Proceedings of the Philosophy of Science Association 2002 (3):S112-S123 (2002)
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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Reconstructing the Past: Parsimony, Evolution, and Inference.Elliott Sober - 1992 - Philosophical Review 101 (3):725-729.
How to Tell When Simpler, More Unified, or Less Ad Hoc Theories Will Provide More Accurate Predictions.Malcolm Forster & Elliott Sober - 1994 - British Journal for the Philosophy of Science 45 (1):1-35.
Citations of this work BETA
Empiricism and/or Instrumentalism?Prasanta S. Bandyopadhyay, Mark Greenwood, Gordon Brittan & Ken A. Aho - 2014 - Erkenntnis 79 (S5):1019-1041.
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.
Chimpanzee Mindreading and the Value of Parsimonious Mental Models.Hayley Clatterbuck - 2015 - Mind and Language 30 (4):414-436.
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