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dc.contributor.authorChmura, Thorstenen
dc.contributor.authorGoerg, Sebastian J.en
dc.contributor.authorSelten, Reinharden
dc.date.accessioned2011-10-21-
dc.date.accessioned2012-04-24T11:05:36Z-
dc.date.available2012-04-24T11:05:36Z-
dc.date.issued2011-
dc.identifier.urihttp://hdl.handle.net/10419/57509-
dc.description.abstractIn 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.en
dc.language.isoengen
dc.publisher|aMax Planck Institute for Research on Collective Goods |cBonnen
dc.relation.ispartofseries|aPreprints of the Max Planck Institute for Research on Collective Goods |x2011,26en
dc.subject.jelC72en
dc.subject.jelC91en
dc.subject.jelC92en
dc.subject.ddc330en
dc.subject.keywordlearningen
dc.subject.keyword2 x 2 gamesen
dc.subject.keywordexperimental dataen
dc.subject.stwSpieltheorieen
dc.subject.stwLernprozessen
dc.subject.stwTesten
dc.titleLearning in experimental 2 x 2 games-
dc.typeWorking Paperen
dc.identifier.ppn670371890en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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