This is wonderful.

Although it doesn't fit, for some reason this reminds me of Robin Hanson's cognitive tactic of collecting a set of stylized facts (this certainly seems like a useful one) about a field and then trying to come up with simple models which fit those stylized facts.

Perhaps what these have in common is that they both focus on eliminating lots of wrong models from a big pool rather than trying to choose the best model between a small pool (which is what most statistical techniques focus on).

Edit: I think their similarity has more to do with that they both use high level facts to eliminate and suggest classes of models.