Objectively reliable subjective probabilities

Synthese 109 (3):293 - 309 (1996)
Subjective Bayesians typically find the following objection difficult to answer: some joint probability measures lead to intuitively irrational inductive behavior, even in the long run. Yet well-motivated ways to restrict the set of reasonable prior joint measures have not been forthcoming. In this paper I propose a way to restrict the set of prior joint probability measures in particular inductive settings. My proposal is the following: where there exists some successful inductive method for getting to the truth in some situation, we ought to employ a (joint) probability measure that is inductively successful in that situation, if such a measure exists. In order to do show that the restriction is possible to meet in a broad class of cases, I prove a Bayesian Completeness Theorem, which says that for any solvable inductive problem of a certain broad type, there exist probability measures that a Bayesian could use to solve the problem. I then briefly compare the merits of my proposal with two other well-known proposals for constraining the class of admissible subjective probability measures, the leave the door ajar condition and the maximize entropy condition.
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DOI 10.1007/BF00413863
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References found in this work BETA
Cory Juhl & Kevin T. Kelly (1994). Realism, Convergence, and Additivity. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:181 - 189.

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Citations of this work BETA
Gordon Belot (forthcoming). Curve-Fitting for Bayesians? British Journal for the Philosophy of Science:axv061.

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