Model selection, simplicity, and scientific inference


Authors
William L. Harper
University of Western Ontario
Abstract
The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation.
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DOI 10.1086/341841
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References found in this work BETA

Akaike Information Criterion, Curve-Fitting, and the Philosophical Problem of Simplicity.I. Kieseppa - 1997 - British Journal for the Philosophy of Science 48 (1):21-48.
Instrumentalism, Parsimony, and the Akaike Framework.Elliott Sober - 2002 - Philosophy of Science 69 (S3):S112-S123.
"From the Phenomena of Motions to the Forces of Nature": Hypothesis or Deduction?Howard Stein - 1990 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:209 - 222.

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Citations of this work BETA

A Philosopher’s Guide to Empirical Success.Malcolm R. Forster - 2007 - Philosophy of Science 74 (5):588-600.
Evidentiary Inference in Evolutionary Biology.James Justus - 2011 - Biology and Philosophy 26 (3):419-437.

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