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
Ezio Di Nucci
Jack Alan Reynolds
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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.
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Citations of this work BETA
Moti Mizrahi (forthcoming). Why Simpler Arguments Are Better. Argumentation:1-15.
Stephan Hartmann & Matteo Colombo (forthcoming). Bayesian Cognitive Science, Unification and Explanation. British Journal for the Philosophy of Science:axv036.
Samuel Schindler (2013). Theory-Laden Experimentation. Studies in History and Philosophy of Science Part A 44 (1):89-101.
K. Brad Wray (2015). Pessimistic Inductions: Four Varieties. International Studies in the Philosophy of Science 29 (1):61-73.
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