Behavior learning in minority games

Authors:
Guanyi Li;Ying Ma;Yingsai Dong;Zengchang Qin
Affiliations:
Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Venue:
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
Year:
2009

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Abstract

The Minority Game (MG) is a simple model for the collective behavior of agents in an idealized situation where they have to compete through adaptation for a finite resource. It has been regarded as an interesting complex dynamical disordered system from a statistical mechanics point of view. In this paper we have investigated the problem of learning the agent behaviors in the minority game. We assume the existence of one "intelligent agent" who can learn from other agent behaviors. We consider two scenarios in this research: (1) Given an environment with complete information, i.e., all records of agents' choices are known to public. The intelligent agent can use a Decision Tree to learn the patterns of other agents and make predictions. (2) If we only know the data of collective behaviors, we assume the data are generated from combining the behaviors of variant groups of agents. The intelligent agent can use a Genetic Algorithm to optimize these group parameters in order to get the best guess of the original system. The experimental results show that, in this configuration of MG in both environments with complete information and incomplete information, the intelligent agent can learn from history data and predict which side is the minority.