Template-Type: ReDIF-Paper 1.0
Author-Name: Giacomo Bonanno
Author-Name-First: Giacomo
Author-Name-Last: Bonanno
Author-Workplace-Name: Department of Economics, University of California Davis
Title: Supposing and learning: a unified framework for belief revision
Abstract: Consider two possible scenarios for belief revision. Initially the agent either believes that A is not the case (that is, believes not-A) or suspends belief about A. In one scenario she receives reliable information that, as a matter of fact, A is the case; call this scenario "learning that A". In the other scenario she reasons about what she believes would be the case if A were the case; call this scenario "supposing that A". We argue that there are important differences between the two scenarios. It was shown in  Bonanno G. Artificial Intelligence 339 (2025) that it is possible to view the AGM theory of belief revision as a theory of hypothetical, or suppositional, reasoning, rather than a theory of actual belief change in response to new information. By making an addition to the Kripke-Lewis semantics considered in  Bonanno G. Artificial Intelligence 339 (2025), we (1) provide a unified framework for the analysis of both suppositional beliefs and information-driven belief change, (2) argue that some of the AGM axioms are not appropriate for the latter and (3) provide a list of axioms that seem appropriate for belief change in response to new information.
Length: 27
File-URL: https://repec.dss.ucdavis.edu/files/ho66adzrva6o0g0dbkrdbq3woauw/Unified%20framework.pdf
File-Format: application/pdf
Number: 379
Classification-JEL: C0
KeyWords: belief change, belief revision, supposition, information, conditional, Kripke relation, Lewis selection function, Stalnaker selection function
Creation-Date: 20260322
Handle: RePEc:cda:wpaper:379