Benford's Law from Turing Ensembles and Integer Partitions
Abstract
We develop two complementary generative mechanisms that explain when and why Benford's first-digit law arises. First, a probabilistic Turing machine (PTM) ensemble induces a geometric law for codelength. Maximizing its entropy under a constraint on halting length yields Benford statistics. This model shows a phase transition with respect to the halt probability. Second, a constrained partition model (Einstein-solid combinatorics) recovers the same logarithmic profile as the maximum-entropy solution under a coarse-grained entropy-rate constraint, clarifying the role of non-ergodicity (ensemble vs. trajectory averages). We also perform numerical experiments that corroborate our conclusions.
Cite
@article{arxiv.2502.16314,
title = {Benford's Law from Turing Ensembles and Integer Partitions},
author = {Alexander Kolpakov and Aidan Rocke},
journal= {arXiv preprint arXiv:2502.16314},
year = {2025}
}
Comments
10 pages, 2 figures; GitHub repository https://github.com/sashakolpakov/benford-experiment