Multi-Agent-Based Simulation VII: International Workshop, MABS 2006, Hakodate, Japan, May 8, 2006, Revised and Invited Papers (Lecture Notes in Computer Science, 4442)

✍ Scribed by Luis Antunes (editor), Keiki Takadama (editor)

• Publisher: Springer
• Year: 2007
• Tongue: English
• Leaves: 198
• Category: Library

✦ Synopsis

This book constitutes the thoroughly refereed proceedings of the 7th International Workshop on Multi-Agent-Based Simulation, MABS 2006. This was held in Hakodate, Japan, May 8, 2006 as an associated event of AAMAS 2006, the main international conference on autonomous agents and multi-agent systems. The 12 revised full papers presented together with three short papers and two invited papers were carefully reviewed and selected from 25 submissions during two rounds of reviewing.

✦ Table of Contents

Title Page
Preface
Organization
Table of Contents
Exploring the Vast Parameter Space of Multi-Agent Based Simulation
Introduction
Coping with the Huge Parameter Spaces
Principles of Inverse Simulation
Principles of Genetics-Based Validation
How Inverse Simulation and Genetics-Based Validation Work
Example 1: Social Interaction Analysis [8], [9]
Example 2: Model of Competing Firms in Marketing [12]
Example 3: Investors in Behavioral Finance [10], [11]
Concluding Remarks
References
Applications of Agent Based Simulation
Introduction
Evaluation Framework
Problem Description
Modeling Approach
Implementation Approach
Results
Results
Analysis
Problem Description
Modeling Approach
Implementation Approach
Results
Limitations of the Study
Conclusions
References
Analyzing Dynamics of Peer-to-Peer Communication - From Questionnaire Surveys to Agent-Based Simulation
Introduction
Related Work and Research Objectives
Model Description
Basic Concepts of the Artificial World
Data Collection for Model Generation
Simulation System Implementation
Initialization Phase
Execution Phase
Experiment
Experimental Set Up
Results
Discussion
Conclusion
References
Modeling Human Education Data: From Equation-Based Modeling to Agent-Based Modeling
Introduction
Agent-Based Modeling
A Model of Human Capital
Agent-Based Simulation
Experiments
Verification
Identifying New Features
Summary
Contrasting a System Dynamics Model and an Agent-Based Model of Food Web Evolution
Introduction
The System Dynamics Model
Explicit Assumptions in this Model
The Agent-Based Model
Preliminary Results
Discussion
Future Development
Conclusions
Roost Size for Multilevel Selection of Altruism Among Vampire Bats
The Sociobiological Debate
Altruism and the $Roosting Effect$
The Simulation
Simulation Details
Implementation
Evidence from Simulations: Resistance to Mutation and Ideal Roost Size
Hypotheses
Findings
Conclusions and Discussion
Tactical Exploration of Tax Compliance Decisions in Multi-agent Based Simulation
Introduction
Context of Research
The e*plore Methodology
A Structure of Models to Explore the Tax Compliance Problem
$Ec_0$: Modelling the Standard Theory
Extending $Ec_0$ with History
Stubbornness and Imitation
I Fought the Law and the Law Won
Experimental Environment
Conclusions
Learning to Use a Perishable Good as Money
Introduction
Model
Simulation Design
Model 1. Theoretical Model
Model 2. Belief Learning with Full Information
Model 3. Belief Learning with Partial Information
Model 4. Reinforcement Learning
Other Simulation Settings
Results
Conclusions
Deriving the Steady State Distribution of Equilibrium A
A Holonic Approach to Model and Deploy Large Scale Simulations
Introduction
Holonic Modelling of Large Scale Simulations
Holonic Framework
Holarchy Example
PSA Simulation Model
Modules for Large Simulations
MadKit Principles
Transparent Connection of Kernels
Distribution of a Simulation
Simulation
Related Works
Conclusion
Concurrent Modeling of Alternative Worlds with Polyagents
Introduction
The Challenge of Modeling Multi-agent Interactions
The Polyagent Modeling Construct
Comparison with Other Paradigms
Examples of Polyagents
Factory Scheduling
Path Planning for Robotic Vehicles
Characterizing and Predicting Agent Behavior
Discussion
Conclusion
References
Integrating Learning and Inference in Multi-agent Systems Using Cognitive Context
About Context
The Research Context
Context in Reasoning
Context in Learning
Context in Human Cognition
Combining Context-Dependent Learning and Reasoning
A Demonstration Model
The Environment
General Structure of Context Agent
The Context Identification System (CIS)
The Context-Dependent Memory (CDM)
The Local Learning Algorithm (LL)
The Inference System (IS)
Preliminary Results
Conclusion
Can Agents Acquire Human-Like Behaviors in a Sequential Bargaining Game? – Comparison of Roth’s and Q-Learning Agents –
Introduction
Bargaining Game
Modeling Agents
Agent Architecture
An Example of a Negotiation Process
Simulation
Simulation Cases
Evaluation Criteria and Parameter Setting
Simulation Results
Discussion
Subject Experiment Result
Roth's Learning Agents
Q-Learning Agents
Validity of Simulation Results and Design Guideline of Agents
Conclusions
Quantifying Degrees of Dependence in Social Dependence Relations
Introduction
Dependence Relations and Dependence Situations
DS-Graphs: Graphs for Dependence Situations
A Notation for DS-Graphs
Calculating Objective Degrees of Dependence in DS-Graphs
Additional Concepts
Degrees of Transitive Dependences
Degrees of Bilateral Dependence
Negotiation Power of Agents in Societies
Refining Objective Degrees of Dependence with Subjective Estimates
Sample Calculations of Degrees of Dependence
Degrees of Dependence and Negotiation Powers
Subjective Degrees of Dependence and Social Reasoning
Conclusion
Author Index