Objective and subjective foundations for multiple priors

Maxwell B. Stinchcombe

Journal of Economic Theory, 2016, vol. 165, issue C, 263-291

Abstract: Foundations for priors can be grouped in two broad categories: objective, deriving probabilities from observations of similar instances; and subjective, deriving probabilities from the internal consistency of choices. Partial observations of similar instances and the Savage–de Finetti extensions of subjective priors yield objective and subjective sets of priors suitable for modeling choice under ambiguity. These sets are best suited to such modeling when the distribution of the observables, or the prior to be extended, is non-atomic. In this case, the sets can be used to model choices between elements of the closed convex hull of the faces in the set of distributions over outcomes, equivalently, all sets bracketed by the upper and lower probabilities induced by correspondences.

Keywords: Multiple prior models and ambiguous choice; Partial observability and partially identified models; Finitely additive learning models; Savage–de Finetti indeterminacy; Dempster compatible sets of probabilities; Difficulty of learning problems (search for similar items in EconPapers)
JEL-codes: C4 D8 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:165:y:2016:i:c:p:263-291

DOI: 10.1016/j.jet.2016.04.011

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