Larry Epstein () (University of Rochester) Jawwad Noor () (University of Rochester) Alvaro Sandroni () (J.L.Kellogg School of Management, MEDS, Northwestern University)
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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?"
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Paper provided by University of Rochester - Center for Economic Research (RCER) in its series RCER Working Papers with number
518.
Length: 49 pages Date of creation: Jul 2005 Date of revision: Handle: RePEc:roc:rocher:518
Contact details of provider: Postal: UNIVERSITY OF ROCHESTER, CENTER FOR ECONOMIC RESEARCH, DEPARTMENT OF ECONOMICS, HARKNESS 231 ROCHESTER NEW YORK 14627 U.S.A.
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Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
Chambers, Christopher P. & Hayashi, Takashi, 2005.
"Bayesian consistent prior selection,"
Working Papers
1238, California Institute of Technology, Division of the Humanities and Social Sciences.
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