Table of Contents
Summary v
Samenvatting xi
1 Introduction 1
1.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Complex and adaptive socio-technical systems . . . . . . . . . . . . . . . . . . . 4
1.3 Uncertainty in socio-technical systems . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Approach and reading guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Cooperation 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Literature analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Reference and co-reference analysis . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 Standing on shoulders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Disciplinary and cross-disciplinary approaches . . . . . . . . . . . . . . . . . . . 13
2.3.1 Biology and animal behaviour . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Game theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.3 Behavioural science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.4 Strategic and organisational management . . . . . . . . . . . . . . . . . 20
2.3.5 Theory and practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 A layered approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.1 Macro layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.2 Meso layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.3 Micro layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Investigating cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Energy networks case studies 29
3.1 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Thermal grids in the Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 Warmtebedrijf Delft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.2 Partnership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.3 Opportunities and threats . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
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3.3.4 Future developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Aardwarmte Den Haag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.2 Partnership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4.3 Opportunities and threats . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 CCS activity in the Rotterdam area . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.6 Carbon dioxide capture and use – OCAP . . . . . . . . . . . . . . . . . . . . . . 41
3.6.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.6.2 Partnership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.6.3 Opportunities and Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6.4 Future developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.7 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.7.1 Comparison with literature analysis . . . . . . . . . . . . . . . . . . . . 46
3.7.2 Cooperation process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.7.3 Cooperation at different layers . . . . . . . . . . . . . . . . . . . . . . . . 47
3.8 Using the case study method for investigating cooperation . . . . . . . . . . . 49
3.9 Using the case study insights for other methods . . . . . . . . . . . . . . . . . . 50
4 Graph theory and uncertainty 51
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2 Graph theoretical planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.1 Mathematical background . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.2 Extension: network within a bounded region . . . . . . . . . . . . . . 54
4.2.3 Application to a planned syngas network . . . . . . . . . . . . . . . . . 57
4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 Investigating cooperation with graph theoretical planning . . . . . . . . . . . 60
5 Agent-based models of a syngas cluster 63
5.1 The use of models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.1.1 Modelling paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.1.2 Models as an exploratory tool . . . . . . . . . . . . . . . . . . . . . . . . 66
5.2 Syngas cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.2.1 The need for cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.2.2 Model implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.3 A model for analysing the syngas cluster . . . . . . . . . . . . . . . . . . . . . . . 69
5.4 A model for cooperation in syngas agents . . . . . . . . . . . . . . . . . . . . . . 73
5.4.1 Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.4.2 Varying the behaviour of agents . . . . . . . . . . . . . . . . . . . . . . . 75
5.5 Discussion on model outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.6 ABM for modelling cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6 Cooperation in an energy game 83
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2 Serious gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.3 Energy market game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
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6.3.1 Rules and game play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.3.2 Premises of the game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.4 Questioning the students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.5 Outcomes and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.6 Free-form gaming for innovative contracting . . . . . . . . . . . . . . . . . . . . 92
6.6.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.6.2 Road roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.6.3 Rules and game play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.6.4 Premises of the game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.6.5 Outcomes and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.7 Gaming as a tool for investigating cooperation . . . . . . . . . . . . . . . . . . . 95
7 Synthesis 97
7.1 What we know about cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.1.1 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.1.2 Graph theoretical planning . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.1.3 Agent-based modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7.1.4 Serious gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.2 Contrasting the approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.2.1 Goals of the exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
7.2.2 Logic or framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7.2.3 Abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7.2.4 Openness of the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.2.5 Main elements concerned . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.2.6 Learning parties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.2.7 Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7.2.8 Rules present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7.2.9 Level of detail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7.2.10 Treatment of uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7.3 Three pitfalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.4 Multi-perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.4.1 Case studies as a general approach . . . . . . . . . . . . . . . . . . . . . . 109
7.4.2 Graph theory and agent-based models . . . . . . . . . . . . . . . . . . . 109
7.4.3 Bringing together simulation and gaming . . . . . . . . . . . . . . . . . 110
7.4.4 Graph theory and gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.5 Using research for policy making . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
8 Discussion and Conclusion 113
8.1 Insights from the applied methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
8.1.1 Cooperation in case studies . . . . . . . . . . . . . . . . . . . . . . . . . . 113
8.1.2 Cooperation in graph theoretical planning . . . . . . . . . . . . . . . . 114
8.1.3 Cooperation in agent-based models . . . . . . . . . . . . . . . . . . . . . 115
8.1.4 Cooperation in serious gaming . . . . . . . . . . . . . . . . . . . . . . . . 115
8.2 Learning from previous research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
8.3 Cooperation in complex socio-technical systems . . . . . . . . . . . . . . . . . 117
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8.4 Informing decision makers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
8.5 Reflection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
8.5.1 Energy networks are like other cooperation efforts . . . . . . . . . . . 120
8.5.2 Choosing your game is no routine . . . . . . . . . . . . . . . . . . . . . . 120
8.5.3 Networks old and new . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
8.5.4 Muddling through – a pessimistic view? . . . . . . . . . . . . . . . . . . 121
8.5.5 Macro myopia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
8.5.6 Procedures as emergent property . . . . . . . . . . . . . . . . . . . . . . 122
8.5.7 Testing and validating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
8.5.8 Simple versus complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
8.5.9 On researching and modelling cooperation . . . . . . . . . . . . . . . . 123
8.5.10 Postnormal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
8.5.11 Complexity and modesty . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
8.6 Further research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Appendices 126
A Graph theory 129
A.1 Minimum cost spanning tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
A.2 Euclidean Steiner minimal tree problem . . . . . . . . . . . . . . . . . . . . . . . 129
A.3 Minimal cost Gilbert network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
A.4 Adapted Melzak method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
A.4.1 Determine angles of edges incident to Steiner points . . . . . . . . . . 131
A.4.2 Determine location of Steiner points . . . . . . . . . . . . . . . . . . . . 132
B Interviewees 137
C Questionnaire for cases 139
D Unveiling models 141
E Questionnaire for the Energy Market Game 143
F Outcome of the 2012 questionnaire 145
Bibliography 147
Acknowledgements 163
Curriculum vitae 165
Next Generation Infrastructures PhD thesis series 167
Abstract
To ensure dependable, affordable, and sustainable use of energy, stakeholders in energy production, distribution, and consumption are increasingly seeking for cooperation. They aim to jointly tackle large energy projects in an environmental context that is changing at an increasing rate, towards increasing complexity. Cooperation is seen as a remedy against the uncertainties of a hyper-competitive society, but the mechanisms of cooperation and the trade-offs are still poorly understood. This thesis provides clarification on how we can use different methods to understand cooperation activities and how to support cooperative efforts. We come to the conclusion that cooperation is a multidimensional issue that can only be understood properly when looking through different research lenses. Each perspective leads to a different image of cooperation and a clarification of why actors take specific steps in a process, what they aim to accomplish, and how they behave. The investigated methods (graph theoretical planning, agent-based modelling, serious gaming, and case studies) are valuable for understanding the decision making process, but no method can predict the results of cooperation attempts. We deem this impossible given the complexity of the systems we are interested in. However, graph theoretical planning can quickly provide information on network spatial configurations given certain constraints. Agent-based modelling allows for investigating the diversity of actors and the system consequences of their responses to each other. Serious gaming focuses more on players’ behaviour to each other and to the system. Case studies provide a rich description of the systems that we are interested in and allows for extraction of (procedural) lessons. To show the focus and/or breadth of each method we mapped them in two dimensions. The first dimension that we distinguish is that of world-view. A ‘rational’ perspective seeks for clear cause and effect relationships, clearly identified goals, and knowable rules and laws. A ‘behavioural’ perspective acknowledges the idiosyncrasies of individual decision makers and the fact that behaviour is to a great extent determined by social settings and networks of power and influence. A procedural view emphasises the process steps that are necessary for achieving cooperation – the emergent ‘rules of the game’. The second dimension pertains to the level of abstraction. Following general systems theory, we find that a distinction in micro-meso-macro level phenomena helps in classifying the different strands of research and their contribution to systemic understanding of cooperation phenomena. While we are interested in cooperation among organisations (meso level), we acknowledge that organisations consist of individuals (micro level) and form a part of a larger institutional, cultural, or national setting (macro level). Cooperation in organisations is both influenced from ‘above’ and from ‘below’ in interdependent ways.
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