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Bayesianism without learning

  • Samet, Dov

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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File URL: http://www.sciencedirect.com/science/article/B6WWP-45FJXRT-M/2/21dd8bdd7d1fda6e1661cf245831aeb9
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Article provided by Elsevier in its journal Research in Economics.

Volume (Year): 53 (1999)
Issue (Month): 2 (June)
Pages: 227-242

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Handle: RePEc:eee:reecon:v:53:y:1999:i:2:p:227-242
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/622941

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  1. Dov Samet, 1998. "Quantified beliefs and believed quantities," Game Theory and Information 9805003, EconWPA.
  2. Battigalli, Pierpaolo & Bonanno, Giacomo, 1997. "The Logic of Belief Persistence," Economics and Philosophy, Cambridge University Press, vol. 13(01), pages 39-59, April.
  3. Aviad Heifetz & Dov Samet, 1996. "Topology-Free Typology of Beliefs," Game Theory and Information 9609002, EconWPA, revised 17 Sep 1996.
  4. 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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