Professor Jason Shachat jason.shachat@durham.ac.uk
Professor
We report results from an experiment in which humans repeatedly play one of two games against a computer program that follows either a reinforcement or an experience weighted attraction learning algorithm. Our experiment shows these learning algorithms detect exploitable opportunities more sensitively 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 does not vary significantly. These factors lead to a strong linear relationship between the humans' and algorithms' action choice proportions that is suggestive of the algorithms' best response correspondences.
Journal Article Type | Article |
---|---|
Publication Date | 2012-03 |
Deposit Date | Sep 17, 2014 |
Journal | Journal of Economic Dynamics and Control |
Print ISSN | 0165-1889 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 36 |
Issue | 3 |
Pages | 383-402 |
DOI | https://doi.org/10.1016/j.jedc.2011.09.007 |
Public URL | https://durham-repository.worktribe.com/output/1420843 |
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