Learning While Searching for the Best Alternative

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dc.contributor.author ADAM, Klaus
dc.date.accessioned 2011-05-09T15:10:51Z
dc.date.available 2011-05-09T15:10:51Z
dc.date.issued 2001
dc.identifier.citation Journal of Economic Theory, 2001, 101, 1, 252-280
dc.identifier.issn 0022-0531
dc.identifier.uri http://hdl.handle.net/1814/16898
dc.description.abstract This paper delivers the solution to an optimal search problem where the searcher faces more than one search alternative and is learning about the attractiveness of the respective alternatives during the search process. The optimal sampling strategy is characterized by simple reservation prices that determine which of the search alternatives to sample and when to stop searching. The reservation price criterion is optimal for a large class of learning rules, including Bayesian, nonparametric, and ad-hoc learning rules. The considered search problem contains as special cases many earlier contributions to the search literature and thereby unifies and generalizes two directions of research search with learning from a single search alternative and search without learning from several search alternatives. (C) 2001 Academic Press.
dc.title Learning While Searching for the Best Alternative
dc.type Article
dc.neeo.contributor ADAM|Klaus|aut|
dc.identifier.volume 101
dc.identifier.startpage 252
dc.identifier.endpage 280
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dc.identifier.issue 1


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