Dam / Nikolic / Lukszo Agent-Based Modelling of Socio-Technical Systems

1. Auflage 2012
ISBN: 978-94-007-4933-7
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 9, 285 Seiten

Reihe: Agent-Based Social Systems

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 TU Delft 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.

Autoren/Hrsg.

Nikolic, Igor

Lukszo, Zofia

Dam, Koen H. van

Inhaltsverzeichnis

1;Agent-Based Modelling of Socio-Technical Systems;4
1.1;Foreword;6
1.2;Preface;9
1.3;Contents;11
1.4;Contributors;17
1.5;List of Figures;23
1.6;List of Tables;26
2;Chapter 1: Introduction;27
2.1;1.1 Why This Book?;27
2.2;1.2 Infrastructures as Complex Adaptive Socio-technical Systems;28
2.3;1.3 Better Decision-Making Needed;30
2.4;1.4 Agent-Based Modelling for Decision Support;31
2.5;1.5 A Book in Two Parts;33
2.5.1;Acknowledgements;33
2.6;References;33
3;Part I: Theory and Practice;35
3.1;Chapter 2: Theory;36
3.1.1;2.1 Introduction;36
3.1.1.1;2.1.1 Focus;37
3.1.1.2;2.1.2 Structure of the Chapter;37
3.1.1.3;2.1.3 Example: Westland Greenhouse Cluster;38
3.1.2;2.2 Systems;39
3.1.2.1;2.2.1 History of Systems Thinking;39
3.1.2.1.1;Greenhouse Example;40
3.1.2.2;2.2.2 Systems;41
3.1.2.2.1;Idealisation;42
3.1.2.2.2;Multiple Components;42
3.1.2.2.3;Components Are Interdependent;42
3.1.2.2.4;Organised;43
3.1.2.2.5;Emergent Properties;43
3.1.2.2.6;Boundaries;43
3.1.2.2.7;Enduring;43
3.1.2.2.8;Environment;44
3.1.2.2.9;Feedback;44
3.1.2.2.10;Non-trivial Behaviour;44
3.1.2.3;2.2.3 World Views;44
3.1.2.3.1;Greenhouse Example;45
3.1.2.4;2.2.4 Observer-Dependence;46
3.1.2.4.1;Objectivity;46
3.1.2.4.2;Greenhouse Example;47
3.1.2.4.3;Reductionism and Holism;48
3.1.2.4.4;Greenhouse Example;49
3.1.2.5;2.2.5 System Boundaries;49
3.1.2.6;2.2.6 System Nestedness;50
3.1.2.6.1;Greenhouse Example;51
3.1.3;2.3 Adaptive;51
3.1.3.1;2.3.1 Adaptation Versus Evolution;52
3.1.3.2;2.3.2 Evolution-More than just Biology;53
3.1.3.3;2.3.3 Adaptation in Its Many Forms;55
3.1.3.4;2.3.4 Direction of Adaptation;56
3.1.3.5;2.3.5 Coupled Fitness Landscape;57
3.1.3.5.1;Irreversibility;59
3.1.3.6;2.3.6 Intractability;59
3.1.4;2.4 Complexity;61
3.1.4.1;2.4.1 Simple;61
3.1.4.1.1;Functional Simplicity;62
3.1.4.1.2;Structural Simplicity;63
3.1.4.1.3;Occam's Razor;63
3.1.4.1.4;Greenhouse Example;64
3.1.4.2;2.4.2 Complicated;64
3.1.4.2.1;Greenhouse Example;66
3.1.4.3;2.4.3 Complex;66
3.1.4.3.1;Dynamics;67
3.1.4.3.2;Self-similarity or Scale Invariance;68
3.1.4.3.3;Greenhouse Example;69
3.1.5;2.5 Complex Adaptive Systems;69
3.1.5.1;Greenhouse Example;70
3.1.5.2;2.5.1 Chaos and Randomness;70
3.1.5.2.1;Repetition;71
3.1.5.2.2;Deterministic;71
3.1.5.2.3;Initial Conditions;71
3.1.5.2.4;Attractors;72
3.1.5.2.5;Instability and Robustness;72
3.1.5.2.6;Greenhouse Example;73
3.1.5.3;2.5.2 Emergence, Self-organisation and Patterns;73
3.1.5.3.1;Greenhouse Example;74
3.1.5.3.2;Self-organisation;75
3.1.5.3.3;Patterns;75
3.1.6;2.6 Modelling Complex Adaptive Systems;76
3.1.6.1;2.6.1 What Does a Model of a Complex Adaptive System Need?;76
3.1.6.1.1;Multi-domain and Multi-disciplinary Knowledge;77
3.1.6.1.2;Generative and Bottom up Capacity;78
3.1.6.1.3;Adaptivity;78
3.1.6.1.4;Modelling Options;78
3.1.6.2;2.6.2 Agent-Based Modelling;79
3.1.6.3;2.6.3 What It Is and Is not;80
3.1.6.3.1;Agent-Based Model;80
3.1.6.3.2;Multi-agent System;81
3.1.6.3.3;Arti?cial Intelligence;81
3.1.6.3.4;Object-Oriented Program?;82
3.1.7;2.7 Anatomy of an Agent-Based Model;82
3.1.7.1;2.7.1 Agent;82
3.1.7.1.1;2.7.1.1 State;83
3.1.7.1.2;2.7.1.2 Changing States;84
3.1.7.1.2.1;Rules;84
3.1.7.1.2.2;Actions;85
3.1.7.1.2.3;Behaviour;85
3.1.7.1.2.4;Greenhouse Example;85
3.1.7.2;2.7.2 Environment;86
3.1.7.2.1;2.7.2.1 Information;86
3.1.7.2.2;2.7.2.2 Structure;87
3.1.7.2.2.1;Soup;88
3.1.7.2.2.2;Space;88
3.1.7.2.2.3;Small-World Networks;89
3.1.7.2.2.4;Scale-Free Networks;89
3.1.7.2.2.5;Greenhouse Example;90
3.1.7.3;2.7.3 Time;90
3.1.7.3.1;Discrete Time;91
3.1.7.3.2;Assumption of Parallelism;91
3.1.7.3.3;Scheduler;91
3.1.7.3.4;Greenhouse Example;92
3.1.8;References;93
3.2;Chapter 3: Practice;97
3.2.1;3.1 Introduction;97
3.2.2;3.2 Step 1: Problem Formulation and Actor Identi?cation;98
3.2.2.1;3.2.1 Step 1 Example;99
3.2.2.1.1;Example: Westland Greenhouse Cluster;100
3.2.2.1.2;What Is the Problem;100
3.2.2.1.3;Initial Hypothesis;100
3.2.2.1.4;Whose Problem Are We Addressing?;100
3.2.2.1.5;Other Actors;100
3.2.2.1.6;Our Role;101
3.2.3;3.3 Step 2: System Identi?cation and Decomposition;101
3.2.3.1;3.3.1 Inventory;101
3.2.3.2;3.3.2 Structuring;102
3.2.3.2.1;3.3.2.1 Structuring of Agents and Interactions;103
3.2.3.2.2;3.3.2.2 Iteration;103
3.2.3.2.3;3.3.2.3 Environment;104
3.2.3.3;3.3.3 Step 2 Example;105
3.2.4;3.4 Step 3: Concept Formalisation;106
3.2.4.1;3.4.1 Software Data Structures;107
3.2.4.2;3.4.2 Ontology;108
3.2.4.3;3.4.3 Step 3 Example;109
3.2.4.3.1;Software Data Structures;109
3.2.4.3.2;Ontology;110
3.2.5;3.5 Step 4: Model Formalisation;112
3.2.5.1;3.5.1 Developing a Model Narrative;112
3.2.5.2;3.5.2 Pseudo-code;113
3.2.5.2.1;3.5.2.1 Elements of Pseudo-code;113
3.2.5.2.1.1;Computation and Assignment;113
3.2.5.2.1.2;Iterations and Loops;114
3.2.5.2.1.3;Conditions;114
3.2.5.2.1.4;Input/Output;115
3.2.5.2.2;3.5.2.2 Uni?ed Modelling Language;115
3.2.5.3;3.5.3 Step 4 Example;116
3.2.5.3.1;Model Narrative Example, Actions per Time Tick;116
3.2.5.3.2;Pseudo-code;116
3.2.6;3.6 Step 5: Software Implementation;117
3.2.6.1;3.6.1 Modelling Environment;118
3.2.6.1.1;3.6.1.1 NetLogo;118
3.2.6.1.2;3.6.1.2 Repast;118
3.2.6.1.3;3.6.1.3 Custom Code;119
3.2.6.2;3.6.2 Programming Practices;119
3.2.6.2.1;3.6.2.1 Version Control;120
3.2.6.2.2;3.6.2.2 Documenting Code;120
3.2.6.2.3;3.6.2.3 Naming Conventions;121
3.2.6.2.4;3.6.2.4 Divisions of Tasks and Responsibilities;121
3.2.6.2.5;3.6.2.5 Bug Tracking;121
3.2.6.3;3.6.3 Step 5 Example;122
3.2.6.3.1;NetLogo;122
3.2.7;3.7 Step 6: Model Veri?cation;122
3.2.7.1;3.7.1 Recording and Tracking Agent Behaviour;124
3.2.7.2;3.7.2 Single-Agent Testing;125
3.2.7.2.1;3.7.2.1 Theoretical Prediction and Sanity Checks;125
3.2.7.2.2;3.7.2.2 Breaking the Agent;126
3.2.7.3;3.7.3 Interaction Testing in a Minimal Model;126
3.2.7.4;3.7.4 Multi-agent Testing;127
3.2.7.4.1;3.7.4.1 Variability Testing;127
3.2.7.4.2;3.7.4.2 Timeline Sanity;128
3.2.7.5;3.7.5 Step 6 Example;128
3.2.8;3.8 Step 7: Experimentation;129
3.2.8.1;3.8.1 Experiment Design Aspects;129
3.2.8.1.1;3.8.1.1 Hypothesis Type;129
3.2.8.1.2;3.8.1.2 Time;131
3.2.8.1.3;3.8.1.3 Scenarios and Scenario Space;131
3.2.8.2;3.8.2 Experiment Setup;132
3.2.8.2.1;3.8.2.1 Full Factorial;132
3.2.8.2.2;3.8.2.2 Random Parameter;133
3.2.8.2.3;3.8.2.3 Latin Hypercube Sampling;133
3.2.8.2.4;3.8.2.4 Monte Carlo;134
3.2.8.2.5;3.8.2.5 Repetitions;134
3.2.8.2.6;3.8.2.6 Random Seed;135
3.2.8.3;3.8.3 Experiment Execution;136
3.2.8.3.1;3.8.3.1 Running on a Single Computer;136
3.2.8.3.2;3.8.3.2 Scaling, Running on Clusters;137
3.2.8.3.3;3.8.3.3 Collecting and Storing Data;137
3.2.8.3.4;3.8.3.4 Checking Data Consistency;138
3.2.8.4;3.8.4 Step 7 Example;138
3.2.8.4.1;Hypothesis Example;138
3.2.9;3.9 Step 8: Data Analysis;140
3.2.9.1;3.9.1 Data Exploration;140
3.2.9.1.1;3.9.1.1 Hypothesis Driven Analysis;141
3.2.9.1.2;3.9.1.2 Location of Patterns;141
3.2.9.1.3;3.9.1.3 Data Analysis;142
3.2.9.1.4;3.9.1.4 Analysis Tools;142
3.2.9.2;3.9.2 Pattern Visualisation and Identi?cation;144
3.2.9.2.1;3.9.2.1 Recognising Emergent Patterns;144
3.2.9.2.1.1;Dynamic Behaviour;144
3.2.9.2.1.2;Attractor Changes;144
3.2.9.2.1.3;Metastable Behaviour;144
3.2.9.2.1.4;Lack of a Pattern;144
3.2.9.2.2;3.9.2.2 Visualisation;144
3.2.9.2.2.1;Network Representation;146
3.2.9.2.2.2;Animation;146
3.2.9.2.3;3.9.2.3 Visualisation Caveats;146
3.2.9.3;3.9.3 Pattern Interpretation and Explanation;147
3.2.9.4;3.9.4 Experiment Iteration;147
3.2.9.5;3.9.5 Step 8 Example;148
3.2.9.5.1;Hypothesis Driven Analysis;148
3.2.10;3.10 Step 9: Model Validation;150
3.2.10.1;3.10.1 Historic Replay;151
3.2.10.2;3.10.2 Expert Validation;152
3.2.10.3;3.10.3 Validation by Literature Comparison;153
3.2.10.4;3.10.4 Validation by Model Replication;153
3.2.10.5;3.10.5 Step 9 Example;154
3.2.10.5.1;Expert Validation;154
3.2.11;3.11 Step 10: Model Use;154
3.2.11.1;3.11.1 Outcome Presentation;155
3.2.11.2;3.11.2 Raising New Questions;155
3.2.11.3;3.11.3 Long Term Stakeholder Engagement;156
3.2.11.4;3.11.4 Agent-Based Models and Stakeholders;156
3.2.11.5;3.11.5 Computer Models and Mental Models;157
3.2.11.6;3.11.6 Step 10 Example;158
3.2.12;3.12 Chapter Conclusions;159
3.2.13;References;160
4;Part II: Case Studies;162
4.1;Chapter 4: Introduction to the Case Studies;163
4.1.1;4.1 Case Studies;163
4.1.1.1;4.1.1 Operational Models;164
4.1.1.2;4.1.2 Tactical Models;164
4.1.1.3;4.1.3 Strategic Models;164
4.1.1.4;4.1.4 Setup of Case Study Chapters;165
4.1.1.5;4.1.5 State-of-the-Art Modelling;166
4.1.2;4.2 An Ontology for Socio-technical Systems;166
4.1.2.1;4.2.1 Aims;166
4.1.2.2;4.2.2 Development;167
4.1.2.3;4.2.3 Key Concepts;168
4.1.2.4;4.2.4 Usage;169
4.1.3;References;171
4.2;Chapter 5: Agent-Based Models of Supply Chains;172
4.2.1;5.1 Introduction;172
4.2.1.1;5.1.1 Supply Chains;173
4.2.1.2;5.1.2 Abnormal Situation Management;173
4.2.1.3;5.1.3 Supply Chain Modelling;174
4.2.1.4;5.1.4 Chapter Organisation;175
4.2.2;5.2 Oil Re?nery Supply Chain;175
4.2.2.1;5.2.1 Step 1: Problem Formulation and Actor Identi?cation;175
4.2.2.2;5.2.2 Step 2: System Identi?cation and Decomposition;176
4.2.3;5.3 Multi-plant Enterprise Supply Chain;180
4.2.3.1;5.3.1 Step 1: Problem Formulation and Actor Identi?cation;180
4.2.3.2;5.3.2 Step 2: System Identi?cation and Decomposition;181
4.2.4;5.4 Step 3: Concept Formalisation;181
4.2.5;5.5 Step 4: Model Formalisation;185
4.2.5.1;5.5.1 Procurement;185
4.2.5.2;5.5.2 Order Assignment;186
4.2.6;5.6 Step 5: Software Implementation;188
4.2.7;5.7 Step 6: Model Veri?cation;188
4.2.8;5.8 Step 7: Experimentation;190
4.2.8.1;5.8.1 Experimental Setup for the Oil Re?nery Supply Chain;190
4.2.8.2;5.8.2 Experimental Setup for the Multi-plant Enterprise;193
4.2.9;5.9 Step 8: Data Analysis;194
4.2.9.1;5.9.1 Delay in Shipment in the Oil Re?nery Supply Chain;194
4.2.9.2;5.9.2 Normal and Abnormal Behaviour Analysis for the Multi-plant Enterprise;195
4.2.10;5.10 Step 9: Model Validation;198
4.2.11;5.11 Step 10: Model Use;198
4.2.12;5.12 Conclusions;199
4.2.13;References;200
4.3;Chapter 6: An Agent-Based Model of Consumer Lighting;202
4.3.1;6.1 Introduction;202
4.3.2;6.2 Step 1: Problem Formulation and Actor Identi?cation;204
4.3.3;6.3 Step 2: System Identi?cation and Decomposition;204
4.3.3.1;6.3.1 Inventory;204
4.3.3.1.1;6.3.1.1 Social Subsystem;205
4.3.3.1.2;6.3.1.2 Technological Subsystem;205
4.3.3.1.3;6.3.1.3 Interactions;206
4.3.3.1.3.1;Social Interactions;206
4.3.3.1.3.2;Technological Interactions;207
4.3.3.1.3.3;Socio-technical Interactions;208
4.3.3.2;6.3.2 Structuring;208
4.3.4;6.4 Step 3: Concept Formalisation;209
4.3.5;6.5 Step 4: Model Formalisation;211
4.3.5.1;6.5.1 Social Structure;211
4.3.5.2;6.5.2 Model Narrative and Pseudo Code;212
4.3.6;6.6 Step 5: Software Implementation;214
4.3.7;6.7 Step 6: Model Veri?cation;215
4.3.8;6.8 Step 7: Experimentation;215
4.3.9;6.9 Step 8: Data Analysis;216
4.3.10;6.10 Step 9: Model Validation;218
4.3.11;6.11 Step 10: Model Use;219
4.3.12;6.12 Conclusions;219
4.3.13;References;220
4.4;Chapter 7: An Agent-Based Model of CO2 Policies and Electricity Generation;222
4.4.1;7.1 Introduction;222
4.4.2;7.2 Step 1: Problem Formulation and Actor Identi?cation;223
4.4.3;7.3 Step 2: System Identi?cation and Decomposition;225
4.4.4;7.4 Step 3: Concept Formalisation;227
4.4.5;7.5 Step 4: Model Formalisation;227
4.4.5.1;7.5.1 Model Narrative;228
4.4.5.2;7.5.2 Investment;229
4.4.5.3;7.5.3 Bidding on Markets;229
4.4.6;7.6 Step 5: Software Implementation;231
4.4.7;7.7 Step 6: Model Veri?cation;232
4.4.7.1;7.7.1 Preliminary Simulations;232
4.4.7.2;7.7.2 Extreme-Condition Tests and Discussion;233
4.4.7.3;7.7.3 Agent Behaviour Tests;233
4.4.7.4;7.7.4 Repetitions;234
4.4.8;7.8 Step 7: Experimentation;234
4.4.9;7.9 Step 8: Data Analysis;235
4.4.10;7.10 Step 9: Model Validation;236
4.4.11;7.11 Step 10: Model Use;236
4.4.11.1;7.11.1 Emergent Insights from Iterations and Discussions;237
4.4.11.2;7.11.2 Serious Game;237
4.4.12;7.12 Conclusions;238
4.4.13;References;239
4.5;Chapter 8: An Agent-Based Model of a Mobile Phone Production, Consumption and Recycling Network;241
4.5.1;8.1 Introduction;241
4.5.2;8.2 Step 1: Problem Formulation and Actor Identi?cation;243
4.5.3;8.3 Step 2: System Identi?cation and Decomposition;243
4.5.4;8.4 Step 3: Concept Formalisation;246
4.5.5;8.5 Step 4: Model Formalisation;248
4.5.5.1;8.5.1 Create Contracts;248
4.5.5.2;8.5.2 Purchase;249
4.5.5.3;8.5.3 Process;249
4.5.5.4;8.5.4 Invest;251
4.5.6;8.6 Step 5: Software Implementation;251
4.5.7;8.7 Step 6: Model Veri?cation;251
4.5.8;8.8 Step 7: Experimentation;253
4.5.9;8.9 Step 8: Data Analysis;255
4.5.9.1;8.9.1 Default Case;255
4.5.9.2;8.9.2 Sweep 1;256
4.5.9.3;8.9.3 Sweep 2;257
4.5.9.4;8.9.4 Sweep 3;259
4.5.10;8.10 Step 9: Model Validation;259
4.5.11;8.11 Step 10: Model Use;260
4.5.12;8.12 Conclusions;261
4.5.13;References;262
4.6;Chapter 9: Next Steps in Modelling Socio-technical Systems: Towards Collaborative Modelling;264
4.6.1;9.1 Complications of Modelling Socio-technical Systems;264
4.6.1.1;9.1.1 Agent-Based Model as Software;265
4.6.2;9.2 Applying Semantic Web Technologies and Methods for Modelling;266
4.6.2.1;9.2.1 Semantic Web Technologies;266
4.6.2.2;9.2.2 Using SPARQL to Extract Structured Data;268
4.6.2.3;9.2.3 Using SPARQL Within Simulations;269
4.6.2.3.1;9.2.3.1 SPARQL for Agent Intelligence;270
4.6.2.3.2;9.2.3.2 SPARQL for Simulation Debugging;271
4.6.2.3.3;9.2.3.3 Disadvantages;272
4.6.2.4;9.2.4 Implementations by Other Researchers;272
4.6.3;9.3 Case Study: Mobile Phone Recycling Networks;272
4.6.4;9.4 Future Work: Enabling Collaboration Between Modellers;276
4.6.4.1;9.4.1 Limitations of Using a Single Ontology for Modelling;276
4.6.4.2;9.4.2 Enabling Multiple System Views;277
4.6.4.3;9.4.3 Integrating Multiple Ontologies Together for Simulations;278
4.6.4.4;9.4.4 Simple Example of Integrating Multiple Ontologies;278
4.6.5;9.5 Conclusion;281
4.6.6;References;281
5;Index;283