Learning in experimental 2 x 2 games

Thorsten Chmura (), Sebastian Goerg and Reinhard Selten

No 2011_26, Discussion Paper Series of the Max Planck Institute for Research on Collective Goods from Max Planck Institute for Research on Collective Goods

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.

Keywords: learning; 2 x 2 games; Experimental data (search for similar items in EconPapers)
JEL-codes: C72 C91 C92 (search for similar items in EconPapers)
Date: 2011-10
New Economics Papers: this item is included in nep-cbe, nep-evo, nep-exp, nep-gth and nep-mic
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Related works:
Journal Article: Learning in experimental 2×2 games (2012)
Working Paper: Learning in experimental 2×2 games (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:mpg:wpaper:2011_26

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