Belief Revision from Probability
This work addresses belief revision dynamics for researchers in formal epistemology and AI, but it is incremental as it builds on prior probabilistic accounts.
The paper explores the implications of a probabilistic account of belief for belief revision dynamics, showing that it validates principles weaker than AGM but stronger than Lockean theories, and argues that this framework compares favorably to other probabilistic accounts.
In previous work ("Knowledge from Probability", TARK 2021) we develop a question-relative, probabilistic account of belief. On this account, what someone believes relative to a given question is (i) closed under entailment, (ii) sufficiently probable given their evidence, and (iii) sensitive to the relative probabilities of the answers to the question. Here we explore the implications of this account for the dynamics of belief. We show that the principles it validates are much weaker than those of orthodox theories of belief revision like AGM, but still stronger than those valid according to the popular Lockean theory of belief, which equates belief with high subjective probability. We then consider a restricted class of models, suitable for many but not all applications, and identify some further natural principles valid on this class. We conclude by arguing that the present framework compares favorably to the rival probabilistic accounts of belief developed by Leitgeb and by Lin and Kelly.