We build on AGM belief revision and propose a class of updating rules called pragmatic rules. Pragmatic updating applies to multiple priors and requires that the agent’s posteriors be the subset of her priors that perfectly predict the occurred event, if such priors exist. We construct a propositional language based on qualitative probability, and demonstrate the strong relation between belief updating rules and belief revision rules in this language. We show that an updating rule is consistent with AGM belief revision if and only if it is pragmatic. While maximum likelihood updating is pragmatic in general, full-Bayesian updating is not.
Co-Authors (if applicable)
Name
Affiliation
Country
Jinling Ma
The University of Hong Kong
China
Presenter #2
Name
Henrique De Oliveira
Email
Email hidden; Javascript is required.
Affiliation
São Paulo School of Economics – FGV
Country
Brazil
Title of Paper
Robust Merging of Information
Co-Authors (if applicable)
Name
Affiliation
Country
Yuhta Ishii
Pennsylvania State University
US
Xiao Lin
University of Pennsylvania
US
Presenter #3
Name
Michèle Müller-Itten
Email
Email hidden; Javascript is required.
Affiliation
University of Notre Dame
Country
US
Title of Paper
Rational Inattention via Ignorance Equivalence
Co-Authors (if applicable)
Name
Affiliation
Country
Roc Armenter
Federal Reserve Bank of Philadelphia Research Department
