Non-Bayesian updating: A theoretical framework

Larry G. Epstein*, Jawwad Noor, Alvaro Sandroni

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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 languageEnglish (US)
Pages (from-to)193-229
Number of pages37
JournalTheoretical Economics
Issue number2
StatePublished - 2008


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

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)


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