Non-Bayesian Updating: a Theoretical Framework

Larry Epstein (), Jawwad Noor () and Alvaro Sandroni ()
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Alvaro Sandroni: J.L.Kellogg School of Management, MEDS, Northwestern University

No 518, RCER Working Papers from University of Rochester - Center for Economic Research (RCER)

Abstract: This paper models an agent in a multi-period setting who does not update according to Bayes. Rule, and who is self-aware and anticipates her updating behavior when formulating plans. Choice-theoretic axiomatic foundations are provided. Then the model is specialized axiomatically to capture updating biases that re.ect excessive weight given to (i) prior be- liefs, or alternatively, (ii) the realized sample. Finally, the paper describes a counterpart of the exchangeable Bayesian model, where the agent tries to learn about parameters, and some answers are provided to the question, "what does a non-Bayesian updater learn?"

Keywords: skewed; returns (search for similar items in EconPapers)
Date: 2005-07
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Related works:
Working Paper: NON-BAYESIAN UPDATING: A THEORETICAL FRAMEWORK (2005)
Working Paper: NON-BAYESIAN UPDATING: A THEORETICAL FRAMEWORK (2005)
Working Paper: Non-Bayesian Updating: A Theoretical Framework (2003)
Journal Article: Non-Bayesian updating: A theoretical framework (2008)
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Persistent link: http://EconPapers.repec.org/RePEc:roc:rocher:518

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