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.
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.:
- Dov Samet, 1998.
"Quantified beliefs and believed quantities,"
Game Theory and Information
- Heifetz, Aviad & Samet, Dov, 1998.
"Topology-Free Typology of Beliefs,"
Journal of Economic Theory,
Elsevier, vol. 82(2), pages 324-341, October.
- Giacomo Bonanno & Pierpaolo Battigalli, 2004.
"The Logic Of Belief Persistency,"
9518, University of California, Davis, Department of Economics.
- 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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