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Learning in experimental 2 x 2 games

  • Thorsten Chmura

    (Department of Economics, Ludwig-Maximilians-Universitat Munich)

  • Sebastian Goerg

    ()

    (Max Planck Institute for Research on Collective Goods, Bonn)

  • Reinhard Selten

    (cLaboratory for Experimental Economics (BonnEconLab), University of Bonn)

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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Paper provided by Max Planck Institute for Research on Collective Goods in its series Working Paper Series of the Max Planck Institute for Research on Collective Goods with number 2011_26.

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Date of creation: Oct 2011
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Handle: RePEc:mpg:wpaper:2011_26
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