Objective and subjective foundations for multiple priors

• 成果类型: Article
• 署名作者: Stinchcombe, Maxwell B.
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2016.04.011
• 发表日期: 2016
• 页码: 263-291
• 关键词: 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

摘要

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. (C) 2016 Elsevier Inc. All rights reserved.