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Radical Probabilism and Bayesian Conditioning*

Published online by Cambridge University Press:  01 January 2022

Abstract

Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves one's conditional beliefs untouched (the ‘Rigidity’ condition). Although Richard Jeffrey denied the general applicability of this condition, one of his main contributions to probabilistic thinking is a form of belief updating—now typically called ‘Jeffrey conditioning’ or ‘probability kinematics’—that is appropriate in circumstances in which Rigidity is satisfied, but where the interaction causes one to reevaluate one's probability judgments over some partition of the possibility space without conferring certainty on any particular element. The most familiar occasion for Jeffrey conditioning is receipt of uncertain evidence: things partially perceived or remembered. But it also serves to illuminate belief updating occasioned by a change in one's degrees of conditional belief, a kind of belief change largely ignored by classical Bayesianism. I argue that such changes in conditional belief can also be basic (in the sense of not being analyzable as a consequence of conditioning on factual information) and offer a kinematical model for a particular kind change in conditional belief. Both are applied to changes in preference. Finally, I argue that Rigidity can fail when changes of belief give inferential grounds for changes in conditional belief (and vice versa). These failures show that conditioning methods are properly regarded, not as valid rules of inference, but as tools in the ‘art of judgment’.

Type
Research Article
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

This paper is dedicated to the memory of Richard Jeffrey, from whom I learned so much.

References

Armendt, Brad (1980), “Is There a Dutch Book Theorem for Probability Kinematics?”, Is There a Dutch Book Theorem for Probability Kinematics? 47:563588.Google Scholar
Bolker, Ethan (1966), “Functions Resembling Quotients of Measures”, Functions Resembling Quotients of Measures 124:292312.Google Scholar
Bradley, Richard (1999), “Conditional Desirability”, Conditional Desirability 47:2355.Google Scholar
Diaconis, Percy, and Zabell, Sandy (1982), “Updating Subjective Probability”, Updating Subjective Probability 77:822830.Google Scholar
Earman, John (1992), Bayes or Bust: A Critical Examination of Bayesian Confirmation Theory. Cambridge, MA: MIT Press.Google Scholar
Field, Harty (1978), “A Note on Jeffrey Conditionalization”, A Note on Jeffrey Conditionalization 45:361367.Google Scholar
Garber, Daniel (1980), “Field and Jeffrey Conditionalization”, Field and Jeffrey Conditionalization 47:142145.Google Scholar
Howson, Colin (1996), “Bayesian Rules of Updating”, Bayesian Rules of Updating 45:195208.Google Scholar
Jeffrey, Richard (1983), The Logic of Decision. Chicago: University of Chicago Press.Google Scholar
Jeffrey, Richard (1992), Probability and the Art of Judgment. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Ramsey, Frank Plumpton ([1926] 1990), “Truth and Probability,” in Mellor, D. H. (ed.), Philosophical Papers. Cambridge: Cambridge University Press.Google Scholar
Savage, Leonard (1972), The Foundations of Statistics. New York: Dover.Google Scholar
Skyrms, Brian (1987), “Dynamic Coherence and Probability Kinematics”, Dynamic Coherence and Probability Kinematics 54:120.Google Scholar
Skyrms, Brian (1996), “The Structure of Radical Probabilism”, The Structure of Radical Probabilism 45:285297.Google Scholar
Teller, Paul (1973), “Conditionalization and Observation”, Conditionalization and Observation 26:218258.Google Scholar
van Fraassen, Bas C. (1980), “Rational Belief and Probability Kinematics”, Rational Belief and Probability Kinematics 47:165187.Google Scholar
van Fraassen, Bas C. (1984), “Belief and the Will”, Belief and the Will 81:235256.Google Scholar
Wagner, Carl (2002), “Probability Kinematics and Commutativity”, Probability Kinematics and Commutativity 69:266278.Google Scholar