To continue with some philosophical reflections on the papers from the “Ockham’s razor” conference, let me respond to something in Shalizi’s recent comments (http://cscs.umich.edu/~crshalizi/weblog/). His emphasis on the interest in understanding processes and mechanisms, as opposed to mere prediction, seems exactly right. But he raises a question that seems to me simply answered (on grounds of evidence): If “a model didn’t seem to need” a mechanism, it is left out, why?
“It’s this, the leave-out-processes-you-don’t-need, which seems to me the core of the Razor for scientific model-building. This is definitely not the same as parameter-counting, and I think it’s also different from capacity control and even from description-length-measuring (cf.), though I am open to Peter persuading me otherwise. I am not, however, altogether sure how to formalize it, or what would justify it, beyond an aesthetic preference for tidy models. (And who died and left the tidy-minded in charge?) The best hope for such justification, I think, is something like Kevin’s idea that the Razor helps us get to the truth faster, or at least with fewer needless detours. Positing processes and mechanisms which aren’t strictly called for to account for the phenomena is asking for trouble needlessly.”
But it is easy to see that if a model M is adequate for data x regarding an aspect of a phenomenon (i.e., M had passed reasonably severe tests with x) , then a model M’ that added an “unnecessary” mechanism would have passed with very low severity, or, if one prefers, M’ would be very poorly corroborated. To justify “leaving-out-processes-you-don’t-need” then, the appeal is not to aesthetics or heuristics but to the severity or well-testedness of M and M’.
Error Statistics Philosophy 