Authors
Elliott Sober
University of Wisconsin, Madison
Abstract
Akaike’s framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike’s framework helps provide instrumentalism with the epistemology it needs. Akaike’s criterion for model selection also throws light on the role of parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike’s framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered.
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DOI 10.1086/341839
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References found in this work BETA

The Scientific Image.C. Van Fraassen Bas - 1980 - Oxford University Press.
The Scientific Image.Michael Friedman - 1982 - Journal of Philosophy 79 (5):274-283.
The Scientific Image.William Demopoulos & Bas C. van Fraassen - 1982 - Philosophical Review 91 (4):603.
The Logic of Scientific Discovery.Karl Popper - 1959 - Studia Logica 9:262-265.

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Citations of this work BETA

Simplicity and Model Selection.Guillaume Rochefort-Maranda - 2016 - European Journal for Philosophy of Science 6 (2):261-279.
New Tools for Theory Choice and Theory Diagosis.John R. Welch - 2013 - Studies in History and Philosophy of Science Part A 44 (3):318-329.

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