David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Philosophy of Science 77 (2):236-272 (2010)
One of the fundamental problems of epistemology is to say when the evidence in an agent’s possession justifies the beliefs she holds. In this paper and its prequel, we defend the Bayesian solution to this problem by appealing to the following fundamental norm: Accuracy An epistemic agent ought to minimize the inaccuracy of her partial beliefs. In the prequel, we made this norm mathematically precise; in this paper, we derive its consequences. We show that the two core tenets of Bayesianism follow from the norm, while the characteristic claim of the Objectivist Bayesian follows from the norm along with an extra assumption. Finally, we consider Richard Jeffrey’s proposed generalization of conditionalization. We show not only that his rule cannot be derived from the norm, unless the requirement of Rigidity is imposed from the start, but further that the norm reveals it to be illegitimate. We end by deriving an alternative updating rule for those cases in which Jeffrey’s is usually supposed to apply.
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Citations of this work BETA
Rachael Briggs (forthcoming). Foundations of Probability. Journal of Philosophical Logic:1-16.
Jennifer Carr (2015). Epistemic Expansions. Res Philosophica 92 (2):217-236.
Kenny Easwaran (2015). Dr. Truthlove Or: How I Learned to Stop Worrying and Love Bayesian Probabilities. Noûs 50 (2).
Ulrike Hahn (2014). The Bayesian Boom: Good Thing or Bad? Frontiers in Psychology 5.
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