Non-Bayesian Updating: A Theoretical Framework

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

No 505, RCER Working Papers from University of Rochester - Center for Economic Research (RCER)

Abstract: This paper models an agent in an infinite horizon 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 reflect excessive weight given to (i) prior beliefs, 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: non-Bayesian updating; overreaction; underreaction; confirmatory bias; law of small numbers; gambler's fallacy; hot hand fallacy; temptation; self-control; learning; menus (search for similar items in EconPapers)
Pages: 60 pages
Date: 2003-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Non-Bayesian updating: A theoretical framework (2008)
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 (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:roc:rocher:505

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