Advertisement

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)

Abstract

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.

Keywords

Price Change High Payoff Agent Type Payoff Matrix Standard Agent 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Izumi, K., Ueda, K.: Phase transition in a foreign exchange market: Analysis based on an artificial market approach. IEEE Transactions on Evolutionary Computation 5, 456–470 (2001)CrossRefGoogle Scholar
  2. 2.
    Chen, S.-H., Yeh, C.-H.: Evolving traders and the buisiness school with genetic programming: a new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25, 363–393 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Izumi, K., Nakamura, S., Ueda, K.: Development of an artificial market model based on a field study. Information Science (in press)Google Scholar
  4. 4.
    Arthur, W.B.: Inductive reasoning and bounded rationality (the el farol problem). American Economic Review 84, 406 (1994)Google Scholar
  5. 5.
    Minority Game’s web page, http://www.unifr.ch/econophysics/
  6. 6.
    Zhang, Y.-C.: Modeling market mechanism with evolutionary games. Europhys. News 29, 51–54 (1998)Google Scholar
  7. 7.
    Challet, D., Zhang, Y.-C.: Emergence of cooperation and organization in an evolutionary game. Physica A 246, 407–418 (1997)CrossRefGoogle Scholar
  8. 8.
    Marsili, M.: Market mechanism and expectations in minority and majority games. Physica A 299, 93–103 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Cavagna, A.: Irrelevance of memory in the minority game. Physical Review E 59, 3783–3786 (1999)CrossRefGoogle Scholar
  10. 10.
    Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 493–500 (1999)CrossRefGoogle Scholar
  11. 11.
    Joshi, S., Parket, J., Bedau, M.A.: Technical trading creates a prisoner’s dilemma: Results from an agent-based model. In: Abu-Mostafa, Y.S., LeBaron, B., Lo, A.W., Weigend, A.S. (eds.) Computational Finance 1999, pp. 465–479. MIT Press, Cambridge (2000)Google Scholar
  12. 12.
    Izumi, K.: Complexity of agents and complexity of markets. In: Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Osawa, Y., Washio, T. (eds.) New Frontiers in Artificial Intelligence, pp. 110–120. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    LeBaron, B.: Evolution and time horizons in an agent based stock market. Macroeconomic Dynamics 5, 225–254 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Chen, S.H., Liao, C.C.: Agent-based computational modeling of the stock price-volume relation. Information Sciences (in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

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

Personalised recommendations