On a tangential note, "The robust beauty of improper linear models in decision making"[1] is probably one of my favorite papers on modeling and decision research.
> Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method; for example, they may be obtained on the basis of intuition, derived from simulating a clinical judge's predictions, or set to be equal. This article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors.