to ensure that the policy-execution solved the initial societal problem. Policies might become obsolete, might not work, have unintended
affects (like creating bureaucracy) or might lose its support among elected officials,
or other alternatives might pop up that are better.
Policy-making is a complex process in which many stakeholders play a role. In
the various phases of policy-making different actors are dominant and play a role.
Figure 1.1 shows only some actors that might be involved, and many of them are not
included in this figure. The involvement of so many actors results in fragmentation
and often actors are even not aware of the decisions made by other actors. This makes
it difficult to manage a policy-making process as each actor has other goals and might
be self-interested.
Public values (PVs) are a way to try to manage complexity and give some guidance.
Most policies are made to adhere to certain values. Public value management (PVM)
represents the paradigm of achieving PVs as being the primary objective (Stoker
2006). PVM refers to the continuous assessment of the actions performed by public
officials to ensure that these actions result in the creation of PV (Moore 1995). Public
servants are not only responsible for following the right procedure, but they also have
to ensure that PVs are realized. For example, civil servants should ensure that garbage
is collected. The procedure that one a week garbage is collected is secondary. If it is
necessary to collect garbage more (or less) frequently to ensure a healthy environment
then this should be done. The role of managers is not only to ensure that procedures
are followed but they should be custodians of public assets and maximize a PV.
There exist a wide variety of PVs (Jørgensen and Bozeman 2007). PVs can be
long-lasting or might be driven by contemporary politics. For example, equal access
is a typical long-lasting value, whereas providing support for students at universities
is contemporary, as politicians might give more, less, or no support to students. PVs
differ over times, but also the emphasis on values is different in the policy-making
cycle as shown in Fig. 1.2. In this figure some of the values presented by Jørgensen
and Bozeman (2007) are mapped onto the four policy-making stages. Dependent on
the problem at hand other values might play a role that is not included in this figure.
1
Introduction to Policy-Making in the Digital Age
evidence-based
5
will of the people
public interest
fair
balancing of interests
listening
accountability
citizen involvement
transparancy
Policy
enforcement
and evaluation
Policy
formulation
Policy
execution
Policy
implementation
protection of
individual rights
transparancy
evidence-based
accountability
timelessness
equal access
reliable
efficiency
flexible
fair
honesty
responsiveness
efficiency
robust
Fig. 1.2 Public values in the policy cycle
Policy is often formulated by politicians in consultation with experts. In the PVM
paradigm, public administrations aim at creating PVs for society and citizens. This
suggests a shift from talking about what citizens expect in creating a PV. In this view
public officials should focus on collaborating and creating a dialogue with citizens
in order to determine what constitutes a PV.
1.3
Developments
There is an infusion of technology that changes policy processes at both the individual
and group level. There are a number of developments that influence the traditional
way of policy-making, including social media as a means to interact with the public
(Bertot et al. 2012), blogs (Coleman and Moss 2008), open data (Janssen et al. 2012;
Zuiderwijk and Janssen 2013), freedom of information (Burt 2011), the wisdom
of the crowds (Surowiecki 2004), open collaboration and transparency in policy
simulation (Wimmer et al. 2012a, b), agent-based simulation and hybrid modeling
techniques (Koliba and Zia 2012) which open new ways of innovative policy-making.
Whereas traditional policy-making is executed by experts, now the public is involved
to fulfill requirements of good governance according to open government principles.
6
M. Janssen and M. A. Wimmer
Also, the skills and capabilities of crowds can be explored and can lead to better and
more transparent democratic policy decisions. All these developments can be used for
enhancing citizen’s engagement and to involve citizens better in the policy-making
process. We want to emphasize three important developments.
1.3.1
The Availability of Big and Open Linked Data (BOLD)
Policy-making heavily depends on data about existing policies and situations to
make decisions. Both public and private organizations are opening their data for use
by others. Although information could be requested for in the past, governments
have changed their strategy toward actively publishing open data in formats that are
readily and easily accessible (for example, European_Commission 2003; Obama
2009). Multiple perspectives are needed to make use of and stimulate new practices
based on open data (Zuiderwijk et al. 2014). New applications and innovations can
be based solely on open data, but often open data are enriched with data from other
sources. As data can be generated and provided in huge amounts, specific needs for
processing, curation, linking, visualization, and maintenance appear. The latter is
often denoted with big data in which the value is generated by combining different
datasets (Janssen et al. 2014). Current advances in processing power and memory
allows for the processing of a huge amount of data. BOLD allows for analyzing
policies and the use of these data in models to better predict the effect of new policies.
1.3.2
Rise of Hybrid Simulation Approaches
In policy implementation and execution, many actors are involved and there are a
huge number of factors influencing the outcomes; this complicates the prediction
of the policy outcomes. Simulation models are capable of capturing the interdependencies between the many factors and can include stochastic elements to deal with
the variations and uncertainties. Simulation is often used in policy-making as an
instrument to gain insight in the impact of possible policies which often result in
new ideas for policies. Simulation allows decision-makers to understand the essence
of a policy, to identify opportunities for change, and to evaluate the effect of proposed changes in key performance indicators (Banks 1998; Law and Kelton 1991).
Simulation heavily depends on data and as such can benefit from big and open data.
Simulation models should capture the essential aspects of reality. Simulation
models do not rely heavily on mathematical abstraction and are therefore suitable
for modeling complex systems (Pidd 1992). Already the development of a model
can raise discussions about what to include and what factors are of influence, in this
way contributing to a better understanding of the situation at hand. Furthermore,
experimentation using models allows one to investigate different settings and the
influence of different scenarios in time on the policy outcomes.
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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.
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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
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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 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: 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
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
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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
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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.
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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
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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
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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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