Algorithmic information theory gives an idealized notion of compressibility that is often presented as an objective measure of simplicity. It is suggested at times that Solomonoff prediction, or algorithmic information theory in a predictive setting, can deliver an argument to justify Occam’s razor. This article explicates the relevant argument and, by converting it into a Bayesian framework, reveals why it has no such justificatory force. The supposed simplicity concept is better perceived as a specific inductive assumption, the assumption of effectiveness. It is this assumption that is the characterizing element of Solomonoff prediction and wherein its philosophical interest lies.
Information (theme 2)
The University of Chicago Press
dx.doi.org/10.1086/687257
Philosophy of Science
Safe Statistics
Algorithms and Complexity

Sterkenburg, T.F. (2016). Solomonoff Prediction and Occam's Razor. Philosophy of Science, 83(4), 459–479. doi:10.1086/687257