Model structure adequacy analysis: Selecting models on the basis of their ability to answer scientific questions
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Synthese 163 (3):357 - 370 (2008)
Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.
|Keywords||Model selection Model identification Model development Multi-model inference Bull trout Salvelinus confluentus Monitoring|
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References found in this work BETA
Deborah G. Mayo (2001). Error and the Growth of Experimental Knowledge. International Studies in the Philosophy of Science 15 (1):455-459.
Ronald N. Giere (2004). How Models Are Used to Represent Reality. Philosophy of Science 71 (5):742-752.
Citations of this work BETA
Deborah Mayo & Jean Miller (2008). The Error Statistical Philosopher as Normative Naturalist. Synthese 163 (3):305 - 314.
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