Discovering Patterns by Searching for Simplicity

I discuss the use of Kolmogorov complexity and Bayes’ theorem in Solomonoff’s inductive method to explicate a generM concept of simplicity. This makes it possible to understand how the search for simple, i.e., short, computational descriptions of (empirical) data yields to the discovery of patterns, and hence more probable predictions. I show how the simplicity bias of Langley’s BACON.2 and Thagard’s PI is subsumed by Rissanen’s Minimum Description Length principle, which is a computable approximation of Solomonoff’s uncomputable inductive method.