Skepsis on the scenario of Biological Evolution provided by stochastic models

TL;DR

This paper investigates whether stochastic models, which produce power law distributions and long-range correlations, genuinely represent biological evolution, using nonextensive statistics to analyze the randomness of these processes.

Contribution

It introduces a general analysis of stochastic models in biological evolution using Tsallis' nonextensive statistics to assess their randomness and relevance.

Findings

Power law distributions are linked to long-range correlations in biological systems.

The study questions the true randomness of processes modeled by stochastic approaches.

Implications for understanding biological evolution mechanisms.

Abstract

Stochastic models, based on random processes, may lead to power law distributions, which provide long range correlations. The observation of power law behavior and the presence of long range correlations in biological systems has been demonstrated in various studies. The combination of the two just mentioned results, theoretical and experimental, supports strongly the scenario of biological evolution across different organisms. In the current Letter we explore in a general way, using the algebra of Nonextensive Statistics introduced by Tsallis and coworkers, if the processes which are described by a class of stochastic models are really random and discuss the results with regard to a possible biological evolution.