Rinard, Susanna (2014) Imprecise Probability and Higher Order Vagueness. In: UNSPECIFIED.

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Abstract

There is a trade-off between specificity and accuracy in existing models of belief. Descriptions of agents in the tripartite model, which recognizes only three doxastic attitudes—belief, disbelief, and suspension of judgment—are typically accurate, but not sufficiently specific. The orthodox Bayesian model, which requires real-valued credences, is perfectly specific, but often inaccurate: we often lack precise credences. I argue, first, that a popular attempt to fix the Bayesian model by using sets of functions is also inaccurate, since it requires us to have interval-valued credences with perfectly precise endpoints. We can see this problem as analogous to the problem of higher order vagueness. Ultimately, I argue, the only way to avoid these problems is to endorse Insurmountable Unclassifiability. This principle has some surprising and radical consequences. For example, it entails that the trade-off between accuracy and specificity is in-principle unavoidable: sometimes it is simply impossible to characterize an agent’s doxastic state in a way that is both fully accurate and maximally specific. What we can do, however, is improve on both the tripartite and existing Bayesian models. I construct a new model of belief—the minimal model—that allows us to characterize agents with much greater specificity than the tripartite model, and yet which remains, unlike existing Bayesian models, perfectly accurate.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Rinard, Susanna
Subjects:	General Issues > Confirmation/Induction General Issues > Decision Theory General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Depositing User:	Susanna Rinard
Date Deposited:	12 Jul 2014 12:21
Last Modified:	12 Jul 2014 12:21
Item ID:	10865
Subjects:	General Issues > Confirmation/Induction General Issues > Decision Theory General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Date:	10 July 2014
URI:	https://philsci-archive.pitt.edu/id/eprint/10865

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