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

Journal of Economic Theory 162:352-371 (2016)
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Abstract

We present a general framework for representing belief-revision rules and use it to characterize Bayes's 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's rule, but a new assignment of probabilities to some events. Despite their differences, Bayes's 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.

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Author Profiles

Franz Dietrich
Centre National de la Recherche Scientifique
Christian List
Ludwig Maximilians Universität, München
Richard Bradley
London School of Economics

Citations of this work

What is conditionalization, and why should we do it?Richard Pettigrew - 2020 - Philosophical Studies 177 (11):3427-3463.
Foundations for Knowledge-Based Decision Theories.Zeev Goldschmidt - forthcoming - Australasian Journal of Philosophy.
A Theory of Bayesian Groups.Franz Dietrich - 2017 - Noûs 53 (3):708-736.
Aggregating Causal Judgments.Richard Bradley, Franz Dietrich & Christian List - 2014 - Philosophy of Science 81 (4):491-515.

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Updating Subjective Probability.Persi Diaconis & Sandy L. Zabell - 1982 - Journal of the American Statistical Association 77 (380):822-830.
The Logic of Conditionals.[author unknown] - 1978 - Philosophy of Science 45 (1):155-158.

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