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

[Zeitschriftenartikel]

Sgroi, Daniel; Zizzo, Daniel John

Zitationshinweis

Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):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]
Klassifikation Erhebungstechniken und Analysetechniken der Sozialwissenschaften; Wirtschaftswissenschaften
Freie Schlagwörter neural networks; normal-form games; bounded rationality
Sprache Dokument Englisch
Publikationsjahr 2008
Seitenangabe S. 27-38
Zeitschriftentitel Journal of Economic Behavior & Organization, 69 (2008) 1
DOI http://dx.doi.org/10.1016/j.jebo.2008.09.008
Status Postprint; begutachtet (peer reviewed)
Lizenz PEER Licence Agreement (applicable only to documents from PEER project)
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