Objective and subjective foundations for multiple priors
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.
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References listed on IDEAS
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- Maxwell B. Stinchcombe & Halbert White, 1992. "Some Measurability Results for Extrema of Random Functions Over Random Sets," Review of Economic Studies, Oxford University Press, vol. 59(3), pages 495-514.
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- Nabil I. Al-Najjar, 2009. "Decision Makers as Statisticians: Diversity, Ambiguity, and Learning," Econometrica, Econometric Society, vol. 77(5), pages 1371-1401, September.
- Stinchcombe, Maxwell B., 1990. "Bayesian information topologies," Journal of Mathematical Economics, Elsevier, vol. 19(3), pages 233-253. Full references (including those not matched with items on IDEAS)
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