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.
Additional Metadata
THEME Information (theme 2)
Publisher The University of Chicago Press
Persistent URL dx.doi.org/10.1086/687257
Journal Philosophy of Science
Project Safe Statistics
Citation
Sterkenburg, T.F. (2016). Solomonoff Prediction and Occam's Razor. Philosophy of Science, 83(4), 459–479. doi:10.1086/687257