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Mathematical arguments against Occam's razor

The no free lunch (NFL) theorems for inductive inference prove that Occam's razor must rely on ultimately arbitrary assumptions concerning the prior probability distribution found in our world. Specifically, suppose one is given two inductive inference algorithms, A and B, where A is a Bayesian procedure based on the choice of some prior distribution motivated by Occam's razor (e.g., the prior might favor hypotheses with smaller Kolmogorov complexity). Suppose that B is the anti-Bayes procedure, which calculates what the Bayesian algorithm A based on Occam's razor will predict – and then predicts the exact opposite. Then there are just as many actual priors (including those different from the Occam's razor prior assumed by A) in which algorithm B outperforms A as priors in which the procedure A based on Occam's razor comes out on top. In particular, the NFL theorems show that the "Occam factors" Bayesian argument for Occam's razor must make ultimately arbitrary modeling assumptions.