Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-26T03:54:30.841Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning Theory and the Philosophy of Science

Published online by Cambridge University Press:  01 April 2022

Kevin T. Kelly ,
Oliver Schulte  and
Cory Juhl
Kevin T. Kelly*
Affiliation:
Department of Philosophy, Carnegie Mellon University
Oliver Schulte*
Affiliation:
Department of Philosophy, University of Alberta
Cory Juhl*
Affiliation:
Department of Philosophy, University of Texas, Austin
*
‡Send reprint requests to the senior author, Department of Philosophy, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213; e-mail kk3n@andrew.cmu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper places formal learning theory in a broader philosophical context and provides a glimpse of what the philosophy of induction looks like from a learning-theoretic point of view. Formal learning theory is compared with other standard approaches to the philosophy of induction. Thereafter, we present some results and examples indicating its unique character and philosophical interest, with special attention to its unified perspective on inductive uncertainty and uncomputability.

Type
Research Article
Information
Philosophy of Science , Volume 64 , Issue 2 , June 1997 , pp. 245 - 267
Copyright
Copyright © 1997 by the Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

We are indebted to Clark Glymour and Teddy Seidenfeld for substantial comments on earlier drafts.

References

DeFinetti, B. (1972), Probability, Induction and Statistics. New York: Wiley.Google Scholar
Diaconis, P. and Freedman, D. (1986), “On the Consistency of Bayes Estimates”, Annals of Statistics 14: 1–26.Google Scholar
Dorling, J. (1979), “Bayesian Personalism, the Methodology of Scientific Research Programs, and Duhem's Problem”, Studies in the History and Philosophy of Science 10: 177–187.CrossRefGoogle Scholar
Earman, J. (1992), Bayes or Bust? Cambridge, MA: MIT Press.Google Scholar
Feynman, R. (1994), The Character of Physical Law. Cambridge, MA: MIT Press.Google Scholar
Fishburn, P. C. (1981), “Subjective Expected Utility: A Review of Normative Theories”, Theory and Decision 13: 139–199.CrossRefGoogle Scholar
Gaifman, H. and Snir, M. (1982), “Probabilities over Rich Languages, Testing and Randomness”, Journal of Symbolic Logic 47: 495–548.CrossRefGoogle Scholar
Garber, D. (1983), “Old Evidence and Logical Omniscience in Bayesian Confirmation Theory”, in Earman, J. (ed.), Testing Scientific Theories: Minnesota Studies in the Philosophy of Science, 10. Minneapolis: University of Minnesota Press, pp. 99–133.Google Scholar
Gärdenfors, P. (1988), Knowledge in Flux. Cambridge, MA: MIT Press.Google Scholar
Glymour, C. (1980), Theory and Evidence. Princeton: Princeton University Press.Google Scholar
Gold, E. M. (1967), “Language Identification in the Limit”, Information and Control 10: 447–474.CrossRefGoogle Scholar
Goldman, A. (1986), Epistemology and Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Good, I. J. (1983), Good Thinking. Minneapolis: University of Minnesota Press.Google Scholar
Halmos, P. (1974), Measure Theory. New York: Springer.Google Scholar
Harper, W. and DiSalle, R. (1986), “Inferences from Phenomena in Gravitational Physics”, in Darden, L. (ed.), Proceedings of the 1996 Biennial Meeting of the Philosophy of Science Association. Chicago: University of Chicago Press, pp. S46–S54.Google Scholar
Hawthorne, J. (1993), “Bayesian Induction is Eliminative Induction”, Unpublished manuscript.CrossRefGoogle Scholar
Hempel, C. G. (1966), Philosophy of Natural Science. New York: Prentice Hall.Google Scholar
Howson, C. and Urbach, P. (1990), Scientific Reasoning: The Bayesian Approach. LaSalle: Open Court.Google Scholar
Juhl, C. (1993), “Bayesianism and Reliable Scientific Inquiry”, Philosophy of Science 60: 302–319.CrossRefGoogle Scholar
Juhl, C. (1994), “The Speed-Optimality of Reichenbach's Straight Rule of Induction”, British Journal for the Philosophy of Science 45: 857–863.CrossRefGoogle Scholar
Juhl, C. (1995), “Is Gold-Putnam Diagonalization Complete?”, Journal of Philosophical Logic 24: 117–138.CrossRefGoogle Scholar
Juhl, C. (1997), “Objectively Reliable Subjective Probabilities”, forthcoming in Synthese.CrossRefGoogle Scholar
Kearns, M. and Vazirani, U. (1994), An Introduction to Computational Learning Theory. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Kelly, K. (1991), “Reichenbach, Induction and Discovery”, Erkenntnis 35: 123–149.Google Scholar
Kelly, K. (1996), The Logic of Reliable Inquiry. New York: Oxford University Press.Google Scholar
Kelly, K. and Glymour, C. (1990), “Theory Discovery from Data with Mixed Quantifiers”, Journal of Philosophical Logic 19: 1–33.CrossRefGoogle Scholar
Kelly, K. and Glymour, C. (1992), “Inductive Inference from Theory-Laden Data”, Journal of Philosophical Logic 21: 391–444.CrossRefGoogle Scholar
Kelly, K., Juhl, C., and Glymour, C. (1994), “Reliability, Realism and Relativism”, in Clark, P. and Hale, B. (eds.), Reading Putnam. London: Blackwell, pp. 98–161.Google Scholar
Kelly, K. and Schulte, O. (1996), “The Computable Testability of Theories Making Uncomputable Predictions”, Erkenntnis 42: 29–66.Google Scholar
Kelly, K. and Schulte, O. (1996), “Church's Thesis and Hume's Problem”, forthcoming in Logic, Methodology, and Philosophy of Science X, Dordrecht: Kluwer.Google Scholar
Kelly, K., Schulte, O., and Hendricks, V. (1996), “Reliable Belief Revision”, forthcoming in Logic, Methodology, and Philosophy of Science X, Dordrecht: Kluwer.Google Scholar
Kitcher, P. (1993), The Advancement of Science. New York: Oxford.Google Scholar
Kuhn, T. (1970), The Structure of Scientific Revolutions. Chicago: University of Chicago Press.Google Scholar
Levi, I. (1983), The Enterprise of Knowledge. Cambridge, MA: MIT Press.Google Scholar
Levi, I. (1991), The Fixation of Belief and its Undoing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Maher, P. (1996), “Subjective and Objective Confirmation”, Philosophy of Science 63: 149–174.CrossRefGoogle Scholar
Mayo, D. (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Nozick, R. (1981), Philosophical Explanations. Cambridge, MA: Harvard University Press.Google Scholar
Osherson, D. and Martin, E. (1997), “Scientific Discovery Based on Belief Revision”, Journal of Symbolic Logic, forthcoming.Google Scholar
Osherson, D., Stob, M., and Weinstein, S. (1986), Systems that Learn. Cambridge, MA: MIT Press.Google Scholar
Osherson, D. and Weinstein, S. (1988), “Mechanical Learners Pay a Price for Bayesianism”, Journal of Symbolic Logic 53: 1245–1252.CrossRefGoogle Scholar
Poincare, H. (1952), Science and Hypothesis. New York: Dover.Google Scholar
Putnam, H. (1963), “Degree of Confirmation and Inductive Logic”, in Schilpp, A. (ed.), The Philosophy of Rudolph Carnap. LaSalle: Open Court, pp. 761–783.Google Scholar
Schervish, M., Seidenfeld, T., and Kadane, J. (1990), “An Approach to Consensus and Certainty with Increasing Evidence”, Journal of Statistical Planning and Inference 25: 401–414.CrossRefGoogle Scholar
Schulte, O. (1996), Hard Choices in Scientific Inquiry. Doctoral thesis, Department of Philosophy, Carnegie Mellon University.Google Scholar