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Experience-weighted Attraction Learning in Normal Form Games

Camerer, Colin F. and Ho, Teck Hua (1999) Experience-weighted Attraction Learning in Normal Form Games. Econometrica, 67 (4). pp. 827-874. ISSN 0012-9682. doi:10.1111/1468-0262.00054.

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In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit). A key feature is a parameter δ that weights the strength of hypothetical reinforcement of strategies that were not chosen according to the payoff they would have yielded, relative to reinforcement of chosen strategies according to received payoffs. The other key features are two discount rates, φ and ρ, which separately discount previous attractions, and an experience weight. EWA includes reinforcement learning and weighted fictitious play (belief learning) as special cases, and hybridizes their key elements. When δ= 0 and ρ= 0, cumulative choice reinforcement results. When δ= 1 and ρ=φ, levels of reinforcement of strategies are exactly the same as expected payoffs given weighted fictitious play beliefs. Using three sets of experimental data, parameter estimates of the model were calibrated on part of the data and used to predict a holdout sample. Estimates of δ are generally around .50, φ around .8 − 1, and ρ varies from 0 to φ. Reinforcement and belief-learning special cases are generally rejected in favor of EWA, though belief models do better in some constant-sum games. EWA is able to combine the best features of previous approaches, allowing attractions to begin and grow flexibly as choice reinforcement does, but reinforcing unchosen strategies substantially as belief-based models implicitly do.

Item Type:Article
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URLURL TypeDescription ItemWorking Paper
Camerer, Colin F.0000-0003-4049-1871
Ho, Teck Hua0000-0001-5210-4977
Additional Information:© 1999 Econometric Society. Manuscript received January, 1997; final revision received August, 1998. Article first published online: 9 Dec. 2003.
Subject Keywords:Learning, behavioral game theory, reinforcement learning, fictitious play
Issue or Number:4
Record Number:CaltechAUTHORS:20110210-093101968
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Official Citation:Camerer, C. and Hua Ho, T. (1999), Experience-weighted Attraction Learning in Normal Form Games. Econometrica, 67: 827–874. doi: 10.1111/1468-0262.00054
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:22107
Deposited By: Tony Diaz
Deposited On:11 Feb 2011 22:23
Last Modified:09 Nov 2021 16:03

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