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 InfoPaper provided by EconWPA in its series Game Theory and Information with number 9902004.
Length: 17 pages
Date of creation: 23 Feb 1999
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Bayesian updating; prior and posterior;
Other versions of this item:
- 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
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