Suppressing escape events in maps of the unit interval with demographic noise

TL;DR

This paper investigates how to prevent escape events in stochastic maps of the unit interval with demographic noise, proposing censoring of the noise distribution to improve consistency with microscopic models.

Contribution

It introduces a method of censoring the noise distribution to better match microscopic model behavior, avoiding the need for higher moments or truncation.

Findings

Censoring noise distribution reduces escape events effectively.

Higher moments do not improve accuracy over Gaussian noise.

Censoring aligns mesoscopic results with microscopic model behavior.

Abstract

We explore the properties of discrete-time stochastic processes with a bounded state space, whose deterministic limit is given by a map of the unit interval. We find that, in the mesoscopic description of the system, the large jumps between successive iterates of the process can result in probability leaking out of the unit interval, despite the fact that the noise is multiplicative and vanishes at the boundaries. By including higher-order terms in the mesoscopic expansion, we are able to capture the non-Gaussian nature of the noise distribution near the boundaries, but this does not preclude the possibility of a trajectory leaving the interval. We propose a number of prescriptions for treating these escape events, and we compare the results with those obtained for the metastable behavior of the microscopic model, where escape events are not possible. We find that, rather than truncating the noise distribution, censoring this distribution to prevent escape events leads to results which are more consistent with the microscopic model. The addition of higher moments to the noise distribution does not increase the accuracy of the final results, and it can be replaced by the simpler Gaussian noise.