A Dual-Threshold Probabilistic Knowing Value Logic
Shanxia Wang

TL;DR
This paper introduces a dual-threshold probabilistic knowing value logic that unifies probabilistic attitudes and high-confidence value attitudes in uncertain multi-agent scenarios, with applications to privacy and knowledge representation.
Contribution
It presents a novel formalism combining probabilistic and high-confidence knowing value logics, along with sound axiomatic systems and a two-layer construction for probabilistic models.
Findings
Established sound axiomatic systems for the logic.
Developed a two-layer construction based on type-space distributions.
Proved a structured weak-completeness theorem for the high-threshold fragment.
Abstract
We introduce a dual-threshold probabilistic knowing value logic for uncertain multi-agent settings. The framework captures within a single formalism both probabilistic-threshold attitudes toward propositions and high-confidence attitudes toward term values, thereby connecting probabilistic epistemic logic with classical knowing value logic. It is especially motivated by privacy-sensitive scenarios in which an attacker assigns high posterior probability to a candidate sensitive value without guaranteeing that it is the true one. The main idea is to separate the threshold domains of propositional and value-oriented operators. While ranges over the full rational threshold interval, the knowing-value operator is restricted to . This high-threshold restriction has a structural effect: once , two distinct values cannot both…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference · Semantic Web and Ontologies
