Neural Computing and Applications

, Volume 18, Issue 2, pp 109–113 | Cite as

A neural networks approach to minority game

Original Article


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.


Minority game Learning algorithms Neural networks 


  1. 1.
    Arthur WB (1994) Inductive reasoning and bounded rationality. In: Krugman (ed) Am Econ Assoc Papers and Proc, pp 88–406Google Scholar
  2. 2.
    Bernaschi M, Grilli L, Vergni D (2002) Statistical analysis of fixed income market. Physica A: Stat Mech appl 308(1–4):381–390MATHCrossRefGoogle Scholar
  3. 3.
    Cavagna A (1999) Irrelevance of memory in the minority game. Phys Rev E 59:R3783CrossRefGoogle Scholar
  4. 4.
    Grilli L (2004) Long-term fixed income market structure. Physica A: Stat Mech appl 332:441–447CrossRefGoogle Scholar
  5. 5.
    Hart M, Jefferies P, Johnson NF, Hui PM (2000) Crowd-anticrowd model of the minority game, cond-mat/0003486Google Scholar
  6. 6.
    Hertz J, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley, Redwood CityGoogle Scholar
  7. 7.
    Kinzel W, Kanter I (2000) Dynamics of interacting neural networks. J Phys A 33:L141–L147MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Metzler R (2002) Neural Networks, Game Theory and Time Series Generation. Dissertation, cond-mat/0212486Google Scholar
  9. 9.
    Metzler R, Kinzel W, Ein-Dor L, Kanter I (2001) Generation of unpredictable time series by a neural nertwork. Phys Rev E 63(5):056126CrossRefGoogle Scholar
  10. 10.
    Minsky M, Papert S (1969) Perceptrons: an introduction to computational geometry. MIT Press, CambridgeMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Dipartimento di Scienze Economiche, Matematiche e StatisticheUniversità degli Studi di FoggiaFoggiaItaly

Personalised recommendations