Algorithmic Information Theory and Foundations of Probability

TL;DR

This paper explores how algorithmic information theory, particularly Kolmogorov complexity, can clarify the connection between mathematical probability and real-world phenomena.

Contribution

It introduces a framework linking algorithmic complexity with probability theory to better understand their foundational relationship.

Findings

Provides a theoretical basis for probability using Kolmogorov complexity

Bridges the gap between abstract probability and practical applications

Suggests new perspectives on the foundations of probability theory

Abstract

The use of algorithmic information theory (Kolmogorov complexity theory) to explain the relation between mathematical probability theory and `real world' is discussed.