Randomness and initial segment complexity for probability measures
Andre Nies, Frank Stephan

TL;DR
This paper explores a weak form of algorithmic randomness for probability measures on Cantor space, linking initial segment complexity growth to measure randomness properties and examining triviality and ergodic measures.
Contribution
It introduces a new weak randomness notion for measures, connects it to initial segment complexity growth, and analyzes its relation to classical randomness and triviality notions.
Findings
Maximal growth of complexity implies weak randomness.
Full Martin-Löf randomness of a measure implies ML absolute continuity.
The converse of this implication does not hold due to atoms in measures.
Abstract
We study algorithmic randomness properties for probability measures on Cantor space. We say that a measure on the space of infinite bit sequences is ML absolutely continuous if the non-ML-random bit sequences form a null set with respect to~. We think of this as a weak randomness notion for measures. We begin with examples, and provide a robustness property related to Solovay tests. Our main work connects our weak randomness notion to the growth of the initial segment complexity for measures~; the latter is defined as a -average over the complexity of strings of the same length. We show that a maximal growth implies our weak randomness property, but also that both implications of the Levin-Schnorr theorem fail. We discuss -triviality and -triviality for measures and relate these two notions with each other. Here triviality means that the growth of initial…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · semigroups and automata theory · Benford’s Law and Fraud Detection
