Rochefort-Maranda, Guillaume (2016) Simplicity and model selection. European Journal for Philosophy of Science. ISSN 1879-4912

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Abstract

In this paper I compare parametric and nonparametric regression models with the help of a simulated data set. Doing so, I have two main objectives. The first one is to differentiate five concepts of simplicity and assess their respective importance. The second one is to show that the scope of the existing philosophical literature on simplicity and model selection is too narrow because it does not take the nonparametric approach into account, S112–S123, 2002; Forster and Sober in The British Journal for the Philosophy of Science 45, 1–35, 1994; Forster, 2001, in Philosophy of Science 74, 588–600, 2007; Hitchcock and Sober in The British Journal for the Philosophy of Science 55, 1–34, 2004; Mikkelson in Philosophy of Science 73, 440–447, 2006; Baker 2013). More precisely, I point out that a measure of simplicity in terms of the number of adjustable parameters is inadequate to characterise nonparametric models and to compare them with parametric models. This allows me to weed out false claims about what makes a model simpler than another. Furthermore, I show that the importance of simplicity in model selection cannot be captured by the notion of parametric simplicity. ‘Simplicity’ is an umbrella term. While parametric simplicity can be ignored, there are other notions of simplicity that need to be taken into consideration when we choose a model. Such notions are not discussed in the previously mentioned literature. The latter therefore portrays an incomplete picture of why simplicity matters when we choose a model. Overall I support a pluralist view according to which we cannot give a general and interesting justification for the importance of simplicity in science.

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Item Type:	Published Article or Volume
Creators:	Creators Email ORCID Rochefort-Maranda, Guillaume guillaumemaranda@hotmail.com
Keywords:	simplicity; philosophy of science; model selection
Subjects:	General Issues > Data General Issues > Confirmation/Induction General Issues > Formal Learning Theory Specific Sciences > Probability/Statistics
Depositing User:	Dr. Guillaume Rochefort-Maranda
Date Deposited:	27 Aug 2017 14:09
Last Modified:	27 Aug 2017 14:09
Item ID:	13364
Journal or Publication Title:	European Journal for Philosophy of Science
Publisher:	Springer
DOI or Unique Handle:	https://doi.org/10.1007/s13194-016-0137-1
Subjects:	General Issues > Data General Issues > Confirmation/Induction General Issues > Formal Learning Theory Specific Sciences > Probability/Statistics
Date:	May 2016
ISSN:	1879-4912
URI:	https://philsci-archive.pitt.edu/id/eprint/13364

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