David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
British Journal for the Philosophy of Science 41 (4):509-530 (1990)
Much of scientific inference involves fitting numerical data with a curve, or functional relation. The received view is that the fittest curve is the curve which best balances the conflicting demands of simplicity and accuracy, where simplicity is measured by the number ofparameters in the curve. The problem with this view is that there is no commonly accepted justification for desiring simplicity. This paper presents a measure of the stability of equations. It is argued that the fittest curve is the curve which best balances stability and accuracy. The received view is defended with a proof that simplicity corresponds to stability, for linear regression equations. 1This paper is based on part of my doctoral dissertation. My thanks go to my thesis supervisor Professor Alasdair Urquhart for his encouragement, constructive criticism, and for directing me to several relevant articles: to my advisor Professor Ian Hacking for reminding me to concentrate on results that might have some application in the real world; and to my friend Wendy Brandts for sharing her ideas on a closely related problem. My thanks also to an anonymous referee of The British Journal of the Philosophy of Science for several helpful comments, to my friends and family for unfailing support, and to the Social Sciences and Humanities Research Council (awards 452-86-5885 and 453-87-0513) and the University of Toronto for financial assistance.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
S. DeVito (1997). A Gruesome Problem for the Curve-Fitting Solution. British Journal for the Philosophy of Science 48 (3):391-396.
I. Kieseppa (1997). Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity. British Journal for the Philosophy of Science 48 (1):21-48.
Prasanta S. Bandyopadhyay & Robert J. Boik (1999). The Curve Fitting Problem: A Bayesian Rejoinder. Philosophy of Science 66 (3):402.
I. A. Kieseppä (1997). Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity. British Journal for the Philosophy of Science 48 (1):21-48.
Malcolm Forster & Elliott Sober (1994). How to Tell When Simpler, More Unified, or Less Ad Hoc Theories Will Provide More Accurate Predictions. British Journal for the Philosophy of Science 45 (1):1-35.
Aris Spanos (2007). Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach. Philosophy of Science 74 (5):1046-1066.
Scott DeVito (1997). A Gruesome Problem for the Curve-Fitting Solution. British Journal for the Philosophy of Science 48 (3):391-396.
Malcolm R. Forster (1995). The Golfer's Dilemma: A Reply to Kukla on Curve-Fitting. British Journal for the Philosophy of Science 46 (3):348-360.
Prasanta S. Bandyopadhayay, Robert J. Boik & Prasun Basu (1996). The Curve Fitting Problem: A Bayesian Approach. Philosophy of Science 63 (3):272.
André Kukla (1995). Forster and Sober on the Curve-Fitting Problem. British Journal for the Philosophy of Science 46 (2):248-252.
Added to index2009-01-28
Total downloads4 ( #267,530 of 1,101,833 )
Recent downloads (6 months)0
How can I increase my downloads?