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A Neural Networks approach to Minority Game

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  • Luca Grilli

    ()

  • Angelo Sfrecola

    ()

Abstract

The minority game (MG) comes from the so-called “El Farol bar” problem by W.B. Arthur. The underlying idea is competition for limited resources and it can be applied to different fields such as: stock markets, alternative roads between two locations and in general problems in which the players in the “minority” win. Players in this game use a window of the global history for making their decisions, we propose a neural networks approach with learning algorithms in order to determine players strategies. We use three different algorithms to generate the sequence of minority decisions and consider the prediction power of a neural network that uses the Hebbian algorithm. The case of sequences randomly generated is also studied.

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File URL: http://dx.doi.org/10.1007/s00521-007-0163-1
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Bibliographic Info

Paper provided by Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia in its series Quaderni DSEMS with number lg_nca_2009.

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Date of creation: Oct 2009
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Publication status: Published in Neural Computing and Applications, Vol. 18, n. 2, pp. 109-113, Feb. 2009.
Handle: RePEc:ufg:qdsems:lg_nca_2009

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Keywords: Minority game; Learning algorithms; Neural networks.;

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  1. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
  2. Massimo Bernaschi & Luca Grilli & Davide Vergni, 2002. "Statistical analysis of fixed income market," Quaderni DSEMS lg_physa_2002, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
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