Model-Selection Theory: The Need for a More Nuanced Picture of Use-Novelty and Double-Counting
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This paper argues that common intuitions regarding a) the specialness of "use-novel" data for confirmation, and b) that this specialness implies the "no-double-counting rule", which says that data used in "constructing" (calibrating) a model cannot also play a role in confirming the model's predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in light of prominent accounts of confirmation of model predictions. We show that, on the Bayesian account of confirmation, and also on the standard Classical hypothesis-testing account, claims a) and b) are not generally true, but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialised Classical model-selection methods, on the other hand, uphold a nuanced version of claim a), but this comes apart from b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised.