Algorithmic Complexity in Noise Induced Transport Systems

TL;DR

This paper investigates the algorithmic complexity of noise-induced transport systems, linking information theory with physical transport phenomena to quantify information transfer in computational models.

Contribution

It introduces the use of Kolmogorov complexity to measure information transfer in noise-induced transport systems, providing a novel quantitative approach.

Findings

Algorithmic complexity correlates with transport efficiency.

Noise-induced transport can be characterized by information content.

Quantitative measures of information transfer are established.

Abstract

Time correlated fluctuations interacting with a spatial asymmetry potential are sufficient conditions to give rise to transport of Brownian particles. The transfer of information coming from the nonequilibrium bath, viewed as a source of negentropy, give rise to the correlated noise. The algorithmic complexity of an object provides a means of quantitating its information contents. The Kolmogorov information entropy or algorithmic complexity is investigated in order to quantitate the transfer of information that occurs in computational models showing noise induced transport. The complexity is measured in terms of the average number of bits per time unit necessary to specify the sequence generated by the system.