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| Author: | Camerer, C. Ho, T-H. |
| Title: | Experience-weighted attraction learning in normal form games |
| Journal: | Econometrica
1999 : JUL, VOL. 67:4, p. 827-874 |
| Index terms: | Learning Game theory Models |
| Language: | eng |
| Abstract: | 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 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 which separately discount previous attractions, and an experience weight. EWA includes reinforcement learning and weighted fictitious play (belief learning) as special cases, hybridizing their key elements. 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. Reinforcement and belief-learning special cases are generally rejected in favour of EWA. |
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