How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions
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 45 (1):1-35 (1994)
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 , 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. * Both of us gratefully acknowledge support from the Graduate School at the University of Wisconsin-Madison, and NSF grant DIR-8822278 (M.F.) and NSF grant SBE-9212294 (E.S.). Special thanks go to A. W. F. Edwards.William Harper. Martin Leckey. Brian Skyrms, and especially Peter Turney for helpful comments on an earlier draft.
|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
Michael Huemer (2009). When is Parsimony a Virtue? Philosophical Quarterly 59 (235):216-236.
Samuel Schindler (2013). Theory-Laden Experimentation. Studies in History and Philosophy of Science Part A 44 (1):89-.
Michael Morreau (2013). Mr. Fit, Mr. Simplicity and Mr. Scope: From Social Choice to Theory Choice. Erkenntnis:1-16.
Sungsu Kim (2011). Multiple Realization and Evidence. Philosophical Psychology 24 (6):739 - 749.
Malcolm Forster (2007). A Philosopher's Guide to Empirical Success. Philosophy of Science 74 (5):588-600.
Similar books and articles
Abraham Meidan & Boris Levin (2002). Choosing From Competing Theories in Computerised Learning. Minds and Machines 12 (1):119-129.
Prasanta S. Bandyopadhyay & Robert J. Boik (1999). The Curve Fitting Problem: A Bayesian Rejoinder. Philosophy of Science 66 (3):402.
Aris Spanos (2007). Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach. Philosophy of Science 74 (5):1046-1066.
Nicholas Maxwell, Simplicity. PhilSci Archive.
Elliott Sober (2001). Instrumentalism Revisited. The Proceedings of the Twentieth World Congress of Philosophy 2001 (91):3 - 39.
Peter Turney (1990). The Curve Fitting Problem: A Solution. British Journal for the Philosophy of Science 41 (4):509-530.
Malcolm R. Forster (1994). Non-Bayesian Foundations for Statistical Estimation, Prediction, and the Ravens Example. Erkenntnis 40 (3):357 - 376.
Scott DeVito (1997). A Gruesome Problem for the Curve-Fitting Solution. British Journal for the Philosophy of Science 48 (3):391-396.
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 downloads115 ( #13,985 of 1,696,808 )
Recent downloads (6 months)30 ( #15,889 of 1,696,808 )
How can I increase my downloads?