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February 2021 The majority vote process and other consensus processes on trees
Maury Bramson, Lawrence F. Gray
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Ann. Appl. Probab. 31(1): 169-198 (February 2021). DOI: 10.1214/20-AAP1586

Abstract

The majority vote process was one of the first interacting particle systems to be investigated. It can be described briefly as follows. There are two possible opinions at each site of a graph G. At rate 1ε, the opinion at a site aligns with the majority opinion at its neighboring sites and, at rate ε, the opinion at a site is randomized due to noise, where ε[0,1] is a parameter.

Despite the simple dynamics of the majority vote process, its equilibrium behavior is difficult to analyze when the noise rate is small but positive. In particular, when the underlying graph is G=Zn with n2, it is not known whether the process possesses more than one equilibrium. This is surprising, especially in light of the close analogy between this model and the stochastic Ising model, where much more is known.

Here, we study the majority vote process on the infinite tree Td with vertex degree d. For d5 and small noise, we show that there are uncountably many mutually singular equilibria, with convergence to such an equilibrium occurring exponentially quickly from nearby initial states.

Our methods are quite flexible and extend to a broader class of models, consensus processes. This class includes the stochastic Ising model and other processes in which the dynamics at a site depend on the number of neighbors holding a given opinion. All of our proofs are carried out in this broader context.

Citation

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Maury Bramson. Lawrence F. Gray. "The majority vote process and other consensus processes on trees." Ann. Appl. Probab. 31 (1) 169 - 198, February 2021. https://doi.org/10.1214/20-AAP1586

Information

Received: 1 March 2019; Revised: 1 March 2020; Published: February 2021
First available in Project Euclid: 8 March 2021

Digital Object Identifier: 10.1214/20-AAP1586

Subjects:
Primary: 60K35
Secondary: 82C22

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.31 • No. 1 • February 2021
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