Imprecise Probabilities in Decision-making
Imprecise Probabilities in Decision-making
Talk by Jonas Karge
- Location: Online
- Start: 9. July 2020 at 1:00 pm
- End: 9. July 2020 at 2:30 pm
- Event series: KBS Seminar
- iCal
proposition as a measure of the strength of her belief in that proposition. According to the orthodox Bayesian picture, an agent's degree of belief is best represented by a single probability function. In particular, the Bayesian claims that agents must assign numerically precise probabilities to every proposition that they can entertain. On an alternative account, an agent’s beliefs ought to be modeled based on imprecise probabilities. With that, imprecise degrees of belief can be represented by a set of probability functions. Recently, however, imprecise probabilities have come under attack. Adam Elga (2010) claims that there is no adequate account of the way they can be manifested in decision-making. In response to Elga, more elaborate accounts of the imprecise framework have been developed. One of them is based on Supervaluationism, originally, a semantic approach to vague predicates. Still, Seamus Bradley (2019) shows that those accounts that solve Elga’s problem, have a more severe defect: they undermine a central motivation to introduce imprecise probabilities in the first place. The aim of my presentation is to modify the supervaluationist approach in such a way that it accounts for both Elga’s and Bradley’s challenges to the imprecise framework.
