Conditional Belief, Knowledge and Probability
Jan van Eijck (CWI, ILLC), Kai Li (Peking University, CWI)

TL;DR
This paper develops a logical framework for reasoning about conditional belief and knowledge using neighbourhood models, connecting it with probabilistic logics, and extends it with public announcement operators.
Contribution
It introduces a sound and complete neighbourhood logic for conditional belief and knowledge, bridging non-numerical plausibility models with probabilistic reasoning.
Findings
Neighbourhood models for belief are closed under public announcement updates.
The proposed calculus is sound and can be extended to achieve completeness for weighted Kripke models.
Adding announcement operators does not increase the language's expressive power.
Abstract
A natural way to represent beliefs and the process of updating beliefs is presented by Bayesian probability theory, where belief of an agent a in P can be interpreted as a considering that P is more probable than not P. This paper attempts to get at the core logical notion underlying this. The paper presents a sound and complete neighbourhood logic for conditional belief and knowledge, and traces the connections with probabilistic logics of belief and knowledge. The key notion in this paper is that of an agent a believing P conditionally on having information Q, where it is assumed that Q is compatible with what a knows. Conditional neighbourhood logic can be viewed as a core system for reasoning about subjective plausibility that is not yet committed to an interpretation in terms of numerical probability. Indeed, every weighted Kripke model gives rise to a conditional neighbourhood…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Logic, Reasoning, and Knowledge · Epistemology, Ethics, and Metaphysics
