Solomonoff induction
Formal Languages and Automata Theory
2026-03-24 v1 Machine Learning
Abstract
This chapter discusses the Solomonoff approach to universal prediction. The crucial ingredient in the approach is the notion of computability, and I present the main idea as an attempt to meet two plausible computability desiderata for a universal predictor. This attempt is unsuccessful, which is shown by a generalization of a diagonalization argument due to Putnam. I then critically discuss purported gains of the approach, in particular it providing a foundation for the methodological principle of Occam's razor, and it serving as a theoretical ideal for the development of machine learning methods.
Cite
@article{arxiv.2603.20274,
title = {Solomonoff induction},
author = {Tom F. Sterkenburg},
journal= {arXiv preprint arXiv:2603.20274},
year = {2026}
}