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An axiomatic characterization of Bayesian updating


Alos-Ferrer, Carlos; Mihm, Maximilian (2023). An axiomatic characterization of Bayesian updating. Journal of Mathematical Economics, 104:102799.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > Economics and Econometrics
Physical Sciences > Applied Mathematics
Uncontrolled Keywords:Applied mathematics, economics and econometrics, belief updating, Bayesian learning
Language:English
Date:1 January 2023
Deposited On:03 Feb 2023 12:35
Last Modified:28 Feb 2024 02:50
Publisher:Elsevier
ISSN:0304-4068
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.jmateco.2022.102799
  • Content: Published Version
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)