TITLE

Conditionalization Does Not (in General) Maximize Expected Accuracy

AUTHOR(S)
SCHOENFIELD, MIRIAM
PUB. DATE
October 2017
SOURCE
Mind;Oct2017, Vol. 126 Issue 504, p1155
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Greaves and Wallace argue that conditionalization maximizes expected accuracy. In this paper I show that their result only applies to a restricted range of cases. I then show that the update procedure that maximizes expected accuracy in general is one in which, upon learning P, we conditionalize, not on P, but on the proposition that we learned P. After proving this result, I provide further generalizations and show that much of the accuracy-first epistemology program is committed to KK-like iteration principles and to the existence of a class of propositions that rational agents will be certain of if and only if they are true.
ACCESSION #
126292431

 

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