Heterogeneous Populations of Learning Agents in the Minority Game

  • David Catteeuw
  • Bernard Manderick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7113)

Abstract

We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among learning agents can improve when agents use different learning parameters or even evolve their learning parameters. Better coordination leads to less resources being wasted and agents achieving higher individual performance. We also show that learning algorithms which achieve good results when all agents use that same algorithm, may be outcompeted when directly confronting other learning algorithms in the Minority Game.

Keywords

Multiagent System Individual Welfare Evolutionary Game Theory Congestion Game Homogeneous Setting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David Catteeuw
    • 1
  • Bernard Manderick
    • 1
  1. 1.Computational Modeling LabVrije Universiteit BrusselBrusselsBelgium

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