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Bayesianism and Reliable Scientific Inquiry

Published online by Cambridge University Press:  01 April 2022

Cory Juhl
Cory Juhl*
Affiliation:
Department of Philosophy, University of Texas at Austin
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Abstract

The inductive reliability of Bayesian methods is explored. The first result presented shows that for any solvable inductive problem of a general type, there exists a subjective prior which yields a Bayesian inductive method that solves the problem, although not all subjective priors give rise to a successful inductive method for the problem. The second result shows that the same does not hold for computationally bounded agents, so that Bayesianism is “inductively incomplete” for such agents. Finally a consistency proof shows that inductive agents do not need to disregard inductive failure on sets of subjective probability 0 in order to be ideally rational. Together the results reveal the inadequacy of the subjective Bayesian norms for scientific methodology.

Type
Research Article
Information
Philosophy of Science , Volume 60 , Issue 2 , June 1993 , pp. 302 - 319
Copyright
Copyright © Philosophy of Science Association 1993

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Footnotes

†

Kevin Kelly and Teddy Seidenfeld provided helpful suggestions for the construction of the proof of Theorem 1. Many helpful discussions with Kevin Kelly related to all of the issues treated in the paper, especially the material on dominance and the axioms of rational preference. Clark Glymour and Kevin Kelly read earlier drafts of the paper and made many helpful suggestions for improvement.

‡

Send reprint requests to the author, Department of Philosophy, 316 Waggener Hall, University of Texas at Austin, Austin, TX 78712, USA.

References

Gaifman, H. and Snir, M. (1982), “Probabilities Over Rich Languages, Testing and Randomness”, Journal of Symbolic Logic 47: 495–549.CrossRefGoogle Scholar
Garber, D. (1983), “Old Evidence and Logical Omniscience in Bayesian Confirmation Theory”, in Earman, J. (ed.), Testing Scientific Theories. Minneapolis: University of Minnesota Press, pp. 99–131.Google Scholar
Halmos, P. (1950), Measure Theory. New York: Springer-Verlag.CrossRefGoogle Scholar
Hesse, M. (1974), The Structure of Scientific Inference. Berkeley and Los Angeles: University of California Press.CrossRefGoogle Scholar
Kelly, K. (1990), “Effective Epistemology, Psychology and Artificial Intelligence”, in Sieg, W. (ed.), Acting and Reflecting: The Interdisciplinary Turn in Philosophy. Dordrecht: Kluwer, pp. 115–126.CrossRefGoogle Scholar
Kelly, K. (forthcoming), “Learning Theory and Descriptive Set Theory”, Logic and Computation.Google Scholar
Kelly, K.; Juhl, C.; Glymour, C. (forthcoming), “Reliability, Realism, and Relativism”, in P. Clark (ed.), The Gifford Lectures in Honor of Hilary Putnam.Google Scholar
Levi, I. (1990), “Rationally Unbound”, in Sieg, W. (ed.), Acting and Reflecting: The Interdisciplinary Turn in Philosophy. Dordrecht: Kluwer, pp. 211–221.CrossRefGoogle Scholar
Osherson, D.; Stob, M.; and Weinstein, S. (1988), “Mechanical Learners Pay a Price for Bayesianism”, Journal of Symbolic Logic 53: 1245–1251.CrossRefGoogle Scholar
Putnam, H. (1975), “Probability and Confirmation”. Reprinted in Philosophical Papers. Vol. 1, Mathematics, Matter, and Method. New York: Cambridge University Press, pp. 293–304.Google Scholar
Savage, L. (1972), Foundations of Statistics. New York: Dover.Google Scholar