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NON-BAYESIAN UPDATING: A THEORETICAL FRAMEWORK

Larry Epstein (), Jawwad Noor () and Alvaro Sandroni ()
Additional contact information
Alvaro Sandroni: Kellogg School of Management

No WP2005-049, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

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 reflect 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?

Pages: 50 pages
Date: 2005-10
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Journal Article: Non-Bayesian updating: A theoretical framework (2008) Downloads
Working Paper: NON-BAYESIAN UPDATING: A THEORETICAL FRAMEWORK (2005)
Working Paper: Non-Bayesian Updating: a Theoretical Framework (2005) Downloads
Working Paper: Non-Bayesian Updating: A Theoretical Framework (2003) Downloads
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