A few notes on multiple theories and conceptual jump size

Abstract

These are a few notes about some of Ray Solomonoff’s foundational work in algorithmic probability, focussing on the universal prior and conceptual jump size, including a few illustrations of how he thought. His induction theory gives a way to compare the likelihood of different theories describing observations. He used Bayes’ rule of causation to discard theories inconsistent with the observations. Can we find good theories? Lsearch may give a way to search and the conceptual jump size a measure for this.