On the stationary distribution of the noisy voter model
Abstract
Given a transition matrix $P$ indexed by a finite set $V$ of vertices, the voter model is a discretetime Markov chain in $\{0,1\}^V$ where at each timestep a randomly chosen vertex $x$ imitates the opinion of vertex $y$ with probability $P(x,y)$. The noisy voter model is a variation of the voter model in which vertices may change their opinions by the action of an external noise. The strength of this noise is measured by an extra parameter $p \in [0,1]$. The noisy voter model is a Markov chain with state space $\{0,1\}^V$, and has a unique stationary distribution $\Gamma$ when $p > 0$. In this paper, we study $\Gamma$ when the number of vertices in $V$ tends to infinity. In particular we analyse the random variable $S = \sum_{x \in V}\pi(x)\xi(x)$, where $\pi$ is the stationary distribution of the transition matrix $P$, and $\xi \in \{0,1\}$ is sampled from $\Gamma$. Allowing $P$ and $p$ to be functions of the size $V$, we show that, under appropriate conditions, a normalised version of $S$ converges to a Gaussian random variable. We provide further analysis of the noisy voter model on a variety of graphs including the complete graph, cycle, torus and hypercube.
 Publication:

arXiv eprints
 Pub Date:
 December 2021
 arXiv:
 arXiv:2112.01478
 Bibcode:
 2021arXiv211201478P
 Keywords:

 Mathematics  Probability;
 60K35;
 60J10