Strategy Learning in 3x3 Games by Neural Networks
AbstractThis paper presents a neural network based methodology for examining the learning of game-playing rules in never-before seen games. A network is trained to pick Nash equilibria in a set of games and then released to play a larger set of new games. While faultlessly selecting Nash equilibria in never-before seen games is too complex a task for the network, Nash equilibria are chosen approximately 60% of the times. Furthermore, despite training the network to select Nash equilibria, what emerges are endogenously obtained bounded-rational rules which are closer to payoff dominance, and the best response to payoff dominance.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0207.
Date of creation: Mar 2002
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Web page: http://www.econ.cam.ac.uk/index.htm
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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-04-03 (All new papers)
- NEP-CMP-2002-04-03 (Computational Economics)
- NEP-MIC-2002-04-03 (Microeconomics)
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