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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A practical how-to guide for more effective planningthrough multi-actor modelling Careful planning is the cornerstone of a successful initiative, and any plan, policy, or business strategy can only be successful if it has the support of different actors. These actors may beactively pursuing their own agendas, so the plan must not only offer an optimal solution to theproblem, but must also fit the needs and abilities of the actors involved. Actor and Strategy Models: Practical Applications and Step-wise Approaches provides a primer on multi-actormodelling, based on the fundamental premise that actor strategies are explained by investigatingwhat actors can do, think, and want to achieve. Covering a variety of models with detailed background and case examples, this book focuses on practical application. Step-by-step instructions for each approach provide immediately actionable insight, while a general framework for actor and strategy modelling allows the reader to tailor any approach as needed to optimize results in terms of situation-specific planning. Oriented toward real-world strategy, this helpful resource: * Provides models that shed light on the multi-actor dimensions of planning, using a variety of analytical approaches * Includes literature, theoretical underpinnings, and applications for each method covered * Clarifies the similarities, differences, and suitable applications between various actor modelling approaches * Provides a step-wise framework for actor and strategy modelling * Offers guidance for the identification, structuring, and measuring of values and perceptions * Examines the challenges involved in analyzing actors and strategies Even before planning begins, an endeavor's success depends upon a clear understanding of the various actors involved in the planning and implementation stages. From game theory and argumentative analysis, through social network analysis, cognitive mapping, and beyond,Actor and Strategy Models provides valuable insight for more effective planning.
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Actor and Strategy Models
Practical Applications and Step-wise Approaches
Leon M. Hermans
ScottW. Cunningham
With contributions from
Mark de Reuver
Jos S. Timmermans
This edition first published 2018 © 2018 John Wiley & Sons, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Leon M. Hermans, Scott W. Cunningham, Mark de Reuver and Jos S. Timmermans to be identified as the author(s) of this work has been asserted in accordance with law.
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Library of Congress Cataloging-in-Publication Data
Names: Hermans, Leon M., author. | Cunningham, Scott W., author. Title: Actor and strategy models : practical applications and step-wise approaches / by Leon M. Hermans, Scott W. Cunningham. Description: Hoboken, NJ : John Wiley & Sons, 2018. | Includes bibliographical references and index. | Identifiers: LCCN 2017044996 (print) | LCCN 2017050343 (ebook) | ISBN 9781119284734 (pdf) | ISBN 9781119284765 (epub) | ISBN 9781119284703 (cloth) Subjects: LCSH: Business planning–Mathematical models. Classification: LCC HD30.28 (ebook) | LCC HD30.28 .H4816 2018 (print) | DDC 658.4/012011–dc23 LC record available at https://lccn.loc.gov/2017044996
Cover image: © johnwoodcock/Gettyimages Cover design by Wiley
CONTENTS
Preface
Acknowledgements
Part I Introduction
1 The Need for Actor and Strategy Models
1.1 Actors and Decision-Making
1.2 Applications of Actor and Strategy Models
1.3 Scope and Structure of This Book
References
2 A Framework for Actor and Strategy Modeling
2.1 What Are Strategic Actors?
2.2 Conceptual Framework for Strategic Actor Interactions
2.3 Overview of Actor and Strategy Models
2.4 Step-Wise Approach for Actor and Strategy Modeling
2.5 Challenges in Modeling Strategic Actor Interactions
2.6 Summary and Further Reading
Notes
References
Part II Models and Applications
3 Scanning Your Actor Network as Part of Problem Diagnosis
3.1 Diagnosis for Strategic Interaction Problems
3.2 Stakeholder Analysis and Actor Network Scanning
3.3 Step-Wise Approach for Actor Network Scanning
3.4 Case Application: Offshore Wind Energy
3.5 Summary and Further Reading
References
Part II-A Values
4 Identifying, Structuring, and Measuring Values: Value-Focused Thinking
4.1 Values as Fundamental Drivers of Actor Processes
4.2 Value-Focused Thinking for Multi-actor Issues
4.3 Step-Wise Approach for Value-Focused Thinking for Multiple Actors
4.4 Case Application: Rural Livelihoods in Tanzania
4.5 Summary and Further Reading
Notes
References
Part II-B Resource Dependencies
5 Making a Move: Analysis of Options and Conflict Graphs
5.1 Strategic Use of Resources to Shape Environments
5.2 Analysis of Options
5.3 Step-Wise Approach for Analysis of Options
5.4 Case Application: Volunteered Geographical Information
5.5 Case Application: Solar Power
5.6 Summary and Further Reading
References
6 Appraising the Strategic Value of Information: Extensive Games
6.1 The Role of Resources and Information in Strategic Games
6.2 Game Theory and Social Dilemmas
6.3 Step-Wise Approach for Extensive Games
6.4 Case Application: Supply Chain Management
6.5 Summary and Further Reading
References
7 Looking for Coalitions: Cooperative Game Theory
7.1 Group Capability and Cooperation
7.2 Cooperative Game Theory
7.3 Step-Wise Approach for Analyzing Cooperative Potential
7.4 Case Application: Renewable Energy
7.5 Summary and Further Reading
Appendix 7.A: R Code to Support Cooperative Analyses
References
8 Identifying Opportunities for Exchange: Transactional Analysis
8.1 Multi-actor Decision-Making as an Exchange of Control over Resources
8.2 Transactional Analysis
8.3 Step-Wise Approach for Transactional Analysis
8.4 Case Application: Rural Water Management in the Netherlands
8.5 Summary and Further Reading
Appendix 8.A: Calculation of Dependencies
Appendix 8.B: Calculation of Equilibrium Control
Note
References
Part II-C Perceptions
9 Capturing Problem Perceptions: Comparative Cognitive Mapping
9.1 How Perceptions Can Help Explain (In)Activity in Multi-actor Settings
9.2 Comparative Cognitive Mapping
9.3 Step-Wise Approach for Comparative Cognitive Mapping
9.4 Case Application: Pollution Control for Urban Public Spaces
9.5 Summary and Further Reading
References
10 Reconstructing Debate: Argumentative Analysis
10.1 Debates Fuelled by Competing Claims
10.2 Argumentative Analysis
10.3 Step-Wise Approach for Argumentative Analysis
10.4 Case Application: Watershed Protection in the Philippines
10.5 Summary and Further Reading
References
Part II-D Networks
11 Scrutinizing Relations that Shape Actions: Social Network Analysis
11.1 The Importance of Relations Between Actors
11.2 Social Network Analysis
11.3 Step-Wise Approach for Social Network Analysis
11.4 Case Application: Innovation Network for Internet Video Services
11.5 Summary and Further Reading
References
Part III Comparison and Reflection
12 Actor Models: Comparison and Reflection
12.1 When to Use What Model?
12.2 Illustrative Comparison of the Use of Models for a Single Case
12.3 Revisiting the Challenges Involved in Analyzing Actors and Strategies
12.4 Next Steps in the Future of Actor and Strategy Models
References
Index
EULA
List of Tables
Chapter 1
Table 1.1
Chapter 2
Table 2.1
Table 2.2
Table 2.3
Chapter 3
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 3.5
Table 3.6
Table 3.7
Table 3.8
Table 3.9
Table 3.10
Chapter 4
Table 4.1
Table 4.2
Table 4.3
Chapter 5
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6
Table 5.7
Table 5.8
Table 5.9
Table 5.10
Chapter 6
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5
Table 6.6
Chapter 7
Table 7.1
Table 7.2
Table 7.3
Table 7.4
Table 7.5
Table 7.6
Table 7.7
Table 7.8
Table 7.9
Table 7.10
Chapter 8
Table 8.1
Table 8.2
Table 8.3
Table 8.4
Table 8.5
Table 8.6
Table 8.7
Chapter 9
Table 9.1
Table 9.2
Table 9.3
Table 9.4
Table 9.5
Chapter 10
Table 10.1
Table 10.2
Table 10.3
Table 10.4
Chapter 11
Table 11.1
Chapter 12
Table 12.1
Table 12.2
Table 12.3
Table 12.4
Table 12.5
Table 12.6
List of Illustrations
Chapter 2
Figure 2.1
Decision arena and system of interest: actors and agents
Figure 2.2
Conceptual framework for actor and strategy models. Adapted from Hermans (2005) and Hermans and Cunningham (2013)
Figure 2.3
Step-wise approach for actor and strategy modeling. Adapted from Hermans (2005)
Chapter 3
Figure 3.1
Step-wise approach for actor network scanning
Figure 3.2
Power/interest grid and implications for analysis. Based on Bryson (2004) and Johnson et al. (2005)
Figure 3.3
Formal network diagram for offshore wind energy production in the Netherlands
Figure 3.4
Power–interest grid for offshore wind example
Chapter 4
Figure 4.1
Step-wise approach for value-focused thinking
Figure 4.2
Means-ends objective network (example).
Figure 4.3
A fundamental objectives hierarchy (example)
Figure 4.4
Means-ends objectives network for lower zone cattle holders
Figure 4.5
Means-ends objectives network for middle zone farmers
Figure 4.6
Means-ends objective network for government and donor agencies
Figure 4.7
Fundamental objectives hierarchy farmers and cattle holders
Figure 4.8
Fundamental objectives hierarchy water agencies
Figure 4.9
Water productivity of different agricultural activities. (Based on FAO, 2005, p. 27)
Figure 4.10
Average income from agriculture in different zones. (Based on FAO, 2005, pp. 29–31)
Chapter 5
Figure 5.1
Step-wise approach for analysis of options
Figure 5.2
Strategic matrix (abstract example)
Figure 5.3
More elaborate strategic matrices
Figure 5.4
Three forms of a conflict graph
Figure 5.5
Means-ends objective network for local government actors
Figure 5.6
Means-ends objective network for commercial actors
Figure 5.7
Means-ends objective network for industrial actors
Figure 5.8
Partial conflict graph
Figure 5.9
Partial, simplified conflict graph
Figure 5.10
Complete conflict graph
Figure 5.11
Conflict graph in equilibrium
Chapter 6
Figure 6.1
Sequential and simultaneous move games
Figure 6.2
Sequential game with nature
Figure 6.3
Hidden knowledge (left) and hidden action (right)
Figure 6.4
Hidden knowledge as well as hidden action
Figure 6.5
Strategic equivalent games
Figure 6.6
Step-wise approach for extensive form games
Figure 6.7
Progressive steps in developing utility scales
Figure 6.8
Two-mode graph of actors and observed actions
Figure 6.9
Timeline of the supply chain game
Figure 6.10
Initial game tree of the supply chain game (without information sets and payoffs)
Figure 6.11
Game tree of the supply chain game with information sets
Figure 6.12
Complete game tree with equilibria outcomes
Figure 6.13
Resultant Nash equilibria report in Gambit
Figure 6.14
Evaluation of Pareto front
Chapter 7
Figure 7.1
Step-wise approaches for analyzing cooperative games
Figure 7.2
Coalition value line
Figure 7.3
Example ternary plot
Figure 7.4
Solution concepts on a single ternary diagram
Figure 7.5
Coalition value line
Figure 7.6
Three ternary plots
Figure 7.A.1
Core and solution concepts for the Tri-State Water Game
Chapter 8
Figure 8.1
Step-wise approach for transactional analysis
Figure 8.2
Control matrix C
Figure 8.3
Interest matrix X
Figure 8.4
Sociometric star expressing dependency of actor A1 on actors A2–A7
Figure 8.5
Network graph expressing dependency relations between actors A1–A5
Chapter 9
Figure 9.1
The basic structure of a cognitive map (abstract version)
Figure 9.2
Step-wise approach for comparative cognitive mapping
Figure 9.3
Cognitive map municipality (generated with DANA software)
Figure 9.4
Similarity of perceived causality in paths between actors
Chapter 10
Figure 10.1
General argumentative structure in the Toulmin model
Figure 10.2
Step-wise approach for argumentative analysis
Chapter 11
Figure 11.1
Step-wise approach for social network analysis
Figure 11.2
Network for the formative phase
Figure 11.3
Growth phase two-mode network
Chapter 12
Figure 12.1
Resource dependency models for well- and ill-structured situations
Figure 12.2
Power–interest grid for smart mobility case
Figure 12.3
Illustrative objectives hierarchy for environmental benefits car sharing of electric vehicles
Figure 12.4
Game tree electric vehicles car sharing in San Diego
Figure 12.5
Cooperative game representation (percentage of total value in game on axes)
Figure 12.6
Cognitive map car2go (fictitious)
Figure 12.7
Argumentation example in favor of car sharing
Figure 12.8
Network graph for selected activities by Cleantech San Diego organizations
Guide
Cover
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Preface
This book bears the fruits of many years of research and teaching at the Faculty of Technology, Policy and Management of Delft University of Technology. This started quite some time ago with efforts to get a better analytical grip on networks, stakeholders, and actor processes in decision-making. The quest for models and methods that would help policy analysts led researchers to cover everything from soft-OR methods to classic game theory approaches, social network analysis, and Coleman's linear system of action. Early on, also new approaches were developed, sometimes with accompanying supporting software.
In the early days, it was still to be seen if these and similar approaches would actually be able to add value to practicing policy analysts. By now, there is no question that many of these methods, often further developed and tested, are in fact indispensable. This applies not only to settings of public policy analysis, but also to corporate and strategic planning to support strategizing by private companies or non-governmental organizations. A serious analyst can no longer get away with just a quick stakeholder mapping to tackle complex real-world problems. Many more sophisticated models and methods are now available and should be used. With this book, we hope to aid readers in exploring the field and to enable readers to use actor models and approaches for application in their own analyses and research projects.
Much of the earlier research into analytical methods for actor or network analyses is reported under the label of (model-based) actor analysis. To emphasize the use of models as part of these actor analysis approaches, we are now often referring to them under the label of actor and strategy models. The use of models includes a range of approaches, from conceptual to mathematical modeling.
As the research matured, actor and strategy models were also incorporated in the teaching curriculum in Delft. When developing our academic course on actor and strategy models, we struggled to identify suitable reading material. In reviewing the existing literature, we noticed a gap between relatively short practical texts on stakeholder analysis, such as guidance notes and book sections, and specialized textbooks dedicated to a single method or approach, such as game theory or social network analysis. With this book, we hope to fill this gap.
This book offers a primer on analytical models that shed light on the multi-actor dimension in planning and decision-making. Each modeling approach is positioned within the scientific literature, its conceptual underpinnings are introduced, and practical applications are discussed and illustrated with elaborate case examples. The models in this book have proven their use in different types of situations and under varying conditions. A range of modeling approaches is covered and treated in detail, with a chapter dedicated to each of them. We hope this unlocks a world of methods and models to readers who want to develop more analytical flexibility in the ways in which they look at strategic decision situations with multiple actors.
The emphasis in the book is on the practical application of various approaches. Collecting various actor modeling approaches in one volume should help to get an overview of the differences and overlaps between them, and to make flexible and creative use of actors models that suit the needs of different situations. For those who become seriously interested in one particular actor modeling approach, we hope to offer a good starting point for further learning, providing basic concepts and key references for further reading.
Acknowledgements
Over the years, many students and colleagues have contributed to the development of the concepts, ideas, and applications of actor and strategy models included in this book. These include not only students and colleagues in Delft, but also many researchers and co-workers in other institutes and places. In fact, too many to list here. However, a few people have played a key role in the development and use of these models over the years and merit a special mention. Wil Thissen supported, guided, and supervised much of the PhD and MSc thesis research into actor analysis and actor modeling as professor of policy analysis and head of the Delft policy analysis section until 2014. Pieter Bots early on developed an actor analysis approach and software that still adds great value to the toolkit of policy analysts and which forms the basis of the chapter on comparative cognitive mapping included in this book. Furthermore, Pieter still inspires and critically scrutinizes new ideas and thoughts on actor and strategy models. Giampiero Beroggi made important contributions in the early days of research in this field in Delft. His footprint is still visible in many chapters of this book, especially in the chapter dedicated to transactional analysis. Telli van der Lei wrote a PhD dissertation on actor analysis methods and later co-authored a Dutch book aimed at project management professionals, both of which have been of great use in the development of this book. Other colleagues and students in Delft who have made critical contributions to the development of and thinking on actor and strategy models are (in no particular order): Bert Enserink, Jill Slinger, Sertac Oruc, Sharlene Gomes, Stephanie Janssen, Dorien Korbee, Tom de Booij, Roland de Groot, Perry van Overveld, and Richenel Breeveld. Dorien, Sharlene, and Giampiero also reviewed earlier draft versions of some of the chapters in this book. Wim Ravensteijn, João Gorenstein Dedeca, and Alexia Anthanasopoulou did the same. We are very thankful for their time and their useful comments.
Earlier versions of texts used here have been used in teaching, and student comments and feedback on these texts have greatly helped improve their quality and clarity. For this, we would like to thank the students who participated in the Actor and Strategy Models course during the past few years for their enthusiasm, critical remarks, and inspiring applications.
Part IIntroduction
1The Need for Actor and Strategy Models
1.1 Actors and Decision-Making
Actors matter for decision-making. Realizing organizational goals and objectives, successfully implementing a large project, or achieving policy impact is only possible with the support of others. Decision-makers cannot pretend to operate in a command and control environment where their decisions are readily agreed to and taken forward by others. Governments and businesses alike need to navigate and manage their network environment (De Bruijn & Ten Heuvelhof, 2008). A fundamental part of this is knowing who the important actors are, how to activate partners and accommodate critics, when to adapt to your network environment, and when to try to influence it (Van Schendelen, 2005). Moreover, mapping the actors in a network that could offer support in case of different uncertain developments is key for adaptive management, enabling decision-makers to quickly change gears in response to emerging challenges and opportunities.
For a long time, rational planning was considered part of one realm, and understanding networks and social decision processes part of another. In one realm, decision analysts, policy analysts, economists, and engineers would support decision-makers to find smart, efficient, optimal, or robust alternatives that combine multiple objectives, taking into account various types of uncertainties. In another realm, social scientists, organizational scientist, political scientists, and the like would focus on the processes, people, and politics involved, pointing out fundamental drivers and associated dilemmas inherent in multi-actor decision-making. One only needs to think of the writings and influence of Machiavelli about power and politics in decision-making in the early sixteenth century to recognize the importance of this tradition.
These two realms offer useful pillars or poles on a continuum because there are also many fruitful crossovers that combine insights from both realms into new approaches. These approaches have grown in number and maturity in the past few decades. A very visible result is the use of methods and techniques traditionally used more in systems science and engineering to analyze the political and social processes of decision-making. Examples are game theory, social network analysis, and cognitive mapping (Hermans, 2005; Hermans & Thissen, 2009). These approaches represent multi-actor decision-making processes, for instance as games, transactions, debates, or information flows in networks. Essentially, they all use models to capture and explain important features of the actor interactions that drive multi-actor decision-making. Therefore, we refer to these approaches as actor and strategy models.
1.2 Applications of Actor and Strategy Models
The importance of, and interest in, actor and strategy models has grown over the past years. It is now widely recognized that decision-makers cannot be effective if they do not manage their actor environment. These actor environments themselves are undeniably complex. Decisions are made and implemented in decision arenas that lack clearly defined boundaries and participants. Multiple decision arenas are linked, across sectors and across multiple levels of governance. Local decisions and global networks are linked through social media and geopolitics, through globalized production networks and value chains, and for instance local cities are seen as key responders for shared global challenges (Barber, 2013). There are numerous cross-sectoral, interconnected, and hybrid networks of organizations in various forms—public, private, firms, and foundations—and information and capital readily flows across national and regulatory boundaries (Castells, 2010). Moreover, the networks and decision arenas are constantly changing, both within and outside organizations (Freeman, 2010).
As connections among people, organizations, and countries continue to grow and as traditional boundaries among groups, sectors, and segments continue to be redefined, the need for a proper understanding of the actor dimension continues to grow in importance. Actor and strategy models help to gain such understanding. Examples of past applications give an impression of the benefits to be gained from their use.
1.2.1 Flood Protection
The Houston Galveston Bay Area in Texas is highly prone to hurricane-induced disaster. The area has seen a longstanding debate about ways to improve flood protection, and around 2014, this debate was growing increasingly sour. Scientists could not offer a way out because the key experts and research institutes also appeared to be diametrically opposed in their positions. The debate had become so intense and bitter that a productive communication between the two opposing sides was virtually impossible. A process structured around actor models was used to organize a workshop where stakeholders from across the divide jointly explored the complexity of the problems as well as pathways for alternative solutions. The workshop did not deny the irreconcilable differences between actor groups, but still enabled actors to eliminate outcomes that would be bad for all and rather focus on future scenarios with potential wins for at least one of the groups. Two weeks after the workshop, a platform for joint action was formed and the workshop was widely acknowledged as an important contributing factor (Cunningham et al., 2015).
1.2.2 Internet Innovations
In the early days of Internet video services, the late 1990s and early 2000s, Dutch actors played a key role as pioneers. By 2013, the market was dominated by US-based firms such as YouTube, Google, Apple, and Netflix. How could this happen? A social network analysis of the relations among the Dutch key actors over time provided important insights. It showed that the early innovations of the pioneers in Internet video services in the Netherlands were financially supported by the government. However, this financial support stopped when the innovation system was still in its formative phase and had not yet entered its growth phase. With hindsight, this was too early, and was an important reason why early pioneering Dutch actors could not retain their central position in this innovation network. This held important lessons for future Dutch innovation policies (Poel, 2013).
1.2.3 Enterprise Planning
Modern manufacturing companies spend a lot of time and resources to smoothen their workflows and planning processes, integrating various decision support systems and procedures. However, it often turns out that actual integration of operations remains very difficult. Integrated and coupled enterprise planning and control systems cannot prevent continued hick-ups in planning, delays, and cost overruns. Actor models applied to different manufacturing companies showed how different units and departments in these companies, each with their own objectives and responsibilities, were still able to mess up production schedules. Production planners and operations units have to deal with units for product quality control, procurement, sales, and marketing. Their demands and sometimes strict procedures are not very efficient from a short-term operational perspective, but are sometimes critical for the longer-term success of the company. These insights could be used to improve the design of procedures and management information systems (Osorio, 2012).
1.2.4 Pollution Control
Environmental pollution of land, air, and water bodies often stems from various smaller sources that together have a significant impact. In the Netherlands, a group of policy makers from different organizations, levels, and sectors established a platform to reduce such diffuse pollution. As a first step to get going and achieve some early results, they had identified areas where they expected an easy start with some early successes. Among those was the use of chemical herbicides in maintaining public spaces: chemical substances used for weed control in public parks, streets, squares, and pavements. An actor model helped to clarify the different perceptions on this issue held by the key actors involved. This showed that, in practice, a reduction of use of these chemical herbicides might be more difficult than expected. The companies using herbicides and the government agencies that contracted their services were not convinced that good alternatives would be feasible or would be less damaging. This helped government organizations to update their expectations about the chances of easy and early results, but also showed them specific areas where further research and communication would need to focus on (Hermans, 2005).
1.2.5 More Examples of Applications
The use and usefulness of actor and strategy models is illustrated further in Table 1.1. The table shows examples where actor and strategy models have been applied and have made a difference for several cases in the past. Although the table shows many applications in the Netherlands, a bias due to the affiliations of the authors of this book, it also shows examples from various other parts of the world. These indicate that the use of actor and strategy models is not confined to any specific country or region.
Table 1.1Applications of actor and strategy models
Domain
Location/Organization(s)
Purpose of Application
Year
a
Reference
b
Tourism
Municipality of Rhenen, the Netherlands
Identification of actors who could fruitfully collaborate on different issues for local tourism development
2004
Timmermans (2004)
Pollution control
Rijkswaterstaat and province of North Holland, the Netherlands
Ways to convince actors to reduce the use of harmful chemicals in maintenance of urban public spaces
2005
Chapter 9
Water governance
Cebu province, stakeholder platform, and research center in the Philippines
Developing an agenda for joint research and pilot projects to support the development of a regional water management strategy
2005
Chapter 10
Rural livelihoods
Food and Agriculture Organization of the UN and Government of Tanzania
Exploration of room to resolve local conflicts over water for rural livelihoods, between sectors and users
2006
Chapter 4
Water management
Ministry of Agriculture, the Netherlands
Increase knowledge of the actors in the policy arena, identify promising policies and start interaction process
2008
Chapter 8
Transport (rail)
ProRail, the Netherlands
Rail network maintenance decisions based on views and preferences of stakeholders
2009
Brinkman (2009)
European pollution standards
Association of Dutch drinking water companies (Vewin)
Processes behind establishment of official European lists of harmful pollutants—how to be more effective in getting own considerations into this process
2010
Van Overveld et al. (2010)
Sustainable development
Municipalities in Hungary
How formal and informal relationships shape learning for sustainable development in municipalities
2011
Pusztai (2011)
Water quality
Regional water authority Delfland, the Netherlands
Design of collaborative monitoring arrangements for water quality management
2012
Hermans et al. (2012)
Construction
Contracting and construction company BAM, the Netherlands
Communication strategy for the actors involved in city road reconstruction
2012
De Booij and Hermans (2012)
Manufacturing
Manufacturing companies in the Netherlands and Mexico (DSM, MEEIN, Radiall)
Complement integration of enterprise and control systems with information on actor dependencies in manufacturing companies
2012
Osorio (2012)
Drinking water
Vitens Evides International and Lilongwe Water Utility, Malawi
Organizational and institutional incentives that contribute to performance of water companies
2013
Breeveld et al. (2013)
Innovation policy
Internet video service providers, the Netherlands
Investigate effect of policy measures on Internet innovation in the Netherlands
2013
Chapter 11
Flood protection
Houston Galveston Bay Area, USA
Establishing dialogue and joint commitment to action for flood protection in bay area
2014
Cunningham et al. (2015)
Offshore wind energy
North Sea area, for Royal HaskoningDHV
Ways to move toward coordinated offshore energy grid development between countries
2014
Satolli (2015)
Energy distribution
Energy network company Alliander, the Netherlands
Strengthening position of energy grid operators in smart grid innovations
2016
De Reuver et al. (2016)
aYear of publication of this case application in a report, journal article, or (as part of) a book.
bReference is made to the book chapter if an application is discussed in detail in this book, otherwise a reference is provided at the end of this chapter.
1.3 Scope and Structure of This Book
1.3.1 Purpose and Scope
The main purpose of this book is to introduce a range of models that help understand actors and their strategic interactions, and that offer useful tools to practitioners and analysts in the fields of decision-making, policy analysis, management, corporate planning, and related fields. The focus is on models that aid understanding of the behavior of actors who play a role in the larger decision arenas that affect plan implementation or policy success. We prefer to speak of actors for reasons we explain later in this book, but other labels used in practice are stakeholders, agents, players, participants, or decision-makers (in plural form).
Analysts working in the fields of policy analysis, project planning, management, and impact assessment have in common that they use an understanding of existing or past situations with the purpose of exploring possible future situations (Bardach, 2004; Barzelay, 2007). For strategic actor models, this means that we do not just use them to describe the current processes or settings, but that we mainly want to use them to inform decisions about a prospective future situation. We are using models to structure existing knowledge and evidence in a way that helps us to inform decision-making about situations that cannot be observed. As Walker and Van Daalen (2013) describe, this use of models to inform decision-making often involves a trade-off of rigor for relevance. A balance is needed between an accurate description of real-world situations and an informative analysis of prospective actions and their possible consequences. The models covered in this book, and the way in which they are covered, are selected and described with this trade-off in mind.
This book offers an overview and a primer on actor and strategy models. It fills the gap between, on the one hand, the relatively short texts on stakeholder analysis and power mapping such as provided by IIED (2005) or Nash et al. (2009), and, on the other hand, complete textbooks dedicated to specific approaches such as game theory (Osborne & Rubinstein, 1994; Rasmusen, 2006), social network analysis (Wasserman & Faust, 1994; Scott, 2012), value-based approaches (Keeney, 1992; Stewart, 2010), and other relevant actor modeling approaches. We provide an overview of actor models that have proven their use in different types of situations and under varying conditions. In this way, this book describes the world of actor and strategy models that exists beyond stakeholder lists, unlocking a wider toolbox for a better understanding of actors and network environments as it is now available in different corners and traditions of planning, policy analysis, and management.
By dedicating a full chapter to each modeling approach, this book offers a primer on different actor and strategy models, providing basic concepts, step-wise approaches for applications, and key references for further reading. With this, the readers will have a good basis to better structure, understand, and explore complex situations that involve multiple actors. These primers on different models are useful for professionals in the field of strategic planning and policy analysis as the primary audience, but will also offer a useful introduction for scientists, researchers, and graduate students who want to explore the field of actor and strategy modeling. This helps readers who want to develop more analytical flexibility in the ways in which they understand their strategic environments and, more generally, the interactions among actors in processes of policy development and decision-making.
In addition to a primer on different modeling approaches, the book also provides a framework to position and compare these different approaches. Combined with a comparative chapter at the end of the book, this provides insight into the differences and overlaps between models, and helps readers to make flexible and creative use of different models and combinations, in order to meet the needs of different situations. All in all, these materials should enable the use of actor and strategy models in a range of complex problem situations to support understanding, communication, and “what-if” explorations. Also, it provides a basis for further learning for those who become interested to know more about any specific model or approach.
1.3.2 Structure of the Book
This book consists of three main parts. The first part is a general introduction to actor and strategy models, in this chapter and the next. In Chapter 2, we elaborate more on the conceptual and scientific underpinnings of these models and we discuss their use and limitations.
Part two forms the core of the book and discusses applications of different actor and strategy modeling approaches. This part starts in Chapter 3 with an approach for a quick-and-dirty scan of an actor network, as a first problem diagnosis that helps to make an informed choice for a particular approach for further modeling.
Each of the other chapters in the second part of the book covers one specific modeling approach in more detail. Each of these application chapters starts with a short introduction of the potential use of the approach, then continues to position the modeling approach within the scientific literature and to describe its theoretic underpinnings, and then focuses on step-wise approaches for practical applications. The last part of each chapter is always dedicated to an elaborate case example. These chapters are organized according to their focus within the conceptual framework for actor models, as introduced in Chapter 2. Chapters here cover modeling approaches based on value-focused thinking, game theory, cooperative game theory, transactional analysis, cognitive mapping, argumentative analysis, and social network analysis.
Part three of the book consists of a final chapter that contains a comparative reflection where we revisit the usefulness and limitations of the actor and strategy models and offer some further insights on model selection, combination, and future directions.
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2A Framework for Actor and Strategy Modeling
In this chapter, we provide a framework and step-wise approach for the use of actor and strategy models. This framework helps to position the different actor and strategy models in this book against a common conceptual background as well as a common sequence of steps in their application.
We sketch this framework by first clarifying key concepts in actor and strategy models. What do we mean when we talk about actors and strategy? What are the key concepts that are commonly included in actor and strategy models? With the resulting conceptual framework as common background, we introduce different actor and strategy models, based on their conceptual focus. We then move on to the application of these models, introducing a common generic step-wise approach followed by all these models.
There are easy critiques to make of models that attempt to capture strategic interactions between actors. Most critiques point to the difficulties in establishing valid models with a certain predictive power. We address these critiques in the last part of this chapter, where we argue that despite obvious and important limitations, actor models are useful as exemplifying theories for decision-making processes.
2.1 What Are Strategic Actors?
2.1.1 Actors and Systems
There are many situations in which success or failure depends on whether others are working with you, intentionally or unintentionally. In these situations, it is worthwhile to develop a better understanding of who these important others are, what can be expected from them, and if and how they could be influenced to ensure that their actions are congruent with the realization of your objectives. These others are commonly referred to as actors or stakeholders. We use the term actors to refer to individuals, organizations, or groups capable of autonomous and intentional actions that have an impact on a problem or system of interest.
In the above definition of actors, we talk about a system of interest. We use this terminology on purpose. The actor models covered in this book often are used as part of a larger set of activities in policy analysis or strategic planning. This field has come to be dominated by systems thinking during the twentieth century (Thissen & Walker, 2013). The system approaches used in planning, decision-making, and policy analysis share a focus on systems as the central object of policy interventions or management strategies. Strategic actors are actors that seek to influence those systems.
Systems consist of several related entities that together produce certain outcomes of interest, under certain conditions. Systems perform a function when they process inputs into outputs and desirable outcomes. For instance, a transportation system may consist of individual travelers, different means of transportation, and a transport infrastructure, which may be used to produce outcomes such as mobility but also positive or negative environmental externalities. The transportation system here changes inputs, such as a traveler at a location A at a given time, into outputs such as that same traveler at a location B at a later point in time. Other inputs and outputs in this system will relate to money spent, emissions caused, space occupied, and energy use.
Actors come into the picture if they are an important influence on, or in, systems. We can make a distinction between strategic actors on the one hand and system agents on the other hand (Hermans & Cunningham, 2013). Strategic actors are actively trying to shape or influence a system, for instance by altering the physical environment or by putting economic incentives or regulatory sanctions in place that stimulate others to act in a more desired way. They are often policy makers or decision-makers who interact in a dedicated social space for strategic decision-making, a so-called decision arena (Ostrom, 2005).
Whereas strategic actors operate in decision arenas, systems agents operate inside a system. Like strategic actors, system agents are capable of autonomous action. However, individual system agents do not have a significant influence at a system level. Individually, system agents cannot change key system elements and therefore system agents will take these other system elements into account in their actions as given conditions or constraints. The actions of system agents also influence system properties and outcomes, but at the system level this is an emergent influence. It is the aggregate of individual uncoordinated actions, not the result of a collective decision or coordinated action (Scharpf, 1997). Examples of system agents are consumers, travelers, small businesses, or citizens.
The difference between agents and actors is illustrated in Figure 2.1. Strategic actors operate in a decision arena where they seek to influence a system of interest. Agents are located within this system, as part of a socio-technical or socio-ecological system that produces outcomes that are of interest to the strategic actors in the decision arena. Although Figure 2.1 shows one decision arena connected to one system, in many cases, multiple decision arenas and multiple systems may be linked.
Figure 2.1 Decision arena and system of interest: actors and agents
2.1.2 Strategic Actors as Organizations
The actor and strategy models in this book focus on the interactions among strategic actors. System agents may be considered also as part of some actor and strategy models, but they are not central to these models. We speak of a strategic actor when an actor is capable of purposive action that has a significant influence on a system of interest, either directly or indirectly through a decision arena connected to it. System agents differ from strategic actors because, on their own, the actions of an individual agent do not wield a similar influence. If system agents start to coordinate their actions, they may turn into a strategic actor. Concerned citizens or consumers may form a movement or an association that turns them into a strategic actor, capable of purposive action with a significant influence on a system of interest.
In fact, strategic actors generally consist of multiple smaller actors that together form a composite actor or organization. These smaller actors may be individuals or organizational units at lower levels. In principle, an individual person can also be a strategic actor, if he or she is highly influential in a certain arena. However, most often, powerful individuals are considered powerful because of their official role as the formal representative of a composite actor. A minister represents a ministry or department; a chief executive officer is the highest in hierarchy in a business corporation.
Composite actors take joint action through coordinated decisions among their members. Of course, this requires some sort of coordination mechanism and a rule to arrive at collective decisions. Coordination may be achieved in a strict top-down hierarchical manner, by consensus, agreement, or voting (Scharpf, 1997). Following Scharpf (1997), different types of composite actors can be identified, based on how they make decisions, how they take actions, and how resources and purposes are distributed, as shown in Table 2.1.
Table 2.1Coordination mechanisms used by different types of composite actors
Strategic Actors
System Agents
Coalition
Club
Movement
Association
Corporation
Action
Individual
Joint
Joint
Joint
Joint
Organization
Purpose
Individual
Individual
Individual
Collective
Collective
Organization
Resources
Individual
Individual
Collective
Individual
Collective
Organization
Decisions
Individual
Agreement
Voting
Consensus
Voting
Hierarchical
Based on Table 3.1 in Scharpf (1997).
The point of Table 2.1 is not to introduce a rigid categorization, but to show that different types of composite actors exist, with implications for their role and position in interaction processes in decision arenas. For this purpose, let us have another look at the example of consumers who decide to organize themselves into a consumer organization. If the consumer organization has limited control over its members’ resources, it may give voice to their concerns in a strategic setting, but afterward its representatives will still have to persuade all the other members to follow the specific course of action that was agreed in a negotiation with other strategic actors. This is typically the case when resources are not pooled, such as in a movement or a coalition. If the newly established consumer organization is mandated by its members to negotiate on their behalf and reach binding agreements for them, this means that there is a collective purpose and that resources are used collectively—which matches with the association in Scharpf's classification.
2.1.3 A Short Note on Stakeholders
In this book, we talk about actors and agents. Another very common term, especially in management literature, is stakeholder. The term stakeholder may be used to refer to either a strategic actor or a system agent. Stakeholders are system agents mostly when the label is used to stress that individuals or organizations have a stake, which they are likely to act upon, and which policy makers and strategists therefore need to take into account in decision-making. In project management, this use of the stakeholder concept is common (MacArthur, 1997). Stakeholders are also used to refer to parties in a strategic decision-making environment, such as public ministries and departments, large organizations, strategic business partners, and others. In these uses, more common in strategic management (Mitroff, 1983; Freeman, 1984), an organization's stakeholders are similar to what we call strategic actors.
2.2 Conceptual Framework for Strategic Actor Interactions
Constructing an analytical model requires that one make assumptions. Often, such assumptions are related to the types of concepts that are necessary to provide an accurate or useful description of model behavior, and the way in which these concepts are related. Sometimes, there are also some more fundamental, underlying assumptions that are used in specific classes or types of models. These more fundamental assumptions, or first principles, can be referred to as axioms. Actor and strategy models share two key axioms: one on rationality and the other on resource dependence.
2.2.1 Two Key Axioms for Strategic Actor Interactions
2.2.1.1 Rationality1
An actor interacts with its environment through its capacity for action. Actions may change the physical and natural environment, or may influence other actors, social networks, and decision arenas. Actors’ capacities to take actions may also influence other actors even if actions are not (yet) taken. The threat or promise of an action by one actor can influence what another actor decides to do. When we use actor models, we are interested in understanding why actors would chose to take certain actions, and how this helps to explain actor interactions.
A key assumption in actor models is that actions by actors are intentional, purposeful, rational, or strategic. This means that actors are assumed to think about the effects of their actions, and to take these expected consequences into account when deciding on a particular course of action. It also implies that strategic actors analyze the intended goals and actions of other strategic actors.
This notion of intentional or strategic action implies a certain degree of rationality of actors. However, rather than presuming full or complete rationality, actor models are compatible with, or even explicitly based on, a notion of bounded rationality (Simon, 1972). Bounded rationality means that actors’ decisions for certain actions can be intentionally made (with the intention to realize a certain strategic objective or interest) and yet be ill-informed. Decisions are often based on incomplete information, on choices made under time pressure, or influenced by emotional attachments and detachments. The assumption of actor rationality hence does not imply perfect information, complete knowledge, or consistency in preferences. It does mean, however, that strategic actors are assumed to act based on some sort of conscious decision, as opposed to acting mostly on impulses or emotional reflexes. Especially when dealing with strategic actors as organizations this assumption seems valid. In an organization, usually some form of deliberation or reasoning precedes the choice for one action or another.
2.2.1.2 Resource Dependence
If we assume rationality, we can expect actors to engage in interactions with others mainly because they expect this helps them in some way. If actors would not partly depend on the action of other actors for the realization of their objectives or interests, they would not need to interact with them. They may expect a very direct benefit from this interaction, which is the case in some bargaining or negotiation situations, where one favor is exchanged for another. They may also expect a more indirect and immaterial benefit. Good social relations may help create future opportunities. Likewise, getting some sort of agreement on what are key problems and solutions may enable future collective action.
The distribution of resources over different actors creates resource dependencies, which act as the fuel for strategic actor interactions. Resource dependence is the extent to which one actor is dependent on the resources of another actor for the realization of its goals. Because actors depend on resources controlled by others for the realization of their objectives, they will need to engage with the others (Pfeffer & Salancik, 1978; Coleman, 1990). Through exchanges with others, strategic actors try to mobilize resources controlled by others to support their cause. Likewise, if actors realize that others control resources that can effectively prevent them from realizing their objectives, they will try to somehow ensure that these other actors do not use their resources in a way that harms their interests.
2.2.1.3 From Axioms Toward a Conceptual Framework
Building on the two axioms of bounded rationality and resource dependence, there are many different ways to understand and explain strategic actor interactions. Different theories and theoretical frameworks exist in various scientific disciplines such as policy science, social science, management science, and economics. Each of these theories focuses on different parts of actor interactions and emphasizes different characteristics. Underlying most theories, however, are some common basic conceptual building blocks. Different labels may be used, and, in line with a difference in emphasis, particular concepts may be further elaborated in greater detail, but basically two levels of analysis need to be distinguished: a network level and an actor level (Hermans & Thissen, 2009). Concepts on these two levels will be discussed in Sections 2.2.2 and 2.2.3.
2.2.2 Network Level Concepts: Actors, Relations, and Rules in Arenas
Networks consist of actors and their relations, governed by rules within a certain decision arena. Actors are a key concept in actor and strategy models and hence have been already introduced in the previous section and will receive further attention in Section 2.2.3. Here we first introduce the network level concepts of relations, rules, and arenas.
2.2.2.1 Relations
When actors interact, they establish a relation, a connection between them. Through their relations with each other, actors form networks. Networks exist when multiple actors are interrelated in a more or less systematic way (Kenis & Schneider, 1991; Rhodes & Marsh, 1992). The structure and characteristics of these networks are shaped by the actors in it, but also influence the interactions among the actors in the network (Giddens, 1984). Relations between actors exist for the exchange of information, funds, decision-making authority, and other things.
2.2.2.2 Rules
Relations between actors are often established as a result of certain patterns of repeated interactions over time. These patterns of repeated behavior make social interactions more predictable and reliable and are often described through the concept of institutions or rules (Ostrom, 2005; Hodgson, 2007). Thus rules are shaped by the repeated behavior of actors in a network, and subsequently rules themselves become an important force in shaping networks, and behavior in a network (Hodgson, 2007). Rules may be formal and officially described, or they may be informal and known only to the actors active in a network. Rules exist that describe actions that certain actors “may,” “must,” or “must not” take under certain conditions. If rules are not adhered to, formal or informal sanctions may follow. The rules in a network may assign certain positions and certain powers to actors in a network, giving them a more or less influential position (Ostrom, 2005).
2.2.2.3 Arenas
Networks, consisting of actors their relations and the rules that apply, provide the key to explaining actor interactions. What is also needed is a concept that helps to locate specific networks of interest, and that helps to demarcate the boundaries of a network and a particular set of rules. For this, we use the term of a decision arena to refer to a network of actors in relation to a certain issue or system of interest at a certain point in time (Ostrom, 2005; Van Schendelen, 2005). Arenas can exist within organizations, within the public domain, in a market, or in a public–private space. Like actors, arenas are dynamic and subject to constant change: Actors, issues, relations, and rules may all change over time.
