Use-novelty and double-counting: new insights from model selection theory - a podcast by MCMP Team

Charlotte Werndl (Salzburg) gives a talk at the MCMP Colloquium (27 November, 2014) titled "Use-novelty and double-counting: new insights from model selection theory". Abstract: A widely debated issue on confirmation is the requirement of use-novelty (i.e. that data can only confirm models if they have not already been used before, e.g. for calibrating parameters). This paper investigates the issue of use-novelty inthe context of the mathematical methods provided by model selection theory. I will show that the picture model selection theory presents us with about use-novelty is more subtle and nuanced than the commonly endorsed positions by climate scientists and philosophers. More specifically, I will argue that there are two main cases in model selection theory. On the one hand, there are the methods such as cross-validation where the data are required to be use-novel. On the other hand, there are the methods such as the Akaike Information Criterion (AIC) for which the data cannot be use-novel. Still, for some of these methods (like AIC) certain intuitions behind the use-novelty approach are preserved: there is a penalty term in the expression for the degree of confirmation by the data because the data have already been used for calibration. Finally, this picture presented by model selection theory will be compared to the conclusions drawn about use-novelty by Bayesians and proponents of the use-novelty approach.

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