Title: Behavioural analysis in network formation using agent-based simulation systems

Authors: Tomohiro Hayashida, Ichiro Nishizaki, Hideki Katagiri, Rika Kambara

Addresses: Graduate School of Engineering, Department of Artificial Complex Systems Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan. ' Graduate School of Engineering, Department of Artificial Complex Systems Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan. ' Graduate School of Engineering, Department of Artificial Complex Systems Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan. ' Graduate School of Engineering, Department of Artificial Complex Systems Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

Abstract: In the previous study on models of network formation and laboratory experiments, Berninghaus, Ehrhart, Ott and Vogt in 2007 showed that there are some differences between the real human behaviour in the laboratory experiments and the theoretical prediction based on the concept of Nash equilibrium; although a peripheral sponsored star network is theoretically proved to be strict Nash equilibrium, the experimental results showed that there were some players who deviated from the equilibrium strategies. As an efficient tool for explaining such differences, this paper provides a simulation system using artificial adaptive agents for behavioural analysis of the human subjects. The decision making and the learning mechanism of the agents are constructed on the basis of neural networks and genetic algorithms. Through the simulation analysis using the constructed system, it is shown that the differences between the experimental results and the theoretical prediction are explainable in terms of the long-term view and the adaptivity of human behaviour.

Keywords: network formation; agent-based systems; simulation analysis; behavioural analysis; strict Nash equilibrium; artificial adaptive agents; decision making; learning mechanisms; neural networks; genetic algorithms; human behaviour; multi-agent systems.

DOI: 10.1504/IJKESDP.2011.039876

International Journal of Knowledge Engineering and Soft Data Paradigms, 2011 Vol.3 No.1, pp.22 - 39

Published online: 22 Apr 2011 *

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