Representing and Aggregating Conflicting Beliefs

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dc.creatorMaynard-Reid II, Pedrito
dc.creatorLehmann, Daniel
dc.date2002-03-11
dc.date.accessioned2026-07-07T03:18:12Z
dc.date.available2026-07-07T03:18:12Z
dc.descriptionWe consider the two-fold problem of representing collective beliefs and aggregating these beliefs. We propose modular, transitive relations for collective beliefs. They allow us to represent conflicting opinions and they have a clear semantics. We compare them with the quasi-transitive relations often used in Social Choice. Then, we describe a way to construct the belief state of an agent informed by a set of sources of varying degrees of reliability. This construction circumvents Arrow's Impossibility Theorem in a satisfactory manner. Finally, we give a simple set-theory-based operator for combining the information of multiple agents. We show that this operator satisfies the desirable invariants of idempotence, commutativity, and associativity, and, thus, is well-behaved when iterated, and we describe a computationally effective way of computing the resulting belief state.
dc.description19 pages, 5 figures, appears (without proofs) in Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR 2000)
dc.identifierhttps://arxiv.org/abs/cs/0203013
dc.identifierhttp://arxiv.org/abs/cs/0203013
dc.identifierProceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR 2000), April 2000, pp. 153-164
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31020
dc.subjectArtificial Intelligence
dc.subjectLogic in Computer Science
dc.subjectI.2.4; I.2.11
dc.titleRepresenting and Aggregating Conflicting Beliefs
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