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dc.contributor.authorHu, Yingyaoen_US
dc.contributor.authorKayaba, Yutakaen_US
dc.contributor.authorShum, Matten_US
dc.date.accessioned2011-09-27T15:21:12Z-
dc.date.available2011-09-27T15:21:12Z-
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10419/49877-
dc.description.abstractHow do people learn? We assess, in a distribution-free manner, subjects' learning and choice rules in dynamic two-armed bandit (probabilistic reversal learning) experiments. To aid in identification and estimation, we use auxiliary measures of subjects' beliefs, in the form of their eye-movements during the experiment. Our estimated choice probabilities and learning rules have some distinctive features; notably that subjects tend to update in a non-smooth manner following choices made in accordance with current beliefs. Moreover, the beliefs implied by our nonparametric learning rules are closer to those from a (non-Bayesian) reinforcement learning model, than a Bayesian learning model.en_US
dc.language.isoengen_US
dc.publisherJohns Hopkins Univ., Dep. of Economics Baltimore, Md.en_US
dc.relation.ispartofseriesWorking papers // the Johns Hopkins University, Department of Economics 560en_US
dc.subject.jelD83en_US
dc.subject.jelC91en_US
dc.subject.jelC14en_US
dc.subject.ddc330en_US
dc.subject.keywordLearningen_US
dc.subject.keywordexperimentsen_US
dc.subject.keywordeye-trackingen_US
dc.subject.keywordBayesian vs. non-Bayesian learningen_US
dc.subject.keywordnonparametric estimationen_US
dc.subject.stwSch├Ątztheorieen_US
dc.subject.stwLernenen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwSimulationen_US
dc.subject.stwDynamisches Modellen_US
dc.titleNonparametric learning rules from bandit experiments: The eyes have it!en_US
dc.typeWorking Paperen_US
dc.identifier.ppn635252643en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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