Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T02:39:22.368Z Has data issue: false hasContentIssue false

A Philosopher's Guide to Empirical Success

Published online by Cambridge University Press:  01 January 2022

Abstract

The simple question, what is empirical success? turns out to have a surprisingly complicated answer. We need to distinguish between meritorious fit and ‘fudged fit’, which is akin to the distinction between prediction and accommodation. The final proposal is that empirical success emerges in a theory dependent way from the agreement of independent measurements of theoretically postulated quantities. Implications for realism and Bayesianism are discussed.

Type
Philosophy of Science
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This paper was written when I was a visiting fellow at the Center for Philosophy of Science at the University of Pittsburgh; I thank everyone for their support.

References

Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle”, in Petrov, B. N. and Csaki, F. (eds.), Second International Symposium on Information Theory. Budapest: Akademiai Kiado, 267281.Google Scholar
Forster, Malcolm R. (1988), “Unification, Explanation, and the Composition of Causes in Newtonian Mechanics”, Unification, Explanation, and the Composition of Causes in Newtonian Mechanics 19:55101.Google Scholar
Forster, Malcolm R. (2002), “Predictive Accuracy as an Achievable Goal of Science,” Philosophy of Science 69:S124S134.CrossRefGoogle Scholar
Forster, Malcolm R. (2006), “Counterexamples to a Likelihood Theory of Evidence,” Mind and Machines 16:319338.CrossRefGoogle Scholar
Forster, Malcolm R., and Sober, Elliott (1994), “How to Tell When Simpler, More Unified, or Less Ad Hoc Theories Will Provide More Accurate Predictions”, How to Tell When Simpler, More Unified, or Less Ad Hoc Theories Will Provide More Accurate Predictions 45:135.Google Scholar
Harper, William L. (2002), “Howard Stein on Isaac Newton: Beyond Hypotheses”, in Malament, David B. (ed.), Reading Natural Philosophy: Essays in the History and Philosophy of Science and Mathematics. La Salle, IL: Open Court, 71112.Google Scholar
Harper, William L. (2007), “Newton’s Methodology and Mercury’s Perihelion Before and After Einstein.Philosophy of Science, in this issue.CrossRefGoogle Scholar
Hitchcock, Christopher R., and Sober, Elliott (2004), “Prediction versus Accommodation and the Risk of Overfitting”, Prediction versus Accommodation and the Risk of Overfitting 55:134.Google Scholar
Kieseppä, I. A. (1997), “Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity”, Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity 48:2148.Google Scholar
Myrvold, Wayne, and Harper, William L. (2002), “Model Selection, Simplicity, and Scientific Inference”, Model Selection, Simplicity, and Scientific Inference 69:S135S149.Google Scholar
Norton, John D. (2000a), “The Determination of Theory by Evidence: The Case for Quantum Discontinuity, 1900–1915”, The Determination of Theory by Evidence: The Case for Quantum Discontinuity, 1900–1915 97:131.Google Scholar
Norton, John D. (2000b), “How We Know about Electrons”, in Nola, Robert and Sankey, Howard (eds.), After Popper, Kuhn and Feyerabend. Dordrecht: Kluwer, 6797.CrossRefGoogle Scholar
Popper, Karl (1959), The Logic of Scientific Discovery. London: Hutchinson.Google Scholar
Stone, M. (1977), “An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion”, An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion 39:4447.Google Scholar
van Fraassen, Bas (1980), The Scientific Image. Oxford: Oxford University Press.CrossRefGoogle Scholar