Australasian Journal of Philosophy 97 (1):151-166 (2019)
AbstractABSTRACTSuppose that beliefs come in degrees. How should we then measure the accuracy of these degrees of belief? Scoring rules are usually thought to be the mathematical tool appropriate for this job. But there are many scoring rules, which lead to different ordinal accuracy rankings. Recently, Fallis and Lewis  have given an argument that, if sound, rules out many popular scoring rules, including the Brier score, as genuine measures of accuracy. I respond to this argument, in part by noting that the argument fails to account for verisimilitude—that certain false hypotheses might be closer to the truth than other false hypotheses are. Oddie [forthcoming], however, has argued that no member of a very wide class of scoring rules can appropriately handle verisimilitude. I explain how to respond to Oddie's argument, and I recommend a class of weighted scoring rules that, I argue, genuinely measure accuracy while escaping the arguments of Fallis and Lewis as well as Oddie.
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Citations of this work
Accuracy and Verisimilitude: The Good, the Bad, and the Ugly.Miriam Schoenfield - 2022 - British Journal for the Philosophy of Science 73 (2):373-406.
Accuracy, conditionalization, and probabilism.Don Fallis & Peter J. Lewis - 2019 - Synthese 198 (5):4017-4033.
Propositional and credal accuracy in an indeterministic world.Graham Oddie - 2021 - Synthese 199 (3-4):9391-9410.
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Justifying conditionalization: Conditionalization maximizes expected epistemic utility.Hilary Greaves & David Wallace - 2006 - Mind 115 (459):607-632.