Efficient binary consensus in randomized and noisy environments

In this article we investigate randomized binary majority consensus in networks with random topologies and noise. Using computer simulations, we show that asynchronous Simple Majority rule can reach ≃ 100% convergence rate being randomized by an update-biased random neighbor selection and a small fraction of errors. Next, we show that such gains are robust towards additive noise and topology randomization.