Bayesian learning with multiple priors and nonvanishing ambiguity

Show simple item record Zimper, Alexander Ma, Wei 2018-01-12T07:58:08Z 2017-10
dc.description.abstract The existing models of Bayesian learning with multiple priors by Marinacci (Stat Pap 43:145–151, 2002) and by Epstein and Schneider (Rev Econ Stud 74:1275–1303, 2007) formalize the intuitive notion that ambiguity should vanish through statistical learning in an one-urn environment. Moreover, the multiple priors decision maker of these models will eventually learn the “truth.” To accommodate nonvanishing violations of Savage’s (The foundations of statistics, Wiley, New York, 1954) sure-thing principle, as reported in Nicholls et al. (J Risk Uncertain 50:97–115, 2015), we construct and analyze a model of Bayesian learning with multiple priors for which ambiguity does not necessarily vanish in an one-urn environment. Our decision maker only forms posteriors from priors that survive a prior selection rule which discriminates, with probability one, against priors whose expected Kullback–Leibler divergence from the “truth” is too far off from the minimal expected Kullback–Leibler divergence over all priors. The “stubbornness” parameter of our prior selection rule thereby governs how much ambiguity will remain in the limit of our learning model. en_ZA
dc.description.department Economics en_ZA
dc.description.embargo 2018-10-30
dc.description.librarian hj2018 en_ZA
dc.description.sponsorship ERSA (Economic Research Southern Africa) en_ZA
dc.description.uri en_ZA
dc.identifier.citation Zimper, A. & Ma, W. Bayesian learning with multiple priors and nonvanishing ambiguity. Economic Theory (2017) 64: 409-447. en_ZA
dc.identifier.issn 0938-2259 (print)
dc.identifier.issn 1432-0479 (online)
dc.identifier.other 10.1007/s00199-016-1007-y
dc.language.iso en en_ZA
dc.publisher Springer en_ZA
dc.rights © Springer-Verlag Berlin Heidelberg 2016. The original publication is available at en_ZA
dc.subject Ellsberg paradox en_ZA
dc.subject Kullback–Leibler divergence en_ZA
dc.subject Berk’s theorem en_ZA
dc.subject Misspecified priors en_ZA
dc.subject Bayesian learning en_ZA
dc.subject Ambiguity en_ZA
dc.subject Consistency en_ZA
dc.subject Subjective probability en_ZA
dc.subject Expected utility en_ZA
dc.title Bayesian learning with multiple priors and nonvanishing ambiguity en_ZA
dc.type Postprint Article en_ZA

Files in this item

This item appears in the following Collection(s)

Show simple item record