2004, 2002, English, Article, Working paper edition: Strategy Learning in 3x3 Games by Neural Networks Sgroi, Daniel; Zizzo, D. J.

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Bookmark: http://trove.nla.gov.au/version/632
Physical Description
  • 331602 bytes, application/​pdf
  • Working Paper
Published
  • 2004-06-16T16:05:06Z
  • 2002-03
Language
  • English

Edition details

Title
  • Strategy Learning in 3x3 Games by Neural Networks
Author
  • Sgroi, Daniel
  • Zizzo, D. J.
Published
  • 2004-06-16T16:05:06Z
  • 2002-03
Physical Description
  • 331602 bytes, application/​pdf
  • Working Paper
Subjects
Notes
  • This 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.
Language
  • English
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