Bayesianism without learning
AbstractAccording 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Research in Economics.
Volume (Year): 53 (1999)
Issue (Month): 2 (June)
Contact details of provider:
Web page: http://www.elsevier.com/locate/inca/622941
Other versions of this item:JEL classification:
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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.:
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