Bayesianism without Learning
Dov Samet ()
Game Theory and Information from University Library of Munich, Germany
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)
Note: Type of Document - ; pages: 17
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
Journal Article: Bayesianism without learning (1999)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpga:9902004
Access Statistics for this paper
More papers in Game Theory and Information from University Library of Munich, Germany
Bibliographic data for series maintained by EconWPA ().