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Bayesianism without Learning

Dov Samet ()

Game Theory and Information from University Library of Munich, Germany

Abstract: According to the standard definition, a Bayesian agent is one who forms his posterior belief by conditioning his prior belief on what he has learned, that is, on facts of which he has become certain. Here it is shown that Bayesianism can be described without assuming that the agent acquires any certain information; an agent is Bayesian if his prior, when conditioned on his posterior belief, agrees with the latter. This condition is shown to characterize Bayesian models.

Keywords: Bayesian updating; prior and posterior (search for similar items in EconPapers)
JEL-codes: C72 D80 D83 (search for similar items in EconPapers)
Date: 1999-02-23
Note: Type of Document - ; pages: 17
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