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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- Gilboa, Itzhak, 1988. "The complexity of computing best-response automata in repeated games," Journal of Economic Theory, Elsevier, vol. 45(2), pages 342-352, August.
- Costa-Gomes, Miguel & Crawford, Vincent P & Broseta, Bruno, 2001.
"Cognition and Behavior in Normal-Form Games: An Experimental Study,"
Econometric Society, vol. 69(5), pages 1193-1235, September.
- Costa-Gomes, Miguel & Crawford, Vincent P. & Broseta, Bruno, 1998. "Cognition and Behavior in Normal-Form Games: An Experimental Study," University of California at San Diego, Economics Working Paper Series qt1vn4h7x5, Department of Economics, UC San Diego.
- Miguel Costa-Gomes & Vincent P. Crawford & Bruno Broseta, . "Cognition and Behavior in Normal-Form Games:An Experimental Study," Discussion Papers 00/45, Department of Economics, University of York.
- Broseta, Bruno & Costa-Gomes, Miguel & Crawford, Vincent P., 2000. "Cognition and Behavior in Normal-Form Games: An Experimental Study," University of California at San Diego, Economics Working Paper Series qt0fp8278k, Department of Economics, UC San Diego.
- Stahl, Dale II & Wilson, Paul W., 1994. "Experimental evidence on players' models of other players," Journal of Economic Behavior & Organization, Elsevier, vol. 25(3), pages 309-327, December.
- Dale O. Stahl & Paul W. Wilson, 2010.
"On Players' Models of Other Players: Theory and Experimental Evidence,"
Levine's Working Paper Archive
542, David K. Levine.
- Stahl Dale O. & Wilson Paul W., 1995. "On Players' Models of Other Players: Theory and Experimental Evidence," Games and Economic Behavior, Elsevier, vol. 10(1), pages 218-254, July.
- Ben-porath, Elchanan, 1990. "The complexity of computing a best response automaton in repeated games with mixed strategies," Games and Economic Behavior, Elsevier, vol. 2(1), pages 1-12, March.
- Spiliopoulos, Leonidas, 2009. "Neural networks as a learning paradigm for general normal form games," MPRA Paper 16765, University Library of Munich, Germany.
- Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
- Leonidas Spiliopoulos, 2005. "Can the human mind learn to backward induce? A neural network answer," Game Theory and Information 0505008, EconWPA.
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