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Learning to play 3x3 games : neural networks as bounded-rational players

[journal article]

Sgroi, Daniel; Zizzo, Daniel John

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Please use the following Persistent Identifier (PID) to cite this document:http://nbn-resolving.de/urn:nbn:de:0168-ssoar-281143

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Abstract "We present a neural network methodology for learning game-playing rules in general. Existing research suggests learning to find a Nash equilibrium in a new game is too difficult a task for a neural network, but says little about what it will do instead. We observe that a neural network trained to find Nash equilibria in a known subset of games will use self-taught rules developed endogenously when facing new games. These rules are close to payoff dominance and its best response. Our findings are consistent with existing experimental results, both in terms of subject's methodology and success rates." [author's abstract]
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods; Economics
Free Keywords neural networks; normal-form games; bounded rationality
Document language English
Publication Year 2008
Page/Pages p. 27-38
Journal Journal of Economic Behavior & Organization, 69 (2008) 1
DOI http://dx.doi.org/10.1016/j.jebo.2008.09.008
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)