Common p-Belief with Plausibility Measures: Extended Abstract

An epistemic probability model has a common prior if P i = P j for all i, j A .To simplify notation, we write W, ( i ) iA , F , P when P is the common prior.Remark 2.2.We could also allow a different -algebra F i for each P i , but this complexity is unnecessary for the purpose of this paper.Given a state w and an agent i A , the set i (w) represents i's information at state w.With this interpretation in mind, we can now define the agents' knowledge and posterior beliefs.Definition 2.3 (Knowledge).Suppose that W, ( i ) iA , F , (P i ) iA is an epistemic probability model.The knowledge operator for agent i A is the function K i : (W ) (W ) where: for all E W ,If w K i (E) we say that agent i knows that E in w. 3 Definition 2.4 (Posterior Belief).Given an epistemic probability model W, ( i ) iA , F , (P i ) iA .The posterior belief for i at state w is the (possibly partial) function P i,w : (W ) [0, 1] where: for all E W ,Suppose that W, ( i ) iA , F , (P i ) iA is an epistemic-probability model.We write K(E) for the event that everyone knows E, and C(E) for the event that E is common knowledge. 4Formally, everyone knows is a function K : (W ) (W ) where: for all E W ,

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Journal
Electronic Proceedings in Theoretical Computer Science
Published
2025-11-25
DOI
https://doi.org/10.4204/eptcs.437.10
Primary Topic
Logic, Reasoning, and Knowledge
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article
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article

Common p-Belief with Plausibility Measures: Extended Abstract

Eric Pacuit, Lin Yang
Electronic Proceedings in Theoretical Computer Science
Logic, Reasoning, and Knowledge
article

Common p-Belief with Plausibility Measures: Extended Abstract

Eric Pacuit, Lin Yang
article en

Abstract

An epistemic probability model has a common prior if P i = P j for all i, j A .To simplify notation, we write W, ( i ) iA , F , P when P is the common prior.Remark 2.2.We could also allow a different -algebra F i for each P i , but this complexity is unnecessary for the purpose of this paper.Given a state w and an agent i A , the set i (w) represents i's information at state w.With this interpretation in mind, we can now define the agents' knowledge and posterior beliefs.Definition 2.3 (Knowledge).Suppose that W, ( i ) iA , F , (P i ) iA is an epistemic probability model.The knowledge operator for agent i A is the function K i : (W ) (W ) where: for all E W ,If w K i (E) we say that agent i knows that E in w. 3 Definition 2.4 (Posterior Belief).Given an epistemic probability model W, ( i ) iA , F , (P i ) iA .The posterior belief for i at state w is the (possibly partial) function P i,w : (W ) [0, 1] where: for all E W ,Suppose that W, ( i ) iA , F , (P i ) iA is an epistemic-probability model.We write K(E) for the event that everyone knows E, and C(E) for the event that E is common knowledge. 4Formally, everyone knows is a function K : (W ) (W ) where: for all E W ,

Electronic Proceedings in Theoretical Computer ScienceVol. 437
University of Maryland, College Park (US)
Openalex Percentile: Top 81%
Logic, Reasoning, and Knowledge
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Common p-Belief with Plausibility Measures: Extended Abstract — Eric Pacuit, Lin Yang · Electronic Proceedings in Theoretical Computer Science (2025) | TGRS Research Map | TGRS