Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-25T08:21:11.527Z Has data issue: false hasContentIssue false
  • English
  • Français

On Advancing Simple Hypotheses

Published online by Cambridge University Press:  01 April 2022

Daniel N. Osherson  and
Scott Weinstein
Daniel N. Osherson
Affiliation:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Scott Weinstein
Affiliation:
Department of Philosophy, University of Pennsylvania
Get access Rights & Permissions [Opens in a new window]

Abstract

We consider drawbacks to scientific methods that prefer simple hypotheses to complex ones that cover the same data. The discussion proceeds in the context of a precise model of scientific inquiry.

Type
Research Article
Information
Philosophy of Science , Volume 57 , Issue 2 , June 1990 , pp. 266 - 277
Copyright
Copyright © 1989 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

†

Support for this research was provided by the Office of Naval Research under contract No. N00014-87-K-0401. We thank two anonymous referees for helpful discussion.

References

REFERENCES

Angluin, D., and Smith, C. (1983) “Inductive Inference: Theory and Methods”, Computing Surveys 15: 237–269.CrossRefGoogle Scholar
Blumer, A., Ehrenfeucht, A., Haussler, D. and Warmuth, M. (1986), “Classifying Learnable Geometric Concepts with the Vapnik-Chervonenkis Dimension”, in Proc. 18th Symposium on Theory of Computing, Association for Computing Machinery, pp. 273–282.CrossRefGoogle Scholar
Chen, K-J. (1982), “Tradeoffs in the Inductive Inference of Nearly Minimal Size Programs”, Information & Control 52: 68–86.CrossRefGoogle Scholar
Glymour, C., Kelly, K., Osherson, D. and Weinstein, S. (forthcoming), Epistemology of Scientific Inquiry.Google Scholar
Goodman, N. (1966), The Structure of Appearance. 2nd edition. New York: Bobbs-Merrill.Google Scholar
Gold, E. M. (1967), “Language Identification in the Limit”, Information & Control 10: 447–474.CrossRefGoogle Scholar
Kugel, P. (1977), “Induction: Pure and Simple”, Information & Control 35(4): 276–336.CrossRefGoogle Scholar
Osherson, D., Weinstein, S. and Stob, M. (1986), Systems That Learn. Cambridge, MA: MIT Press.Google Scholar
Putnam, H. (1975), “Probability and Confirmation”, in Mathematics, Matter and Method. Cambridge: Cambridge University Press.Google Scholar
Rogers, H. (1967), Theory of Recursive Functions and Effective C omputability. New York: McGraw-Hill.Google Scholar
Sober, E. (1975), Simplicity. Oxford: Oxford University Press.CrossRefGoogle Scholar
Valiant, L. (1984), “A Theory of the Learnable”, Communications of the Association for Computing Machinery 27: 1134–1142.CrossRefGoogle Scholar