Analysis of Learning Types in an Artificial Market

  • Kiyoshi Izumi
  • Tomohisa Yamashita
  • Koichi Kurumatani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3415)


In this paper, we examined the conditions under which evolutionary algorithms (EAs) are appropriate for artificial market models. We constructed three types of agents, which are different in efficiency and accuracy of learning. They were compared using acquired payoff in a minority game, a simplified model of a financial market. As a result, when the dynamics of the financial price was complex to some degree, an EA-like learning type was appropriate for the modeling of financial markets.


Price Change High Payoff Agent Type Payoff Matrix Standard Agent 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kiyoshi Izumi
    • 1
  • Tomohisa Yamashita
    • 1
  • Koichi Kurumatani
    • 1
  1. 1.ITRI, AIST & CRESTJSTTokyoJapan

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