A dilemma for the imprecise bayesian

Synthese 193 (6):1681-1702 (2016)
Abstract
Many philosophers regard the imprecise credence framework as a more realistic model of probabilistic inferences with imperfect empirical information than the traditional precise credence framework. Hence, it is surprising that the literature lacks any discussion on how to update one’s imprecise credences when the given evidence itself is imprecise. To fill this gap, I consider two updating principles. Unfortunately, each of them faces a serious problem. The first updating principle, which I call “generalized conditionalization,” sometimes forces an agent to change her imprecise degrees of belief even though she does not have new evidence. The second updating principle, which I call “the generalized dynamic Keynesian model,” may result in a very precise credal state although the agent does not have sufficiently strong evidence to justify such an informative doxastic state. This means that it is much more difficult to come up with an acceptable updating principle for the imprecise credence framework than one might have thought it would be.
Keywords The imprecise credence framework  Imprecise evidence  New evidence  Dilation  Sharpening  Jeffrey conditionalization  The dynamic Keynesian model
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DOI 10.1007/s11229-015-0800-7
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References found in this work BETA
The Logic of Decision.Richard Jeffrey - 1965 - University of Chicago Press.
Sleeping Beauty: Reply to Elga.David Lewis - 2001 - Analysis 61 (3):171–76.
Subjective Probabilities Should Be Sharp.Adam Elga - 2010 - Philosophers' Imprint 10.

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