Ho, Teck H. and Camerer, Colin F. and Wang, Xin (2008) Individual Differences in EWA Learning with Partial Payoff Information. Economic Journal, 118 (525). pp. 37-59. ISSN 0013-0133. doi:10.1111/j.1468-0297.2007.02103.x. https://resolver.caltech.edu/CaltechAUTHORS:20110207-092141241
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Abstract
We extend experience-weighted attraction (EWA) learning to games in which only the set of possible foregone payoffs from unchosen strategies are known, and estimate parameters separately for each player to study heterogeneity. We assume players estimate unknown foregone payoffs from a strategy, by substituting the last payoff actually received from that strategy, by clairvoyantly guessing the actual foregone payoff, or by averaging the set of possible foregone payoffs conditional on the actual outcomes. All three assumptions improve predictive accuracy of EWA. Individual parameter estimates suggest that players cluster into two separate subgroups (which differ from traditional reinforcement and belief learning).
| Item Type: | Article | |||||||||
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| Additional Information: | © The Author(s). Journal compilation © 2008 Royal Economic Society. Submitted: 12 March 2005; Accepted: 16 December 2006. Article first published online: 20 Dec. 2007. | |||||||||
| Issue or Number: | 525 | |||||||||
| DOI: | 10.1111/j.1468-0297.2007.02103.x | |||||||||
| Record Number: | CaltechAUTHORS:20110207-092141241 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20110207-092141241 | |||||||||
| Official Citation: | Ho, T. H., Wang, X. and Camerer, C. F. (2008), Individual Differences in EWA Learning with Partial Payoff Information. The Economic Journal, 118: 37–59. doi: 10.1111/j.1468-0297.2007.02103.x | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 22037 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | Tony Diaz | |||||||||
| Deposited On: | 16 Sep 2011 22:48 | |||||||||
| Last Modified: | 09 Nov 2021 16:02 |
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