Non-bayesian learning

Larry G. Epstein, Jawwad Noor, Alvaro Sandroni

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


A series of experiments suggest that, compared to the Bayesian benchmark, people may either underreact or overreact to new information. We consider a setting where agents repeatedly process new data. Our main result shows a basic distinction between the long-run beliefs of agents who underreact to information and agents who overreact to information. Like Bayesian learners, non-Bayesian updaters who underreact to observations eventually forecast accurately. Hence, underreaction may be a transient phenomenon. Non-Bayesian updaters who overreact to observations eventually forecast accurately with positive probability but may also, with positive probability, converge to incorrect forecasts. Hence, overreaction may have long-run consequences.

Original languageEnglish (US)
Article number3
JournalB.E. Journal of Theoretical Economics
Issue number1
StatePublished - 2010


  • Non-Bayesian learning

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)

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