| Peter Klein |
This looks like a mighty interesting conference:
Scientific theory choice is guided by judgments of simplicity, a bias frequently referred to as “Ockham’s Razor”. But what is simplicity and how, if at all, does it help science find the truth? Should we view simple theories as means for obtaining accurate predictions, as classical statisticians recommend? Or should we believe the theories themselves, as Bayesian methods seem to justify? The aim of this workshop is to re-examine the foundations of Ockham’s razor, with a firm focus on the connections, if any, between simplicity and truth.
The conference started yesterday; here’s a report on day 1 from Cosma Shalizi. Parsimony, for example, turns out to be more complicated than it appears; here is Shalizi on (recent University of Missouri visitor) Elliott Sober:
What he mostly addressed is when parsimony . . . ranks hypotheses in the same order as likelihood. . . . The conditions needed for parsimony and likelihood to agree are rather complicated and disjunctive, making parsimony seem like a mere short-cut or hack — if you think it should be matching likelihood. He was, however, clear in saying that he didn’t think hypotheses should always be evaluated in terms of likelihood alone. He ended by suggesting that “parsimony” or “simplicity” is probably many different things in many different areas of science (safe enough), and that when there is a legitimate preference for parsimony, it can be explained “reductively”, in terms of service to some more compelling goal than sheer simplicity.