Chen / Yamamoto / Terano Agent-Based Approaches in Economic and Social Complex Systems VI

Post-Proceedings of The AESCS International Workshop 2009
1. Auflage 2011
ISBN: 978-4-431-53907-0
Verlag: Springer Tokyo
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 8, 202 Seiten, eBook

Reihe: Agent-Based Social Systems

Agent-based modeling/simulation is an emergent approach to the analysis of social and economic systems. It provides a bottom-up experimental method to be applied to social sciences such as economics, management, sociology, and politics as well as some engineering fields dealing with social activities. This book includes selected papers presented at the Sixth International Workshop on Agent-Based Approaches in Economic and Social Complex Systems held in Taipei in 2009. We have 39 presentations in the conference, and 14 papers are selected to be included in this volume. These 14 papers are then grouped into six parts: Agent-based financial markets; Financial forecasting and investment; Cognitive modeling of agents; Complexity and policy analysis; Agent-based modeling of good societies; and Miscellany. The research presented here shows the state of the art in this rapidly growing field.

Zielgruppe

Research

Autoren/Hrsg.

Yamamoto, Ryuichi

Terano, Takao

Chen, Shu-Heng

Inhaltsverzeichnis

1;Agent-Based Approaches in Economic and Social Complex Systems VI;1
1.1;Preface
;5
1.2;Committees and Chairs of AESCS’09
;7
1.3;Contents
;9
1.4;Part I Agent-Based Financial Markets;11
1.4.1;Comprehensive Analysis of Information Transmission Among Agents: Similarity and Heterogeneity of Collective Behavior;12
1.4.1.1;1 Introduction;12
1.4.1.2;2 Literature Survey;14
1.4.1.3;3 Model and Methods;15
1.4.1.4;4 Empirical Analysis;18
1.4.1.5;5 Conclusions;25
1.4.1.6;References;26
1.4.2;Examining the Effects of Traders' Overconfidence on Market Behavior;27
1.4.2.1;1 Introduction;27
1.4.2.2;2 The Model;29
1.4.2.2.1;2.1 Market Structure;29
1.4.2.2.2;2.2 Learning of Traders;30
1.4.2.3;3 Simulations;32
1.4.2.4;4 Conclusion;38
1.4.2.5;References;38
1.5;Part II Financial Forecasting and Investment;40
1.5.1;Short Time Correction to Mean Variance Analysis in an Optimized Two-Stock Portfolio;41
1.5.1.1;1 Introduction;41
1.5.1.2;2 Mean-Variance Analysis for the Long Time Scale;43
1.5.1.3;3 Short Term Correction;44
1.5.1.4;4 Decision Mechanism;45
1.5.1.5;5 Simulation Result;46
1.5.1.6;6 Conclusion;50
1.5.1.7;References;50
1.5.2;Exchange Rate Forecasting with Hybrid Genetic Algorithms;52
1.5.2.1;1 Introduction;52
1.5.2.2;2 Methodology and Data Description;54
1.5.2.2.1;2.1 Genetic Algorithms;54
1.5.2.2.2;2.2 Particle Swarm Optimization;55
1.5.2.2.3;2.3 Back Propagation Network;56
1.5.2.2.4;2.4 Variables and Data Normalization;57
1.5.2.3;3 Experiment Design;57
1.5.2.4;4 Experiment Results;59
1.5.2.4.1;4.1 Comparing the Forecasting Results by Three Criteria;59
1.5.2.4.2;4.2 Comparison of the Forecast Results by Monthly Exchange Rate;61
1.5.2.5;5 Conclusion and Suggestions;62
1.5.2.6;References;62
1.6;Part III Cognitive Modeling of Agents;64
1.6.1;Learning Backward Induction: A Neural Network Agent Approach;65
1.6.1.1;1 Introduction;65
1.6.1.2;2 Methodology;66
1.6.1.2.1;2.1 The Class of Games ;66
1.6.1.2.2;2.2 Introduction to Neural Networks;67
1.6.1.2.3;2.3 Simulations, Models and Heuristics;69
1.6.1.2.4;2.4 Performance Testing;70
1.6.1.3;3 Results;71
1.6.1.3.1;3.1 Comparison of NN Topologies;71
1.6.1.3.2;3.2 Comparison of NNs to Standard Classification Algorithms and Heuristics;71
1.6.1.3.3;3.3 Detailed Analysis of bold0mu mumu equation1 Agents' Performance;73
1.6.1.3.4;3.4 Subgame and Truncation Consistency ;73
1.6.1.3.5;3.5 Agent Heterogeneity;74
1.6.1.4;4 Conclusion;74
1.6.1.5;A Technical Presentation of the NN Backpropagation Algorithm;75
1.6.1.6;References;77
1.6.2;Cognitive-Costed Agent Model of the Microblogging Network;78
1.6.2.1;1 Introduction;78
1.6.2.2;2 Data Analysis;79
1.6.2.3;3 Model;82
1.6.2.3.1;3.1 Simple Reciprocal Model;82
1.6.2.3.2;3.2 Reciprocal Model with Cognitive Cost;83
1.6.2.4;4 Discussion and Conclusions;86
1.6.2.5;References;87
1.7;Part IV Complexity and Policy Analysis;88
1.7.1;Landscape Analysis of Possible Outcomes;89
1.7.1.1;1 Introduction;89
1.7.1.2;2 Landscape Analysis of Possible Outcomes;91
1.7.1.3;3 Case Example;93
1.7.1.3.1;3.1 Model;93
1.7.1.3.1.1;3.1.1 Sales Division and Salespersons;93
1.7.1.3.1.2;3.1.2 Organizational Behavior;94
1.7.1.3.1.3;3.1.3 Evaluation System;94
1.7.1.3.1.4;3.1.4 Organizational Learning;94
1.7.1.3.2;3.2 Simulation;95
1.7.1.3.2.1;3.2.1 Verification and Validation;95
1.7.1.3.2.2;3.2.2 Experimental Design;95
1.7.1.3.2.3;3.2.3 Result;96
1.7.1.4;4 Discussion;97
1.7.1.4.1;4.1 Analysis of Uncertainties;97
1.7.1.4.2;4.2 Evaluation and Scope;98
1.7.1.5;5 Summary and Future Study;99
1.7.1.6;References;99
1.7.2;The Flow of Information Through People's Network and Its Effect on Japanese Public Pension System;101
1.7.2.1;1 Introduction;101
1.7.2.2;2 Basics;102
1.7.2.2.1;2.1 The Function for Agent to Agent Interactions;102
1.7.2.2.2;2.2 Three Basic Network Models;105
1.7.2.3;3 Choice of Network Model that Fit Well in Real People's Network;105
1.7.2.4;4 Put the Information on the Agent-to-Agent Network;107
1.7.2.5;5 The Effect of Releasing the Information and Its Expansion on Japanese Public Pension System;108
1.7.2.5.1;5.1 The Execution of Simulation;108
1.7.2.5.2;5.2 Simulation Results;109
1.7.2.5.2.1;5.2.1 The Altered Distribution in Agent's Attitude Score;109
1.7.2.5.2.2;5.2.2 Investigating the Reasons for the Differences from ``Agent's Links'';109
1.7.2.5.2.3;5.2.3 Investigating the Reasons for the Differences from Agent's ``Betweeness Centrality'';114
1.7.2.5.2.4;5.2.4 The Relationships Between Information Flow and Pension Premium Fund;115
1.7.2.5.2.5;5.2.5 Additional Simulation;116
1.7.2.6;6 Conclusion and Future Works;118
1.7.2.7;References;118
1.7.3;Identification of Voting with Individual's Feet Through Agent-Based Modeling;120
1.7.3.1;1 Introduction;120
1.7.3.2;2 Related Work;121
1.7.3.2.1;2.1 Social Phenomenon of Migration;121
1.7.3.2.2;2.2 Studies About VWF;122
1.7.3.3;3 Model Description;123
1.7.3.3.1;3.1 Government Agent Model;123
1.7.3.3.1.1;3.1.1 Basic Concepts of the Model;123
1.7.3.3.1.2;3.1.2 Budget Distribution Strategies;124
1.7.3.3.2;3.2 Inhabitant Agent Model;124
1.7.3.3.3;3.3 How the Agent-Simulator Works;125
1.7.3.4;4 Experiments and Discussions;126
1.7.3.4.1;4.1 Experimental Results;127
1.7.3.4.1.1;4.1.1 Statistics of the Experiments;127
1.7.3.5;5 Proposal of Multiple-Layer Modeling;131
1.7.3.5.1;5.1 Issues of the Current VWIF Model;131
1.7.3.5.2;5.2 Framework of MLM;131
1.7.3.5.3;5.3 On Agents' Action Rules in the MLM Environment;132
1.7.3.6;6 Concluding Remarks;132
1.7.3.7;References;133
1.8;Part V Agent-Based Modeling of Good Societies;134
1.8.1;Communities, Anti-Communities, Pan-Community as Social Order;135
1.8.1.1;1 Introduction;135
1.8.1.2;2 The Friend Selection Strategies based on Attribute and Reputation in Group (FSS-ARG);136
1.8.1.3;3 Evolutionary Simulation of Peace;138
1.8.1.4;4 Emergence of Four Types of Social States;140
1.8.1.5;5 Discussions;144
1.8.1.6;References;145
1.8.2;Bayesian Analysis Method of Time Series Data in Greenhouse Gas Emissions Trading Market;146
1.8.2.1;1 Introduction;146
1.8.2.2;2 Analytical Methods of Time Series Data;147
1.8.2.2.1;2.1 Overview of DFT;147
1.8.2.2.2;2.2 BAM;148
1.8.2.2.3;2.3 Classification Method Using Distribution;149
1.8.2.3;3 Model;150
1.8.2.3.1;3.1 Model of Participant Nations in Emissions Trading;150
1.8.2.3.2;3.2 Compliance Mechanism;152
1.8.2.4;4 Experiments: Comparison of DFT and BAM;153
1.8.2.4.1;4.1 Setting;153
1.8.2.4.2;4.2 Experiment Results;154
1.8.2.5;5 Discussion;156
1.8.2.6;6 Conclusion;157
1.8.2.7;References;157
1.8.3;Large Scale Crowd Simulation of Terminal Station Area When Tokai Earthquake Advisory Information Is Announced Officially;159
1.8.3.1;1 Introduction;159
1.8.3.2;2 A Framework of the Simulation;160
1.8.3.2.1;2.1 Characteristics of the Model Employed in the Research;160
1.8.3.2.2;2.2 An Outline of the Model and Framework of the Simulation;161
1.8.3.3;3 Production Design of LSCSver.1.0;161
1.8.3.3.1;3.1 Details of the Model;161
1.8.3.3.2;3.2 Assessment of the Shortest Distance;163
1.8.3.3.3;3.3 Rules Concerning Movement of Pedestrian Agents;163
1.8.3.3.4;3.4 Rules for Route Selection by Pedestrian Agents;164
1.8.3.4;4 Simulation Analysis;165
1.8.3.4.1;4.1 Rules for Route Selection by Pedestrian Agents;165
1.8.3.4.2;4.2 Results Analysis;165
1.8.3.4.2.1;4.2.1 Measurement Data;167
1.8.3.5;5 Conclusion;171
1.8.3.6;References;171
1.9;Part VI Miscellany;173
1.9.1;Boundary Organizations: An Evaluation of Their Impact Through a Multi-Agent System;174
1.9.1.1;1 Introduction;174
1.9.1.2;2 Boundary Organizations;175
1.9.1.3;3 Methodology;177
1.9.1.4;4 Results;180
1.9.1.5;5 Conclusion;184
1.9.1.6;References;185
1.9.2;A Bibliometric Study of Agent-Based Modeling Literature on the SSCI Database;186
1.9.2.1;1 Introduction;186
1.9.2.2;2 Agent-Based Modeling Description;187
1.9.2.3;3 Overall Analysis of ABM literature;187
1.9.2.4;4 Bradford's Law and Journal Literature;191
1.9.2.5;5 Lotka's Law and Author Productivity;193
1.9.2.5.1;5.1 Distribution of Scientific Productivity of Authors with Equality of Chances of Participation ;193
1.9.2.5.2;5.2 Lotka's Law ;193
1.9.2.6;6 Conclusion;195
1.9.2.7;References;195
1.10;Author Index;196
1.11;Keyword Index;197