Agent-based modelling of socio-technical systems

✍ Scribed by Nikolic, Igor; Lukszo, Zofia; Dam, Koen H. van

• Publisher: Springer
• Year: 2013
• Tongue: English
• Leaves: 285
• Series: Agent-based social systems 9
• Category: Library

✦ Synopsis

Decision makers in large scale interconnected network systems require simulation models for decision support. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level environment. How can such systems be modelled and how can the socio-technical complexity be captured? Agent-based modelling is a proven approach to handle this challenge. This book provides a practical introduction to agent-based modelling of socio-technical systems, based on a methodology that has been developed at Delft University of Technology and which has been deployed in a large number of case studies. The book consists of two parts: the first presents the background, theory and methodology as well as practical guidelines and procedures for building models. In the second part this theory is applied to a number of case studies, where for each model the development steps are presented extensively, preparing the reader for creating own models.

✦ Table of Contents

Agent-Based Modelling of Socio-Technical Systems......Page 4
Foreword......Page 6
Preface......Page 9
Contents......Page 11
Contributors......Page 17
List of Figures......Page 23
List of Tables......Page 26
1.1 Why This Book?......Page 27
1.2 Infrastructures as Complex Adaptive Socio-technical Systems......Page 28
1.3 Better Decision-Making Needed......Page 30
1.4 Agent-Based Modelling for Decision Support......Page 31
References......Page 33
Part I: Theory and Practice......Page 35
2.1 Introduction......Page 36
2.1.2 Structure of the Chapter......Page 37
2.1.3 Example: Westland Greenhouse Cluster......Page 38
2.2.1 History of Systems Thinking......Page 39
Greenhouse Example......Page 40
2.2.2 Systems......Page 41
Components Are Interdependent......Page 42
Enduring......Page 43
2.2.3 World Views......Page 44
Greenhouse Example......Page 45
Objectivity......Page 46
Greenhouse Example......Page 47
Reductionism and Holism......Page 48
2.2.5 System Boundaries......Page 49
2.2.6 System Nestedness......Page 50
2.3 Adaptive......Page 51
2.3.1 Adaptation Versus Evolution......Page 52
2.3.2 Evolution-More than just Biology......Page 53
2.3.3 Adaptation in Its Many Forms......Page 55
2.3.4 Direction of Adaptation......Page 56
2.3.5 Coupled Fitness Landscape......Page 57
2.3.6 Intractability......Page 59
2.4.1 Simple......Page 61
Functional Simplicity......Page 62
Occam's Razor......Page 63
2.4.2 Complicated......Page 64
2.4.3 Complex......Page 66
Dynamics......Page 67
Self-similarity or Scale Invariance......Page 68
2.5 Complex Adaptive Systems......Page 69
2.5.1 Chaos and Randomness......Page 70
Initial Conditions......Page 71
Instability and Robustness......Page 72
2.5.2 Emergence, Self-organisation and Patterns......Page 73
Greenhouse Example......Page 74
Patterns......Page 75
2.6.1 What Does a Model of a Complex Adaptive System Need?......Page 76
Multi-domain and Multi-disciplinary Knowledge......Page 77
Modelling Options......Page 78
2.6.2 Agent-Based Modelling......Page 79
Agent-Based Model......Page 80
Artificial Intelligence......Page 81
2.7.1 Agent......Page 82
2.7.1.1 State......Page 83
Rules......Page 84
Greenhouse Example......Page 85
2.7.2.1 Information......Page 86
2.7.2.2 Structure......Page 87
Space......Page 88
Scale-Free Networks......Page 89
2.7.3 Time......Page 90
Scheduler......Page 91
Greenhouse Example......Page 92
References......Page 93
3.1 Introduction......Page 97
3.2 Step 1: Problem Formulation and Actor Identification......Page 98
3.2.1 Step 1 Example......Page 99
Other Actors......Page 100
3.3.1 Inventory......Page 101
3.3.2 Structuring......Page 102
3.3.2.2 Iteration......Page 103
3.3.2.3 Environment......Page 104
3.3.3 Step 2 Example......Page 105
3.4 Step 3: Concept Formalisation......Page 106
3.4.1 Software Data Structures......Page 107
3.4.2 Ontology......Page 108
Software Data Structures......Page 109
Ontology......Page 110
3.5.1 Developing a Model Narrative......Page 112
Computation and Assignment......Page 113
Conditions......Page 114
3.5.2.2 Unified Modelling Language......Page 115
Pseudo-code......Page 116
3.6 Step 5: Software Implementation......Page 117
3.6.1.2 Repast......Page 118
3.6.2 Programming Practices......Page 119
3.6.2.2 Documenting Code......Page 120
3.6.2.5 Bug Tracking......Page 121
3.7 Step 6: Model Verification......Page 122
3.7.1 Recording and Tracking Agent Behaviour......Page 124
3.7.2.1 Theoretical Prediction and Sanity Checks......Page 125
3.7.3 Interaction Testing in a Minimal Model......Page 126
3.7.4.1 Variability Testing......Page 127
3.7.5 Step 6 Example......Page 128
3.8.1.1 Hypothesis Type......Page 129
3.8.1.3 Scenarios and Scenario Space......Page 131
3.8.2.1 Full Factorial......Page 132
3.8.2.3 Latin Hypercube Sampling......Page 133
3.8.2.5 Repetitions......Page 134
3.8.2.6 Random Seed......Page 135
3.8.3.1 Running on a Single Computer......Page 136
3.8.3.3 Collecting and Storing Data......Page 137
Hypothesis Example......Page 138
3.9.1 Data Exploration......Page 140
3.9.1.2 Location of Patterns......Page 141
3.9.1.4 Analysis Tools......Page 142
3.9.2.2 Visualisation......Page 144
3.9.2.3 Visualisation Caveats......Page 146
3.9.4 Experiment Iteration......Page 147
Hypothesis Driven Analysis......Page 148
3.10 Step 9: Model Validation......Page 150
3.10.1 Historic Replay......Page 151
3.10.2 Expert Validation......Page 152
3.10.4 Validation by Model Replication......Page 153
3.11 Step 10: Model Use......Page 154
3.11.2 Raising New Questions......Page 155
3.11.4 Agent-Based Models and Stakeholders......Page 156
3.11.5 Computer Models and Mental Models......Page 157
3.11.6 Step 10 Example......Page 158
3.12 Chapter Conclusions......Page 159
References......Page 160
Part II: Case Studies......Page 162
4.1 Case Studies......Page 163
4.1.3 Strategic Models......Page 164
4.1.4 Setup of Case Study Chapters......Page 165
4.2.1 Aims......Page 166
4.2.2 Development......Page 167
4.2.3 Key Concepts......Page 168
4.2.4 Usage......Page 169
References......Page 171
5.1 Introduction......Page 172
5.1.2 Abnormal Situation Management......Page 173
5.1.3 Supply Chain Modelling......Page 174
5.2.1 Step 1: Problem Formulation and Actor Identification......Page 175
5.2.2 Step 2: System Identification and Decomposition......Page 176
5.3.1 Step 1: Problem Formulation and Actor Identification......Page 180
5.4 Step 3: Concept Formalisation......Page 181
5.5.1 Procurement......Page 185
5.5.2 Order Assignment......Page 186
5.7 Step 6: Model Verification......Page 188
5.8.1 Experimental Setup for the Oil Refinery Supply Chain......Page 190
5.8.2 Experimental Setup for the Multi-plant Enterprise......Page 193
5.9.1 Delay in Shipment in the Oil Refinery Supply Chain......Page 194
5.9.2 Normal and Abnormal Behaviour Analysis for the Multi-plant Enterprise......Page 195
5.11 Step 10: Model Use......Page 198
5.12 Conclusions......Page 199
References......Page 200
6.1 Introduction......Page 202
6.3.1 Inventory......Page 204
6.3.1.2 Technological Subsystem......Page 205
Social Interactions......Page 206
Technological Interactions......Page 207
6.3.2 Structuring......Page 208
6.4 Step 3: Concept Formalisation......Page 209
6.5.1 Social Structure......Page 211
6.5.2 Model Narrative and Pseudo Code......Page 212
6.6 Step 5: Software Implementation......Page 214
6.8 Step 7: Experimentation......Page 215
6.9 Step 8: Data Analysis......Page 216
6.10 Step 9: Model Validation......Page 218
6.12 Conclusions......Page 219
References......Page 220
7.1 Introduction......Page 222
7.2 Step 1: Problem Formulation and Actor Identification......Page 223
7.3 Step 2: System Identification and Decomposition......Page 225
7.5 Step 4: Model Formalisation......Page 227
7.5.1 Model Narrative......Page 228
7.5.3 Bidding on Markets......Page 229
7.6 Step 5: Software Implementation......Page 231
7.7.1 Preliminary Simulations......Page 232
7.7.3 Agent Behaviour Tests......Page 233
7.8 Step 7: Experimentation......Page 234
7.9 Step 8: Data Analysis......Page 235
7.11 Step 10: Model Use......Page 236
7.11.2 Serious Game......Page 237
7.12 Conclusions......Page 238
References......Page 239
8.1 Introduction......Page 241
8.3 Step 2: System Identification and Decomposition......Page 243
8.4 Step 3: Concept Formalisation......Page 246
8.5.1 Create Contracts......Page 248
8.5.3 Process......Page 249
8.7 Step 6: Model Verification......Page 251
8.8 Step 7: Experimentation......Page 253
8.9.1 Default Case......Page 255
8.9.2 Sweep 1......Page 256
8.9.3 Sweep 2......Page 257
8.10 Step 9: Model Validation......Page 259
8.11 Step 10: Model Use......Page 260
8.12 Conclusions......Page 261
References......Page 262
9.1 Complications of Modelling Socio-technical Systems......Page 264
9.1.1 Agent-Based Model as Software......Page 265
9.2.1 Semantic Web Technologies......Page 266
9.2.2 Using SPARQL to Extract Structured Data......Page 268
9.2.3 Using SPARQL Within Simulations......Page 269
9.2.3.1 SPARQL for Agent Intelligence......Page 270
9.2.3.2 SPARQL for Simulation Debugging......Page 271
9.3 Case Study: Mobile Phone Recycling Networks......Page 272
9.4.1 Limitations of Using a Single Ontology for Modelling......Page 276
9.4.2 Enabling Multiple System Views......Page 277
9.4.4 Simple Example of Integrating Multiple Ontologies......Page 278
References......Page 281
Index......Page 283