Bounded-Rational Behavior by Neural Networks in Normal Form Games
AbstractThis 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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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2000-W30.
Date of creation: 01 Mar 2001
Date of revision:
rationality; learning; neural networks; normal form games; complexity;
Find related papers by JEL classification:
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- D00 - Microeconomics - - General - - - General
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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- Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
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