A Neural Networks approach to Minority Game
AbstractThe 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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Bibliographic InfoPaper provided by Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia in its series Quaderni DSEMS with number lg_nca_2009.
Date of creation: Oct 2009
Date of revision:
Publication status: Published in Neural Computing and Applications, Vol. 18, n. 2, pp. 109-113, Feb. 2009.
Minority game; Learning algorithms; Neural networks.;
Other versions of this item:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
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- Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
- Bernaschi, Massimo & Grilli, Luca & Vergni, Davide, 2002.
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Physica A: Statistical Mechanics and its Applications,
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