Dov Samet (Faculty of Management Tel Aviv University)
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.
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Find related papers by 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
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