
Actor and Strategy Models
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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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Persons
Leon M. Hermans is an assistant professor at the Faculty of Technology, Policy, and Management at Delft University of Technology. His research focuses on the use of actor analysis methods to support public policy analysis and evaluation.
Scott W. Cunningham is an associate professor at the Faculty of Technology, Policy, and Management at Delft University of Technology. He combines work in actor modelling and game theory with research in data mining, technology forecasting, and innovation.
Content
Preface ix
Acknowledgements xi
Part I Introduction 1
1 The Need for Actor and Strategy Models 3
1.1 Actors and Decision-Making 3
1.2 Applications of Actor and Strategy Models 4
1.3 Scope and Structure of This Book 6
References 10
2 A Framework for Actor and Strategy Modeling 13
2.1 What are Strategic Actors? 13
2.2 Conceptual Framework for Strategic Actor Interactions 17
2.3 Overview of Actor and Strategy Models 23
2.4 Step-Wise Approach for Actor and Strategy Modeling 27
2.5 Challenges in Modeling Strategic Actor Interactions 32
References 36
Part II Models and Applications 39
3 Scanning Your Actor Network as Part of Problem Diagnosis 41
3.1 Diagnosis for Strategic Interaction Problems 41
3.2 Stakeholder Analysis and Actor Network Scanning 42
3.3 Step-Wise Approach for Actor Network Scanning 43
3.4 Case Application: Offshore Wind Energy 58
3.5 Summary and Further Reading 68
References 69
Part II-A Values 71
4 Identifying, Structuring, and Measuring Values: Value-Focused Thinking 73
4.1 Values as Fundamental Drivers of Actor Processes 74
4.2 Value-Focused Thinking for Multi-actor Issues 75
4.3 Step-Wise Approach for Value-Focused Thinking for Multiple Actors 77
4.4 Case Application: Rural Livelihoods in Tanzania 88
4.5 Summary and Further Reading 99
References 100
Part II-B Resource Dependencies 103
5 Making a Move: Analysis of Options and Conflict Graphs 105
5.1 Strategic Use of Resources to Shape Environments 105
5.2 Analysis of Options 106
5.3 Step-Wise Approach for Analysis of Options 113
5.4 Case Application: Volunteered Geographical Information 121
5.5 Case Application: Solar Power 135
5.6 Summary and Further Reading 142
References 144
6 Appraising the Strategic Value of Information: Extensive Games 149
6.1 The Role of Resources and Information in Strategic Games 150
6.2 Game Theory and Social Dilemmas 150
6.3 Step-Wise Approach for Extensive Games 158
6.4 Case Application: Supply Chain Management 166
6.5 Summary and Further Reading 179
References 180
7 Looking for Coalitions: Cooperative Game Theory 183
7.1 Group Capability and Cooperation 183
7.2 Cooperative Game Theory 184
7.3 Step-Wise Approach for Analyzing Cooperative Potential 190
7.4 Case Application: Renewable Energy 202
7.5 Summary and Further Reading 209
Appendix 7.A: R Code to Support Cooperative Analyses 210
References 214
8 Identifying Opportunities for Exchange: Transactional Analysis 217
Jos S. Timmermans
8.1 Multi-actor Decision-Making as an Exchange of Control over Resources 218
8.2 Transactional Analysis 218
8.3 Step-Wise Approach for Transactional Analysis 221
8.4 Case Application: Rural Water Management in the Netherlands 229
8.5 Summary and Further Reading 238
Appendix 8.A: Calculation of Dependencies 239
Appendix 8.B: Calculation of Equilibrium Control 240
References 243
Part II-C Perceptions 245
9 Capturing Problem Perceptions: Comparative Cognitive Mapping 247
9.1 How Perceptions Can Help Explain (In)Activity in Multi-actor Settings 247
9.2 Comparative Cognitive Mapping 249
9.3 Step-Wise Approach for Comparative Cognitive Mapping 253
9.4 Case Application: Pollution Control for Urban Public Spaces 263
9.5 Summary and Further Reading 273
References 274
10 Reconstructing Debate: Argumentative Analysis 277
10.1 Debates Fuelled by Competing Claims 277
10.2 Argumentative Analysis 278
10.3 Step-Wise Approach for Argumentative Analysis 281
10.4 Case Application: Watershed Protection in the Philippines 286
10.5 Summary and Further Reading 302
References 303
Part II-D Networks 305
11 Scrutinizing Relations that Shape Actions: Social Network Analysis 307
Mark de Reuver
11.1 The Importance of Relations Between Actors 308
11.2 Social Network Analysis 308
11.3 Step-Wise Approach for Social Network Analysis 312
11.4 Case Application: Innovation Network for Internet Video Services 318
11.5 Summary and Further Reading 327
References 328
Part III Comparison and Reflection 331
12 Actor Models: Comparison and Reflection 333
12.1 When to Use What Model? 333
12.2 Illustrative Comparison of the Use of Models for a Single Case 339
12.3 Revisiting the Challenges Involved in Analyzing Actors and Strategies 353
12.4 Next Steps in the Future of Actor and Strategy Models 358
References 360
Index 363
1
The 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 1.1 Applications of actor and strategy models
Domain Location/Organization(s) Purpose of...System requirements
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