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Bounded-Rational Behavior by Neural Networks in Normal Form Games

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  • Daniel John Zizzo
  • Daniel Sgroi

Abstract

This paper presents a neural network model developed to simulate the endogenous emergence of bounded-rational behavior in normal-form games. There exists an algorithm which, if learnt by a neural network, would enable it to perfectly select Nash equilibria in never before seen games. However, finding this algorithm is too complex a task for a biologically plausible network, and as such it will instead settle for converging to an approximation to Nash in a subset of games. We employ computer simulations to show that Nash equilibria are found approximately 60% of the times, and to characterize the behavioural heuristics acquired by the bounded-rational agent. Pure sum of payoffs dominance, and the best response to this strategy, get closest to predicting the networks behavior.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/2000/w30/ZizzoSgroi.pdf
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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2000-W30.

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Date of creation: 01 Mar 2001
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Handle: RePEc:oxf:wpaper:2000-w30

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Related research

Keywords: rationality; learning; neural networks; normal form games; complexity;

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Cited by:
  1. Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, Springer, vol. 62(4), pages 311-333, May.

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