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dc.contributor.authorChmura, Thorstenen_US
dc.contributor.authorGoerg, Sebastian J.en_US
dc.contributor.authorSelten, Reinharden_US
dc.date.accessioned2011-10-21en_US
dc.date.accessioned2012-04-24T11:05:36Z-
dc.date.available2012-04-24T11:05:36Z-
dc.date.issued2011en_US
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_US
dc.language.isoengen_US
dc.publisherMax Planck Inst. for Research on Collective Goods Bonnen_US
dc.relation.ispartofseriesPreprints of the Max Planck Institute for Research on Collective Goods 2011,26en_US
dc.subject.jelC72en_US
dc.subject.jelC91en_US
dc.subject.jelC92en_US
dc.subject.ddc330en_US
dc.subject.keywordlearningen_US
dc.subject.keyword2 x 2 gamesen_US
dc.subject.keywordexperimental dataen_US
dc.subject.stwSpieltheorieen_US
dc.subject.stwLernprozessen_US
dc.subject.stwTesten_US
dc.titleLearning in experimental 2 x 2 gamesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn670371890en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
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