The Department of Logic & Philosophy of Science Colloquium Series presents
“Imprecise Probability and Higher Order Vagueness”
with Susanna Rinard, Department of Philosophy, University of Missouri- Kansas City
Friday, May 2, 2014
Social Science Tower, Room 777
Many employ a tripartite model with three doxastic attitudes: belief, disbelief, and suspension of judgment. Descriptions in this model are typically /accurate/, but not sufficiently /specific/.The orthodox Bayesian model requires precise real-valued credences. This model is highly /specific/, but often /inaccurate/: we often lack precise credences. Bayesians hoping to accommodate this fact propose using a /set/ of probability functions, which generates interval-valued credences. Unfortunately, this view suffers from a version of the same basic problem: it requires precise interval endpoints. Rinard will show that this problem is structurally analogous to the problem of higher order vagueness. Analogous potential solutions exhibit analogous virtues and failings. Ultimately, she argues, the only way to avoid these problems is to endorse a principle she calls Insurmountable Unclassifiability. This principle has some surprising and radical consequences. For example, it entails that sometimes it is 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. She will present a new, /minimal /model of belief that allows us to characterize agents with much greater specificity than the tripartite model, and yet which remains, unlike existing Bayesian models, perfectly accurate.
For further information, please contact Patty Jones, email@example.com or 949-824-1520.