Resumen
The fundamental difficulty with inference in nontrivial extrapolation where model selection is involved from a rich space of models is that any model estimated in one regime used for decision making in another is fundamentally confounded with disruptive alternatives. These are alternative models which if true would support a diametrically opposed action from the one the estimated model supports. One strategy to support extrapolation and reduce arbitrary fitting and confounding is to force the model to derive from the same mathematical structure that underlies the substantive science appropriate for the phenomena. Then statistical model fitting follows the form of theory generation in artificial intelligence, with statistical model selection tools and the statistician taking the place of the inference engine. However, the problem of confounding still remains. Activity mapping is a recent development which, for any given specific form of alternative kinetic model, identifies the confounded models having that form. In this paper, it is shown how activity maps may be used to help design experiments with improved power for detecting confounding disruptive alternatives in the context of fitting kinetic models to material degradation or failure data with the goal of extrapolation.