What is it about?

Probabilities can't handle ignorance very well. This can lead to unwarranted inferences and conclusions. I argue that imprecise probabilities (sets of probability functions) can meet the criteria of a representation of ignorance for a logic of induction.

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Why is it important?

Without a good representation of ignorance, we can't avoid making unwarranted conclusions or predictions if we use probabilities. Examples from cosmology or the Doomsday argument are discussed in the paper. This is mostly a response to arguments made mainly by John Norton (University of Pittsburgh) against the relevance of imprecise probabilities and their ability to overcome the shortcoming of probabilities.

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This page is a summary of: The Bayesian who knew too much, Synthese, January 2015, Springer Science + Business Media,
DOI: 10.1007/s11229-014-0647-3.
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