Confidence, consensus and aggregation

Brian Hill ()
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Brian Hill: HEC Paris - Recherche - Hors Laboratoire - HEC Paris - Ecole des Hautes Etudes Commerciales, CNRS - Centre National de la Recherche Scientifique, GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper develops and defends a new approach to belief aggregation, involving confidence in beliefs. It is axiomatically characterised by a variant of the Pareto condition that enjoins respecting consensuses borne of compromise. Confidence aggregation generalises standard probability aggregation rules—such as linear pooling— whilst avoiding the spurious unanimity issues that have plagued them. It generates the first family of probability aggregation rules that can faithfully accommodate within-person expertise diversity, hence resolving a longstanding challenge. It is dynamically rational, insofar as it commutes with update. Finally, it recovers as special cases both Bayesian and non-Bayesian approaches to model misspecification.

Keywords: Belief aggregation; confidence in beliefs; Pareto principle; linear pooling; spurious unanimity; expertise; consensus; model averaging; model misspecification (search for similar items in EconPapers)
Date: 2024-05-14
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04381136

DOI: 10.2139/ssrn.4387641

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