Rescaling approach for a stochastic population dynamics equation perturbed by a linear multiplicative Gaussian noise

TL;DR

This paper introduces a rescaling method to analyze a stochastic age-structured population model with Gaussian noise, proving well-posedness and regularity of solutions.

Contribution

It develops a novel rescaling approach transforming the stochastic model into a deterministic form, enabling rigorous analysis of existence and uniqueness of solutions.

Findings

The stochastic model is well-posed with path-wise continuous solutions.

Solutions exhibit regularity in age and space variables.

The rescaling approach effectively handles linear multiplicative Gaussian noise.

Abstract

We are concerned with a nonlinear nonautonomous model represented by an equation describing the dynamics of an age-structured population diffusing in a space habitat $O,$ governed by local Lipschitz vital factors and by a stochastic behavior of the demographic rates possibly representing emigration, immigration and fortuitous mortality. The model is completed by a random initial condition, a flux type boundary conditions on $\partial O$ with a random jump in the population density and a nonlocal nonlinear boundary condition given at age zero. The stochastic influence is expressed by a linear multiplicative Gaussian noise perturbation in the equation. The main result proves that the stochastic model is well-posed, the solution being in the class of path-wise continuous functions and satisfying some particular regularities with respect to the age and space. The approach is based on a rescaling transformation of the stochastic equation into a random deterministic time dependent hyperbolic-parabolic equation with local Lipschitz nonlinearities. The existence and uniqueness of a strong solution to the random deterministic equation is proved by combined semigroup, variational and approximation techniques. The information given by these results is transported back via the rescaling transformation towards the stochastic equation and enables the proof of its well-posedness.