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Simplicity and the Sub-Family Problem for Model Selection

Published online by Cambridge University Press:  25 May 2022

Alireza Fatollahi  and
Kasra Alishahi
Alireza Fatollahi*
Affiliation:
Department of Philosophy, Princeton University, Princeton, USA
Kasra Alishahi*
Affiliation:
Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
*
*Corresponding author. Emails: alireza.fatollahi@gmail.com, alishahikasra@gmail.com
*Corresponding author. Emails: alireza.fatollahi@gmail.com, alishahikasra@gmail.com
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Abstract

Forster and Sober (1994) introduced the “sub-family problem” for model selection criteria that recommend balancing goodness-of-fit against simplicity. This problem arises when a maximally simple model (family of hypotheses) is artificially constructed to have excellent fit with the data. We argue that the problem arises because of a violation of the general maxim that balancing goodness-of-fit against simplicity leads to desirable inferences only if one is comparing models for the consideration of which one has a positive reason independently of the current data.

Type
Article
Information
Philosophy of Science , Volume 90 , Issue 3 , July 2023 , pp. 648 - 668
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Philosophy of Science Association

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References

Akaike, Hirotugu. 1973. “Information Theory as an Extension of the Maximum Likelihood Principle.” In Second International Symposium on Information Theory, edited by Petrov, B. N. and Csaki, F., 267–81. Budapest: Akademiai Kiado.Google Scholar
Douglas, Heather, and Magnus, P.D.. 2013. “State of the Field: Why Novel Prediction Matters.” Studies in the History and Philosophy of Science 44 (4):580–89.10.1016/j.shpsa.2013.04.001CrossRefGoogle Scholar
Forster, Malcolm R. 1995. “The Golfer’s Dilemma: A Reply to Kukla on Curve-Fitting.” British Journal for the Philosophy of Science 46 (3):348–60.10.1093/bjps/46.3.348CrossRefGoogle Scholar
Forster, Malcolm, R. 2006. “A Philosopher’s Guide to Empirical Success.” Philosophy of Science 74 (5):588–600.10.1086/525606CrossRefGoogle Scholar
Forster, Malcolm R., and Sober, Elliott. 1994. “How to Tell when Simpler, More United, or Less Ad Hoc Theories Will Provide More Accurate Predictions.” British Journal for the Philosophy of Science 45 (1):1–35.10.1093/bjps/45.1.1CrossRefGoogle Scholar
Forster Malcolm, R., and Sober, Elliott. 2011. “AIC Scores as Evidence – a Bayesian Interpretation.” In Philosophy of Statistics, edited by Prasanta, S. Bandyopadhyay and Malcolm, R. Forster, 535–49. Amsterdam: Elsevier.10.1016/B978-0-444-51862-0.50016-2CrossRefGoogle Scholar
Giraud, Christophe. 2015. Introduction to High-Dimensional Statistics. Boca Raton, FL: CRC Press.Google Scholar
Hájek, Alan. 2007. “The Reference Class Problem Is Your Problem Too.” Synthese 156 (3):563–85.10.1007/s11229-006-9138-5CrossRefGoogle Scholar
Kukla, André. 1995. “Forster and Sober on the Curve-Fitting Problem.” British Journal for the Philosophy of Science 46 (2):248–52.10.1093/bjps/46.2.248CrossRefGoogle Scholar
Sakamoto, Y., Ishiguro, M., and Kitagawa, G.. 1986. Akaike Information Criterion Statistics. Tokyo: KTK Scientific Publishers.Google Scholar
Steele, Katie, and Werndl, Charlotte. 2018. “Model-Selection Theory: The Need for a More Nuanced Picture of Use-Novelty and Double-Counting.” British Journal for the Philosophy of Science 69 (2):351–75.10.1093/bjps/axw024CrossRefGoogle ScholarPubMed