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Level-strategyproof Belief Aggregation Mechanisms

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorLaraki, Rida
HAL ID: 179670
ORCID: 0000-0002-4898-2424
hal.structure.identifierUniversité de Liverpool
dc.contributor.authorVarloot, Estelle
dc.date.accessioned2022-02-28T09:09:36Z
dc.date.available2022-02-28T09:09:36Z
dc.date.issued2021
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/22782
dc.language.isoenen
dc.subjectProbability Aggregation Functionsen
dc.subjectordered Set of Alternativesen
dc.subjectLevel Strategy-Proofnessen
dc.subjectProportional-Cumulativeen
dc.subjectMiddlemost-Cumulativeen
dc.subject.ddc004en
dc.titleLevel-strategyproof Belief Aggregation Mechanismsen
dc.typeDocument de travail / Working paper
dc.description.abstractenIn the problem of aggregating experts' probabilistic predictions over an ordered set of outcomes, we introduce the axiom of level-strategy\-proofness (level-SP) and prove that it is a natural notion with several applications. Moreover, it is a robust concept as it implies incentive compatibility in a rich domain of single-peakedness over the space of cumulative distribution functions (CDFs). This contrasts with the literature which assumes single-peaked preferences over the space of probability distributions. Our main results are: (1) a reduction of our problem to the aggregation of CDFs; (2) the axiomatic characterization of level-SP probability aggregation functions with and without the addition of other axioms; (3) impossibility results which provide bounds for our characterization; (4) the axiomatic characterization of two new and practical level-SP methods: the proportional-cumulative method and the middlemost-cumulative method; and (5) the application of proportional-cumulative to extend approval voting, majority rule, and majority judgment methods to situations where voters/experts are uncertain about how to grade the candidates/alternatives to be rankeden
dc.publisher.cityParisen
dc.relation.ispartofseriestitlePreprint Lamsadeen
dc.subject.ddclabelInformatique généraleen
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2022-02-28T09:08:39Z
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