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  Learning in experimental 2 x 2 games

Chmura, T., Goerg, S. J., & Selten, R. (2011). Learning in experimental 2 x 2 games.

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 Creators:
Chmura, Thorsten, Author
Goerg, Sebastian J.1, Author              
Selten, Reinhard, Author
Affiliations:
1Max Planck Institute for Research on Collective Goods, Max Planck Society, ou_2173688              

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 Abstract: In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from experimental data. We test whether the models are capable of replicating the aggregate distribution of behavior, as well as correctly predicting individuals' round-by-round behavior. Our results are two-fold: while the simulations with impulse-matching and action-sampling learning successfully replicate the experimental data on the aggregate level, individual behavior is best described by self-tuning EWA. Nevertheless, impulse-matching learning has the second highest score for the individual data. In addition, only self-tuning EWA and impulse-matching learning lead to better round-by-round predictions than the aggregate frequencies, which means they adjust their predictions correctly over time.

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 Dates: 2011
 Publication Status: Published in print
 Pages: -
 Publishing info: Bonn : Max Planck Institute for Research on Collective Goods
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: 2011/26
 Degree: -

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