Non-Bayesian updating: A theoretical framework

Larry G. Epstein; Jawwad Noor; Alvaro Sandroni

Non-Bayesian updating: A theoretical framework

Larry G. Epstein^*, Jawwad Noor, Alvaro Sandroni

^*Corresponding author for this work

Managerial Economics, Decision Sciences and Operations

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

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 to capture updating biases that reflect excessive weight given to either prior beliefs, or, alternatively, to observed data. A counterpart of the exchangeable Bayesian learning model is also described.

Original language	English (US)
Pages (from-to)	193-229
Number of pages	37
Journal	Theoretical Economics
Volume	3
Issue number	2
State	Published - 2008

Keywords

Law of small numbers
Learning
Non-Bayesian updating
Overreaction
Temptation and self-control
Underreaction

ASJC Scopus subject areas

Economics, Econometrics and Finance(all)

Cite this

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AB - 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 to capture updating biases that reflect excessive weight given to either prior beliefs, or, alternatively, to observed data. A counterpart of the exchangeable Bayesian learning model is also described.

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Non-Bayesian updating: A theoretical framework

Abstract

Keywords

ASJC Scopus subject areas

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