Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models

TL;DR

This paper computes and compares time-dependent PDFs in stochastic logistic and Gompertz growth models, revealing noise effects, transitions in distribution shapes, and introducing the concept of information length to unify their dynamics.

Contribution

It introduces a detailed analysis of time-dependent PDFs in stochastic growth models and proposes the use of information length to compare their dynamic evolution.

Findings

Transition from unimodal to bimodal PDFs with increasing multiplicative noise

Gompertz model's weak attractor causes significant growth of small populations

Information length effectively unifies the dynamics of different growth models

Abstract

A probabilistic description is essential for understanding growth processes far from equilibrium. In this paper, we compute time-dependent Probability Density Functions (PDFs) in order to investigate stochastic logistic and Gompertz models, which are two of the most popular growth models. We consider different types of short-correlated internal (multiplicative) and external (additive) stochastic noises and compare the time-dependent PDFs in the two models, elucidating the effects of the additive and multiplicative noises on the form of PDFs. We demonstrate an interesting transition from a unimodal to a bimodal PDF as the multiplicative noise increases for a fixed value of the additive noise. A much weaker (leaky) attractor in the Gompertz model leads to a significant (singular) growth of the population of a very small size. We point out the limitation of using stationary PDFs, mean value and variance in understanding statistical properties of the growth far from equilibrium, highlighting the importance of time-dependent PDFs. We further compare these two models from the perspective of information change that occurs during the growth process. Specifically, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory quantifies the total number of different states that the system undergoes in time, and is called the information length. We show that the time-evolution of the two models become more similar when measured in units of the information length and point out the merit of using the information length in unifying and understanding the dynamic evolution of different growth processes.