An Axiomatic Characterization of Bayesian Updating

Carlos Alós-Ferrer, Maximilian Mihm

Research output: Contribution to journalArticlepeer-review


We provide an axiomatic characterization of Bayesian updating, viewed as a mapping from prior beliefs and new information to posteriors, which is disentangled from any reference to preferences. Bayesian updating is characterized by Non-Innovativeness (events considered impossible in the prior remain impossible in the posterior), Dropping (events contradicted by new evidence are considered impossible in the posterior), and Proportionality (for other events, the posterior simply rescales the prior's probabilities proportionally). The result clarifies the differences between the normative Bayesian benchmark, alternative models, and actual human behavior.

Original languageEnglish (US)
Article number102799
JournalJournal of Mathematical Economics
StatePublished - Jan 2023


  • Bayesian learning
  • Belief updating

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics


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