The inability of the Bayesian model to accomodate Ellsberg-type behavior is well known. This paper focuses on another limitation of the Bayesian model, specific to a dynamic setting, namely the inability to permit a distinction between experiments that are identical and those that are only indistinguishable. It is shown that such a distinction is afforded by recursive multiple-priors utility. Two related technical contributions are the proff of a strong LLN for recursive multiple-priors utility and the extension to sets of priors of the notion of regularity of a probability measure.
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Paper provided by University of Rochester - Center for Economic Research (RCER) in its series RCER Working Papers with number
496.
Length: 23 pages Date of creation: Oct 2002 Date of revision: Publication status: forthcoming in JET Handle: RePEc:roc:rocher:496
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Find related papers by JEL classification: D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information D9 - Microeconomics - - Intertemporal Choice and Growth
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Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
Larry Epstein & Martin Schneider, 2006.
"Learning Under Ambiguity,"
RCER Working Papers
527, University of Rochester - Center for Economic Research (RCER).
[Downloadable!]
Other versions:
Larry Epstein & Martin Schneider, 2002.
"Learning Under Ambiguity,"
RCER Working Papers
497, University of Rochester - Center for Economic Research (RCER), revised Mar 2005.
[Downloadable!]