Non-stationary aging dynamics in ant societies

TL;DR

This paper investigates non-stationary aging dynamics in ant societies, proposing a model that explains how different external conditions lead to either stationary or non-stationary movement patterns, akin to physical aging phenomena.

Contribution

It introduces a stochastic lattice model demonstrating how varying interaction rules and a control parameter can produce both Poisson and log-Poisson statistics in ant movement dynamics.

Findings

Model reproduces non-stationary aging behavior under certain conditions

External factors influence whether ant movement follows Poisson or log-Poisson statistics

Provides a framework linking biological ant dynamics to physical aging processes

Abstract

In recent experiments by Richardson et al. ((2010), PLoS ONE 5(3): e9621. doi:10.1371/ journal.pone.0009621) ant motion out of the nest is shown to be a non-stationary process intriguingly similar to the dynamics encountered in \emph{physical aging} of glassy systems. Specifically, exit events can be described as a Poisson process in logarithmic time, or, for short, a log-Poisson process. Nouvellet et al.(J. Theor. Biol. 266, 573. (2010)) criticized these conclusions and performed new experiments where the exit process could more simply be described by standard Poisson statistics. In their reply, (J Theor. Biol. 269, 356-358 (2011)) Richardson et al. stressed that the two sets of experiments were performed under very different conditions and claimed that this was the likely source of the discrepancy. The focal point of this work is whether log-Poisson and Poisson statistics both are possible under different external conditions. To this end, a model is introduced where interacting ants move in a stochastic fashion from one site to a neighboring site on a finite 2D lattice. The probability of each move is determined by the ensuing changes of a utility function which is a sum of pairwise interactions between ants, weighted by distance. Depending on how the interactions are defined and on a control parameter dubbed 'degree of stochasticity' (DS), the dynamics either quickly converges to a stationary state, where movements are a standard Poisson process, or may enter a non-stationary regime, where exits can be described as suggested by Richardson et al.