Agent-Based Approaches in Economic and Social Complex Systems VII Post-Proceedings of The AESCS International Workshop 2012

Agent-based modelling/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 activiti...

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Main Authors Murata, Tadahiko, Terano, Takao, Takahashi, Shingo
Format eBook
LanguageEnglish
Published Tokyo Springer Nature 2013
Springer Japan
Springer
Edition1
SeriesAgent-Based Social Systems
Subjects
Business and Management, general
Computer Science
Computer Science, general
Computer simulation
Congresses
Data processing Computer science
Economics
Economics, general
Game theory
Mathematical models
Social sciences
Social Sciences, general
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Abstract Agent-based modelling/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 Seventh International Workshop on Agent-Based Approaches in Economic and Social Complex Systems held in Osaka, Japan, in 2012.
AbstractList Agent-based modelling/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 Seventh International Workshop on Agent-Based Approaches in Economic and Social Complex Systems held in Osaka, Japan, in 2012.
Author Takahashi, Shingo
Terano, Takao
Murata, Tadahiko
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Murata, Tadahiko
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Snippet Agent-based modelling/simulation is an emergent approach to the analysis of social and economic systems. It provides a bottom-up experimental method to be...
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SubjectTerms Business and Management, general
Computer Science
Computer Science, general
Computer simulation
Congresses
Data processing Computer science
Economics
Economics, general
Game theory
Mathematical models
Social sciences
Social Sciences, general
Subtitle Post-Proceedings of The AESCS International Workshop 2012
TableOfContents 5.1 The Effectiveness of School Closure -- 5.2 The Effectiveness of Vaccination -- 6 Conclusion -- References -- Influence of Government Expenditure Policies and Tax Rate on GDP in an Agent-Based Artificial Economic System -- 1 Introduction -- 2 Simulation Model -- 2.1 Consumer Agent -- 2.2 Producer Agent -- 2.2.1 Retailer and Wholesaler -- 2.2.2 Equipment-Maker -- 2.3 Bank Agent -- 2.4 Government Agent -- 3 Simulation Conditions -- 4 Simulation Results -- 4.1 Emergent Properties in the Present Artificial Economic Systems -- 4.2 Influence of Government Expenditure Policies on the Macroeconomic Behavior of the Artificial Economic System -- 4.3 Influence of Tax Rate on GDP -- 4.4 Influence of Marginal Propensity to Consume on GDP -- 5 Discussion -- 6 Conclusion -- References -- The Role of Social Network in Migration and Economic Aggregation Through a Brownian Agent Model -- 1 Introduction -- 2 Model Description Through the ODD Protocol -- 2.1 Purpose -- 2.2 Entities, State Variables, and Scales -- 2.3 Process Overview and Scheduling -- 2.4 Design Concepts -- 2.4.1 Basic Principles -- 2.4.2 Emergence -- 2.4.3 Adaptation -- 2.4.4 Objectives -- 2.4.5 Sensing -- 2.4.6 Interaction -- 2.4.7 Stochasticity -- 2.4.8 Collectives -- 2.4.9 Observation -- 2.5 Initialization -- 2.6 Submodels -- 2.7 Determination of Functions -- 3 Computer Simulations of Brownian Agent Migration -- 3.1 Parameter Setup -- 3.2 Simulation Experiments and Results -- 3.2.1 Simulation Experiment 1: The Effect of Social Network on Agent Migration -- 3.2.2 Simulation Experiment 2: The Effect of Social Network on Employment Rate -- 3.2.3 Simulation Experiment 3: The Effect of Social Network on Economic Aggregation -- 4 Discussion -- 5 Conclusion -- References -- Agent-Based Simulation for Pension System in Japan -- 1 Introduction -- 2 Outline of Current Pension System -- 3 Simulation Model
3.1 Learning Classifier System
3.3.3 Differences and Similarities of Cases of 9 and 12 Agents -- 4 Conclusion -- References -- Firms' Interaction in a Scale-Free Trade Network and PricesDynamics -- 1 Introduction -- 2 Background -- 2.1 Empirical Findings About Price Adjustment -- 2.2 Models of Price Adjustment -- 2.3 Firms' Interaction and Network Structure -- 3 Model -- 3.1 Economic Model -- 3.1.1 Household Behavior -- 3.1.2 Firms' Behavior -- 3.2 Expectation Modeling -- 3.3 Price-Updating Modeling -- 4 Model Solution and Implementation -- 4.1 Optimal Pricing -- 4.2 Parameter Settings -- 4.3 Network Structure -- 4.4 Initialization -- 5 Simulation and Results -- 6 Discussion -- 7 Conclusions -- References -- Analysis and Modeling of Customer-Perceived Value of MedicalInsurance Products -- 1 Introduction -- 2 Analysis of Survey Data Using Bayesian Networks -- 2.1 Consumer Surveys -- 2.2 Method of Analysis -- 2.2.1 Analysis Data -- 2.2.2 Analysis Method -- 2.3 Results and Discussion -- 2.3.1 Graph Structure -- 2.3.2 Findings from Probabilistic Inference -- 2.3.3 Discussion -- 3 Multi-agent Simulation for Insurance Product Selection -- 3.1 Overview of the Simulation Model -- 3.2 Multi-agent Simulation Model -- 3.3 Simulation Results and Discussion -- 3.3.1 Findings from Simulations -- 3.3.2 Discussion -- 4 Related Research -- 5 Conclusion -- References -- Part II Applications of Agent-Based Medeling -- The Effects of Vaccination Strategies Against Smallpox Bioterrorism with Agent-Based Simulation -- 1 Introduction -- 2 Methods -- 2.1 Agent-Based Model -- 2.2 SOARS -- 3 Modeling and Formulation -- 3.1 States of Agents (Agent Disease Stage) -- 3.2 Contamination and Infection Model -- 3.2.1 Formulation -- 3.3 Simulated Social Structures -- 3.4 Human Activity Model -- 3.5 Intervention -- 4 Simulation Results -- 4.1 Conditions -- 4.2 Illustrative Scenarios -- 5 Discussion
Intro -- Preface -- Committee Members of AESCS2012 -- Contents -- Part I Fundamentals of Agent-Based Modeling -- Agent-Based Modeling and Simulation Validation by ScenarioAnalysis -- 1 Introduction -- 2 Agent-Based Modeling and Simulation -- 2.1 Agent-Based Modeling -- 2.2 Agent-Based Simulation -- 3 Scenario Analysis -- 3.1 Landscape Analysis -- 3.2 Micro-Dynamics Analysis -- 4 Validating ABMS -- 4.1 Brief Review of Related Studies -- 4.2 Arbitrariness of Presenting Simulation Results -- 5 Discussion -- 5.1 Our Idea for Validation -- 5.2 ABMS Validation by Stakeholders -- 6 Summary and Future Work -- References -- Aspiration-Based Learning to Balance Exploration and Exploitation in Organizational Learning -- 1 Introduction -- 2 Proposed Model -- 2.1 Basic Elements -- 2.2 Payoff -- 2.3 Experimentation Procedure -- 2.4 Updating Probability of Experimentation -- 2.5 Updating the Organizational Code -- 2.6 Updating the Individual's Beliefs -- 2.7 Updating the Aspiration Level -- 2.8 Environmental Turbulence and Turnover -- 2.9 Simulation Cycle -- 3 Simulation and Results -- 3.1 First Scenario -- 3.2 Second Scenario -- 4 Discussion and Conclusion -- References -- Agent-Based Simulation of Diversity and Organizational Performance -- 1 Introduction -- 2 The Model -- 2.1 Assumptions -- 2.2 Outline -- 2.3 Member Agents -- 2.3.1 Objects -- 2.3.2 Learning -- 2.4 Organization -- 2.5 Flow of the Model -- 3 Computational Experiments -- 3.1 Setup -- 3.2 Results -- 3.2.1 Organizational Utility -- 3.2.2 Individual Utility -- 3.2.3 Distance of Behavior -- 3.3 Discussion -- 4 Concluding Remarks -- References -- Configuring Agents' Attributes with Simulated Annealing -- 1 Introduction -- 2 Population Problem of an Aging Society -- 3 IBMs of Population Dynamics and Their Problems -- 3.1 Macroscopic Model of Population Dynamics -- 3.2 IBMs of Population Dynamics
3.1 Demographic Change -- 3.2 Prediction of the Number of Covered Persons -- 3.3 Economic Assumptions -- 3.4 Individual's Wages -- 4 Simulation Results -- 4.1 Projection of Demographic Changes -- 4.2 Financial Projection -- 4.3 Simulation Results for Individuals -- 5 Conclusion -- References -- The ASSA Project: An Intelligent Agent Simulationof Shop-Around Behavior -- 1 Introduction -- 2 ASSA (Agent Simulation of Shop-Around) Project -- 2.1 Decomposition of Downtown Visitor's Shop-Around Behavior -- 2.2 Features of Three Versions of ASSA -- 2.2.1 Features of ASSAver.1 and ver.2 -- 2.2.2 Features of ASSAver.3 -- 2.2.3 Detour at the Plan-Making Action -- 3 Evaluation Framework and Performance Measurements -- 3.1 Evaluation Focus -- 3.1.1 General Evaluation -- 3.1.2 Individual Function Evaluation -- 3.2 Analysis of Similarity in Visit Sequences -- 3.3 Redundancy Analysis of Walk Length -- 3.3.1 Level 1 Indicator -- 3.3.2 Level 2 Indicator -- 3.3.3 Level 3 Indicator -- 3.4 Evaluation of Simulation Performance -- 3.5 Overall Performance of ASSA -- 3.5.1 Osu Simulation Case -- 3.5.2 Kanayama Simulation Case -- 3.6 Individual Function Evaluation: Kanayama Simulation Case -- 3.7 Redundancy Analysis: Kanayama Simulation Case -- 4 Conclusion -- References -- Extension of the Iterative Proportional Fitting Procedureand Its Evaluation Using Agent-Based Models -- 1 Introduction -- 2 Implementation of the Classical IPFP on Agent-Based Models -- 3 Extension to the Cases in Which the Sums of the Row Marginals and Column Marginals Differ -- 4 Extension to ρ-Dimensional Cases -- 5 Evaluation of the Proposed Method with a Numerical Example Generated by ABM -- 6 Conclusion -- References -- An On-the-Fly Learning Framework for a Fluctuating Number of Agents in a Multi-Agent System -- 1 Introduction -- 2 Research Problem -- 3 Proposed Method
3.3 Related Study -- 4 Determination of Agents' Attribute Value with Constrained Optimization -- 4.1 Setup Constraint Conditions -- 4.2 Random Sampling with Simulated Annealing -- 4.3 Ensemble Simulation -- 5 Individual-Based Simulation Model of Population Dynamics -- 5.1 Outline -- 5.2 Initialization of Population -- 5.3 Marriage Event -- 5.3.1 Modeling of a Marriage with Constrained Optimization -- 5.3.2 Performance Function -- 5.4 Divorce Event -- 5.4.1 Modeling of a Divorce as a Constrained Optimization -- 5.4.2 Performance Function -- 5.5 Birth Event -- 5.6 Death Event -- 5.7 Migration Event -- 6 Experiment -- 7 Conclusion -- References -- Money Emergence on a Network Topology -- 1 Introduction -- 2 Related Literature -- 3 The Model -- 4 Replication -- 5 About Topology -- 5.1 Star Network -- 5.2 Random Network -- 5.3 Preferential Attachment Network -- 6 Conclusions -- References -- The Agent-Based Diffusion Model: Simulating Informative and Normative Effects on Consumer Networks -- 1 Introduction -- 2 Previous Research -- 3 Model Proposal -- 3.1 Decision-Making Model That Considers the Informative Effect and Normative Effect -- 3.2 Network of Interactions Among Consumers -- 4 Simulation -- 4.1 Influences of Informative Effect and Normative Effect -- 4.2 Diffusion Process -- 5 Discussion -- 6 Conclusion -- References -- Agent-Based Simulation Using a Model of Network Formation -- 1 Introduction -- 2 Model -- 2.1 The Symmetric Connections Model -- 2.2 Dynamic Network Formation Process and Simulation Algorithm -- 3 Results of the Simulation -- 3.1 Relation Between Link Cost and the Number of Agents -- 3.2 An Analysis of the Correlation Between Links and Utilities -- 3.3 A Comparison of Cases of 9 and 12 Agents -- 3.3.1 Analysis of Cases of Nine Agents -- 3.3.2 Analysis of Cases of 12 Agents
Title Agent-Based Approaches in Economic and Social Complex Systems VII
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