Abstract
It is natural to think of precise probabilities as being special cases of imprecise probabilities, the special case being when one’s lower and upper probabilities are equal. I argue, however, that it is better to think of the two models as representing two different aspects of our credences, which are often (if not always) vague to some degree. I show that by combining the two models into one model, and understanding that model as a model of vague credence, a natural interpretation arises that suggests a hypothesis concerning how we can improve the accuracy of aggregate credences. I present empirical results in support of this hypothesis. I also discuss how this modeling interpretation of imprecise probabilities bears upon a philosophical objection that has been raised against them, the so-called inductive learning problem.
Original language | English |
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Pages (from-to) | 3931-3954 |
Number of pages | 24 |
Journal | Synthese |
Volume | 194 |
Issue number | 10 |
DOIs | |
Publication status | Published - 1 Oct 2017 |