Coarse-grained Shannon entropy of random walks with shrinking steps

TL;DR

This paper investigates the Shannon entropy of one-dimensional random walks with geometrically shrinking steps, revealing a maximum at the dyadic ratio 1/2 due to the interplay of diffusive spreading and fractal structures, with implications for biophysical models.

Contribution

It provides analytical and numerical evidence that the Shannon entropy of Bernoulli convolution-induced distributions peaks at the dyadic ratio 1/2, linking non-Gaussian noise to entropy behavior.

Findings

Entropy peaks at the dyadic ratio 1/2.

Competition between diffusion and fractal structure influences entropy.

Implications for models of protocell self-replication.

Abstract

In one-dimensional diffusive processes with discrete steps characterized by geometrically decaying magnitudes, the usual Gaussian broadening familiar from Brownian motion is replaced by bounded probability distributions over particle positions that are characterized by multi-scale fractal structures. In this work, we study random walks with shrinking steps (known as Bernoulli convolutions), focusing on their behavior in the vicinity of the dyadic contraction ratio 1/2. Our analytical and numerical results show that the coarse-grained Shannon entropy of particle distributions induced by Bernoulli convolutions exhibits a local maximum at the dyadic ratio, arising from the competition between diffusive spreading, which increases entropy, and emergent fine structure, which tends to decrease it. This entropy maximum is a general property of systems driven by non-Gaussian discrete noise, whose dynamics near stable fixed points can be viewed as an autoregressive process - an approximation that is mathematically equivalent to unbiased random walks with shrinking steps. We discuss potential implications of Bernoulli convolution dynamics for protocell self-replication and vesicle proliferation, establishing a link between our information-theoretic approach and biophysical models of cell division.