The noisy voter model with general initial conditions
Abstract
We study the noisy voter model with states and noise probability on arbitrary bounded-degree -vertex graphs with subexponential growth of balls (e.g., finite subsets of ). Cox, Peres and Steif (2016) showed for the binary case (and a wider class of chains) that, when starting from a worst-case initial state, this Markov chain has total variation cutoff at . The second author and Sly (2021) analyzed faster initial conditions for Glauber dynamics for the 1D Ising model, which is the noisy voter for and . They showed that the ``alternating'' initial state is the fastest one if , and conjectured that this holds for all values of the noise . Here we show that for every graph as above and all and initial states , the noisy voter model exhibits cutoff at an explicit function of the autocorrelation of the model started at . Consequently, for and (Glauber dynamics for the 1D Ising model), we confirm the conjecture of [LS21] that the alternating initial condition is asymptotically fastest for all . Analogous results hold in for and all (``checkerboard'' initial conditions are fastest) as well as for and all (``rainbow'' initial conditions are fastest).
Keywords
Cite
@article{arxiv.2507.16188,
title = {The noisy voter model with general initial conditions},
author = {Patrizio Caddeo and Eyal Lubetzky},
journal= {arXiv preprint arXiv:2507.16188},
year = {2025}
}
Comments
38 pages, 5 figures