NON-BAYESIAN UPDATING: A THEORETICAL FRAMEWORK
Larry Epstein (),
Jawwad Noor () and
Alvaro Sandroni ()
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Alvaro Sandroni: Columbia Business School
No WP2005-025, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics
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 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?"
Pages: 50 pages
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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 (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:bos:wpaper:wp2005-025
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