How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions

Abstract
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 [1973], 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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DOI 10.1093/bjps/45.1.1
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Climate Models, Calibration, and Confirmation.K. Steele & C. Werndl - 2013 - British Journal for the Philosophy of Science 64 (3):609-635.
How to Split a Theory: Defending Selective Realism and Convergence Without Proximity.D. Harker - 2013 - British Journal for the Philosophy of Science 64 (1):79-106.
When is Parsimony a Virtue?Michael Huemer - 2009 - Philosophical Quarterly 59 (235):216-236.
Why Simpler Arguments Are Better.Moti Mizrahi - 2016 - Argumentation 30 (3):247-261.

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