Deffuant / Takadama / Cioffi-Revilla Simulating Interacting Agents and Social Phenomena

The Second World Congress
1. Auflage 2010
ISBN: 978-4-431-99781-8
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 280 Seiten

Agent-based modeling and social simulation have emerged as an interdisciplinary area of social science that includes computational economics, organizational science, social dynamics, and complex systems. This area contributes to enriching our understanding of the fundamental processes of social phenomena caused by complex interactions among agents. Bringing together diverse approaches to social simulation and research agendas, this book presents a unique collection of contributions from the Second World Congress on Social Simulation, held in 2008 at George Mason University in Washington DC, USA. This book in particular includes articles on norms, diffusion, social networks, economy, markets and organizations, computational modeling, and programming environments, providing new hypotheses and theories, new simulation experiments compared with various data sets, and new methods for model design and development. These works emerged from a global and interdisciplinary scientific community of the three regional scientific associations for social simulation: the North American Association for Computational Social and Organizational  Science (NAACSOS; now the Computational Social Science Society, CSSS), the European Social Simulation Association (ESSA), and the Pacific Asian Association for Agent-bBased  Approach in Social Systems Sciences (PAAA).

Autoren/Hrsg.

Deffuant, Guillaume

Takadama, Keiki

Cioffi-Revilla, Claudio

Inhaltsverzeichnis

1;Simulating Interacting Agents and Social Phenomena;3
2;Preface;5
3;Contents;7
4;Part I: Norms, Diffusion and Social Networks;13
4.1;A Classification of Normative Architectures;14
4.1.1;1 .Introduction;14
4.1.2;2 .The Selected Cases;16
4.1.3;3 .A Classification of Design Decisions;18
4.1.3.1;3.1 .Design Decision 1: The Scale from Logic to Decision Theory;18
4.1.3.2;3.2 .Design Decision 2: Single or Social Agents?;20
4.1.3.3;3.3 .Design Decision 3: Concepts of Norms;21
4.1.3.4;3.4 .Design Decision 4: Static or Dynamic Norms?;23
4.1.3.5;3.5 .Design Decision 5: Norm Conflicts;23
4.1.4;4 .Conclusion;25
4.1.5;References;28
4.2;The Complex Loop of Norm Emergence: A Simulation Model;30
4.2.1;1 .Introduction;30
4.2.2;2 .Existent Approaches;32
4.2.3;3 .Social Norms;33
4.2.4;4 .Objectives;34
4.2.5;5 .Finding Out Norms;34
4.2.6;6 .Normative Architecture;35
4.2.6.1;6.1 .Norm Recognizer;35
4.2.7;7 .The Simulation Model;38
4.2.7.1;7.1 .Moving Across Scenarios;39
4.2.7.2;7.2 .Social Conformers;39
4.2.7.3;7.3 .Norm-Recognizers;39
4.2.8;8 .The Simulator;40
4.2.9;9 .Results and Discussion;40
4.2.9.1;9.1 .Results with Social Conformers;41
4.2.9.2;9.2 .Results with Norm Recognizers;42
4.2.10;10 .Concluding Remarks;44
4.2.11;References;45
4.3;A Social Network Model of Direct Versus Indirect Reciprocity in a Corrections-Based Therapeutic Community;47
4.3.1;1 .Introduction;47
4.3.2;2 .Theoretical Background;48
4.3.2.1;2.1 .Direct and Indirect Reciprocity;48
4.3.2.2;2.2 .Indirect and Direct Reciprocity in TCs;49
4.3.3;3 .Methodology;50
4.3.3.1;3.1 .Agent-Based Model;50
4.3.3.2;3.2 .Data;52
4.3.3.3;3.3 .Analysis;52
4.3.4;4 .Results;53
4.3.5;5 .Conclusion;55
4.3.6;References;56
4.4;A Force-Directed Layout for Community Detection with Automatic Clusterization;58
4.4.1;1 .Introduction;58
4.4.2;2 .A Weakness of .Q;60
4.4.3;3 .Related Work;60
4.4.4;4 .Community Structure Properties;61
4.4.4.1;4.1 .Node Degree;62
4.4.4.2;4.2 .Graph Distance;62
4.4.5;5 .Algorithms;64
4.4.5.1;5.1 .Force-Directed Layout;64
4.4.5.2;5.2 .Convergence;65
4.4.5.3;5.3 .Motivation;66
4.4.5.4;5.4 .Clustering;67
4.4.5.5;5.5 .Example;67
4.4.6;6 .Results;69
4.4.6.1;6.1 .Social Network Results;69
4.4.7;7 .Improvements;70
4.4.7.1;7.1 .a-Searching;70
4.4.7.2;7.2 .Refining e;70
4.4.8;8 .Results;71
4.4.9;9 .Conclusion & Future Work;71
4.4.10;References;72
4.5;Comparing Two Sexual Mixing Schemes for Modelling the Spread of HIV/AIDS;73
4.5.1;1 .Introduction;73
4.5.2;2 .Specification for Two Sexual Mixing Schemes;74
4.5.2.1;2.1 .A Scheme Based on Simple Aspiration and Quality Measure;75
4.5.2.2;2.2 .A Scheme Based on Endorsements;75
4.5.3;3 .Simulation Results;77
4.5.3.1;3.1 .Characteristics of the Simulated Heterosexual Networks;77
4.5.3.2;3.2 .HIV/AIDS Prevalence;80
4.5.4;4 .Discussion and Outlook;82
4.5.5;References;83
4.6;Exploring Context Permeability in Multiple Social Networks;85
4.6.1;1 .Introduction;85
4.6.2;2 .Multiple and Multi-Modal Relations;87
4.6.3;3 .Relations, Roles and Contexts;88
4.6.4;4 .Social Network Representations and Analysis;89
4.6.5;5 .Consensus Games;90
4.6.6;6 .Experimental Setting;90
4.6.7;7 .Analysis of Simulation Outcomes;91
4.6.8;8 .Conclusions and Future Work;93
4.6.9;References;94
4.7;A Naturalistic Multi-Agent Model of Word-of-Mouth Dynamics;96
4.7.1;1 .Modeling Word-of-Mouth;97
4.7.1.1;1.1 .Evidence on Word-of-Mouth;97
4.7.1.2;1.2 .Existing Models;98
4.7.1.3;1.3 .Target;98
4.7.2;2 .Model;99
4.7.2.1;2.1 .Structure of Interactions;99
4.7.2.2;2.2 .Knowledge Representation;100
4.7.2.3;2.3 .Communicative Behavior;100
4.7.3;3 .Simulation;101
4.7.3.1;3.1 .Dynamics with Active Searches;101
4.7.3.2;3.2 .Using an Event to Facilitate Diffusion of Information;103
4.7.3.3;3.3 .Diffusion of Related Products;104
4.7.4;4 .Discussion;105
4.7.5;References;106
5;Part II: Economy, Market and Organization;107
5.1;Introducing Preference Heterogeneity into a Monocentric Urban Model: An Agent-Based Land Market Model;108
5.1.1;1 .Introduction;109
5.1.2;2 .The Model;110
5.1.3;3 .Simulation Experiments;112
5.1.3.1;3.1 .Experiment 1;113
5.1.3.2;3.2 .Experiment 2;116
5.1.4;4 .Conclusions and Discussions;119
5.1.5;References;121
5.2;The Agent-Based Double Auction Markets: 15 Years On;123
5.2.1;1 .Introduction: It Takes Time to See “Change”;124
5.2.2;2 .Agent-Based Double Auction Markets: Literature Review;125
5.2.2.1;2.1 .Gode-Sunder Model;125
5.2.2.2;2.2 .Santa Fe Double Auction Markets;126
5.2.2.3;2.3 .Andrews-Prager Model;127
5.2.2.4;2.4 .AIE-DA;128
5.2.3;3 .Experimental Design;130
5.2.3.1;3.1 .Market Mechanism;130
5.2.3.2;3.2 .Trading Strategies;131
5.2.3.3;3.3 .GP Trading Agents;133
5.2.3.4;3.4 .Experimental Procedures;134
5.2.4;4 .Results: GP Agents Versus Non-Autonomous Traders;135
5.2.5;5 .Conclusion;138
5.2.6;References;139
5.3;A Doubly Structural Network Model and Analysis on the Emergence of Money*;141
5.3.1;1 .Introduction;141
5.3.1.1;1.1 .Problems Regarding the Origin/Emergence of Money;142
5.3.1.2;1.2 .Mathematical Models for the Emergence of Money;143
5.3.2;2 .Doubly Structural Network Model;144
5.3.3;3 .Doubly Structural Network Model of the Emergence of Money;146
5.3.4;4 .Mean-Field Dynamics Analysis of the Emergence of Money;147
5.3.4.1;4.1 .Mean-Field Dynamics;147
5.3.4.2;4.2 .Emergence Scenario Using Mean-Field Dynamics;147
5.3.5;5 .Simulation Experimentation;150
5.3.6;6 .Conclusion;152
5.3.7;References;152
5.4;Analysis of Knowledge Retrieval Heuristics in Concurrent Software Development Teams;154
5.4.1;1 .Introduction;155
5.4.2;2 .Model;155
5.4.2.1;2.1 .PCANNS Scheme;155
5.4.2.2;2.2 .Concurrent Software Development Team;156
5.4.2.3;2.3 .Knowledge Retrieval Heuristics;157
5.4.2.4;2.4 .Simulation Flow;158
5.4.3;3 .Parameter Calibration;160
5.4.4;4 .Effective Knowledge Retrieval Heuristics;160
5.4.5;5 .Scope and Grounding;163
5.4.6;6 .Summary and Future Work;163
5.4.7;.Appendix: Algorithm of Knowledge-Retrieval Heauristics;164
5.4.8;.Minimum effort type;164
5.4.9;.Risk aversion type;164
5.4.10;.“Ask others” type;165
5.4.11;.“Acquire on my own” type;165
5.4.12;.Broad retrieval type;165
5.4.13;.Random type;166
5.4.14;References;166
5.5;Reputation and Economic Performance in Industrial Districts: Modelling Social Complexity Through Multi-Agent Systems;167
5.5.1;1 .Introduction;167
5.5.1.1;1.1 .Research Hypothesis;169
5.5.2;2 .The Simulation Model;170
5.5.2.1;2.1 .Partner Selection and Economic Exchange;171
5.5.2.2;2.2 .Information Exchange;172
5.5.3;3 .Results;173
5.5.4;4 .Concluding Remarks;177
5.5.5;References;178
6;Part III: Modeling Approaches and Programming Environments;179
6.1;Injecting Data into Agent-Based Simulation;180
6.1.1;1 .Introduction;180
6.1.2;2 .The Classical View;182
6.1.2.1;2.1 .The Logic of Simulation;182
6.1.2.2;2.2 .Issues with Abstract ABMs;182
6.1.3;3 .Sources of Data;184
6.1.4;4 .The Data-Driven Flow: Adapting the Logic;185
6.1.5;5 .Discussion and Difficulties of the Approach;187
6.1.6;6 .A Case Study: the Mentat Model;188
6.1.6.1;6.1 .Context of the Model;188
6.1.6.2;6.2 .The Randomly Initialised Version: Mentat-RND;188
6.1.6.3;6.3 .The Version Initialised with Data: Mentat-DAT;189
6.1.6.4;6.4 .Comparison of Outputs;189
6.1.7;7 .Concluding Remarks;190
6.1.8;References;191
6.2;The MASON HouseholdsWorld Model of Pastoral Nomad Societies;193
6.2.1;1 .Introduction: Motivation and Background;193
6.2.2;2 .The HouseholdsWorld Model;195
6.2.2.1;2.1 .Households;196
6.2.2.2;2.2 .Camps;198
6.2.2.3;2.3 .Natural Environment;199
6.2.3;3 .Simulated Dynamics;199
6.2.4;4 .Summary;201
6.2.5;References;203
6.3;Effects of Adding a Simple Rule to a Reactive Simulation;205
6.3.1;1 .Introduction;205
6.3.2;2 .Extending the Original Model;206
6.3.3;3 .Social Organisation;208
6.3.4;4 .Final Considerations;211
6.3.5;References;211
6.4;Applying Image Texture Measures to Describe Segregation in Agent-Based Modeling;212
6.4.1;1 .Introduction;212
6.4.2;2 .Gibbs Random Field in Image Processing;213
6.4.2.1;2.1 .Definition;213
6.4.2.2;2.2 .Image Simulation with Markov Chain Monte Carlo Methods;215
6.4.2.3;2.3 .Example: An Ising/Potts Class of GRF;216
6.4.3;3 .Schelling Segregation Model;217
6.4.3.1;3.1 .Original Schelling Model;217
6.4.3.2;3.2 .Schelling Model as a Derin and Elliott GRF;218
6.4.4;4 .Texture Measures Applied to Segregation;219
6.4.5;5 .Application to the Output of an Agent Based Model;221
6.4.6;6 .Applications to Social Simulation Beyond the 2D Grid;222
6.4.7;7 .Conclusion;222
6.4.8;References;224
6.5;Autonomous Tags: Language as Generative of Culture;225
6.5.1;1 .Introduction;226
6.5.2;2 .Symbolic Interactionist Simulation;229
6.5.3;3 .SISTER;230
6.5.3.1;3.1 .The Design of SISTER;231
6.5.3.2;3.2 .Mutual Information to Measure Roles;236
6.5.3.3;3.3 .Experiment;236
6.5.3.4;3.4 .Results;238
6.5.3.5;3.5 .Discussion;242
6.5.4;4 .Future Directions;248
6.5.5;References;249
6.6;Virtual City Model for Simulating Social Phenomena;251
6.6.1;1 .Introduction;251
6.6.2;2 .Abstract of SOARS;252
6.6.3;3 .Details of the Model;252
6.6.4;4 .Structure of Virtual City;253
6.6.5;5 .Settings for the Virtual City;253
6.6.6;6 .Behaviors of Agents in the Virtual City;254
6.6.7;7 .An Example of the Virtual City;256
6.6.8;8 .Use Case of the Model;256
6.6.9;9 .Future Works;259
6.6.10;References;260
6.7;Modeling Endogenous Coordination Using a Dynamic Language*;262
6.7.1;1 .Introduction;263
6.7.2;2 .Related Work;263
6.7.3;3 .The EndEC Model;264
6.7.4;4 .Implementation of the EndEC Model;266
6.7.5;5 .Conclusions;272
6.7.6;References;273
7;Author Index;274
8;Keyword Index;276