Bayesianism without learning
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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References listed on IDEAS
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.:
- Aviad Heifetz & Dov Samet, 1996.
"Topology-Free Typology of Beliefs,"
Game Theory and Information
9609002, EconWPA, revised 17 Sep 1996.
- Giacomo Bonanno & Pierpaolo Battigalli, 2004.
"The Logic Of Belief Persistency,"
9518, University of California, Davis, Department of Economics.
- Dov Samet, 1998.
"Quantified beliefs and believed quantities,"
Game Theory and Information
- Samet, Dov, 1998. "Iterated Expectations and Common Priors," Games and Economic Behavior, Elsevier, vol. 24(1-2), pages 131-141, July.
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