Anomalous diffusion and long-range memory in the scaled voter model

TL;DR

This paper investigates a generalized voter model with time-dependent herding, revealing long-range memory effects and connections to bounded fractional Brownian motion through analytical and numerical analysis.

Contribution

It introduces a scaled voter model driven by scaled Brownian motion, providing analytical expressions for moments and passage times, and demonstrating long-range memory in a Markov framework.

Findings

Model exhibits long-range memory indicators.

Analytical expressions match numerical simulations.

Steady-state distribution aligns with bounded fractional Brownian motion.

Abstract

We analyze the scaled voter model, which is a generalization of the noisy voter model with time-dependent herding behavior. We consider the case when the intensity of herding behavior grows as a power-law function of time. In this case, the scaled voter model reduces to the usual noisy voter model, but it is driven by the scaled Brownian motion. We derive analytical expressions for the time evolution of the first and second moments of the scaled voter model. In addition, we have derived an analytical approximation of the first passage time distribution. By numerical simulation, we confirm our analytical results as well as show that the model exhibits long-range memory indicators despite being a Markov model. The proposed model has steady-state distribution consistent with the bounded fractional Brownian motion, thus we expect it to be a good substitute model for the bounded fractional Brownian motion.