Abstract
We propose a nonlinear voter model to study the emergence of global consensus in opinion dynamics. In our model, agent i agrees with one of binary opinions with the probability that is a power function of the number of agents holding this opinion among agent i and its nearest neighbors, where an adjustable parameter α controls the effect of herd behavior on consensus. We find that there exists an optimal value of α leading to the fastest consensus for lattices, random graphs, small-world networks and scale-free networks. Qualitative insights are obtained by examining the spatiotemporal evolution of the opinion clusters.
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