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Supposing and learning: a unified framework for belief revision

Author

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  • Giacomo Bonanno

    (Department of Economics, University of California Davis)

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.

Suggested Citation

  • Giacomo Bonanno, 2026. "Supposing and learning: a unified framework for belief revision," Working Papers 379, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:379
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    File URL: https://repec.dss.ucdavis.edu/files/ho66adzrva6o0g0dbkrdbq3woauw/Unified%20framework.pdf
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    References listed on IDEAS

    as
    1. Stalnaker, Robert, 1998. "Belief revision in games: forward and backward induction1," Mathematical Social Sciences, Elsevier, vol. 36(1), pages 31-56, July.
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    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General

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