English, Article, Journal or magazine article edition: Learning about Learning in Games through Experimental Control of Strategic Interdependence Jason Shachat; J. Todd Swarthout

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Bookmark: http://trove.nla.gov.au/version/195195
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Learning about Learning in Games through Experimental Control of Strategic Interdependence
Author
  • Jason Shachat
  • J. Todd Swarthout
Physical Description
  • preprint
Notes
  • We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either Roth and Erev's reinforcement learning algorithm or Camerer and Ho's EWA algorithm. The human/​algorithm interaction provides results that can't be obtained from the analysis of pure human interactions or model simulations. The learning algorithms are more sensitive than humans in calculating exploitable opponent play. Learning algorithms respond to these calculated opportunities systematically; however, the magnitude of these responses are too weak to improve the algorithm's payoffs. Human play against various decision maker types does not significantly vary. These results demonstrate that humans and currently proposed models of their behavior differ in that humans do not adjust payoff assessments by smooth transition functions and that when humans detect exploitable play they are more likely to choose the best response to this belief.
  • RePEc:wpa:wuwpex:0310003
  • We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either a reinforcement learning or an Experience Weighted Attraction algorithm. Our experiments show these learning algorithms more sensitively detect exploitable opportunities than humans. Also, learning algorithms respond to detected payoff increasing opportunities systematically; however, the responses are too weak to improve the algorithms payoffs. Human play against various decision maker types doesn't significantly vary. These factors lead to a strong linear relationship between the humans and algorithms action choice proportions that is suggestive of the algorithm's best response correspondence.
  • RePEc:exc:wpaper:2006-17
Language
  • English
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