Belief revision generalized: A joint characterization of Bayes' and Jeffrey's rules

• 成果类型: Article
• 署名作者: Dietrich, Franz; List, Christian; Bradley, Richard
• 署名单位: Paris School of Economics; Centre National de la Recherche Scientifique (CNRS); University of London; London School Economics & Political Science
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2015.11.006
• 发表日期: 2016
• 页码: 352-371
• 关键词: Belief revision; subjective probability; Bayes' and Jeffrey's rules; Axiomatic foundations; Fine-grained versus coarse-grained beliefs; unawareness

摘要

We present a general framework for representing belief-revision rules and use it to characterize Bayes' rule as a classical example and Jeffrey's rule as a non-classical one. In Jeffrey's rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayes' rule, but a new assignment of probabilities to some events. Despite their differences, Bayes' and Jeffrey's rules can be characterized in terms of the same axioms: responsiveness, which requires that revised beliefs incorporate what has been learnt, and conservativeness, which requires that beliefs on which the learnt input is 'silent' do not change. To illustrate the use of non-Bayesian belief revision in economic theory, we sketch a simple decision theoretic application. (C) 2015 Published by Elsevier Inc.