Voter Model Meets Rumour Spreading: an FPRAS for Consensus Probabilities on Voter Models with Agnostic Nodes

TL;DR

This paper introduces an FPRAS for estimating consensus probabilities in voter models with agnostic nodes, combining martingale analysis, bounds, formulas, and algorithms for complex networks.

Contribution

It extends voter model analysis to include agnostic nodes, providing new formulas, bounds, and a polynomial-time approximation scheme for consensus probability estimation.

Findings

FPRAS achieves $O(n^2 \, \log n)$ complexity on general graphs.

Number of runs decreases as network size increases.

Provides closed-form formulas for special cases.

Abstract

Problems of consensus in multi-agent systems are often viewed as a series of independent, simultaneous local decisions made between a limited set of options, all aimed at reaching a global agreement. Key challenges in these protocols include estimating the likelihood of various outcomes and finding bounds for how long it may take to achieve consensus, if it occurs at all. To date, little attention has been given to the case where some agents have no initial opinion. In this paper, we introduce a variant of the consensus problem which includes what we call `agnostic' nodes and frame it as a combination of two known and well-studied processes: voter model and rumour spreading. We show (1) a martingale that describes the probability of consensus for a given colour, (2) bounds on the number of steps for the process to end using results from rumour spreading and voter models, (3) closed formulas for the probability of consensus in a few special cases, along with a polynomial-time algorithm for the case where the number of agnostic vertices is at most logarithmic and (4) that the computational complexity of estimating the probability with a Markov chain Monte Carlo process is $O(n^2 \log n)$ for general graphs and $O(n\log n)$ for Erd\H{o}s-R\'enyi graphs, resulting in a fully polynomial-time randomized approximation scheme (FPRAS) for estimating the probabilities of consensus. Furthermore, we present experimental results suggesting that the number of runs needed for a given standard error decreases when the number of nodes increases.