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How to Tell When Simpler, More Unified, or Less Ad Hoc Theories will Provide More Accurate Predictions
Abstract
Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific realism.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Fraassen, B. C. V. (1980) · doi:10.1093/0198244274.001.0001 · Cited by 4,682
Conjectures and Refutations: The Growth of Scientific Knowledge
Popper, K. (2014) · doi:10.4324/9780203538074 · Cited by 327
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Boiteau, T. W., Malone, P. S., Peters, S. A., & Almor, A. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0031858 · Cited by 69
Marrying Fairness and Explainability in Supervised Learning
Grabowicz, P. A., Perello, N., & Mishra, A. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533236 · Cited by 37
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Metadata
- Authors
- FORSTER, M., & SOBER, E.
- Venue
- The British Journal for the Philosophy of Science
- Publisher
- University of Chicago Press
- Published
- 1994-03-01
- Volume
- 45
- Issue
- 1
- Pages
- 1-35
- ISSN
- 0007-0882 (ISSN-L)
- 1464-3537
- OpenAlex
- W1985257401
- Cited by
- 659
- References
- 3
- FWCI
- 37.88
- Type
- journal article
- Language
- en
- Open access
- closed
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