Department of Computer, Control and Management Engineering

We study opinion dynamics in multi-agent networks when a bias toward one of two pos-sible opinions exists, for example reflecting a status quo versus a superior alternative. Our aim is to investigate the combined effect of bias, network structure, and opinion dynamics on the convergence of the system of agents as a whole. Models of such evolving processes can easily become analytically intractable. In this paper, we consider a simple yet mathe-matically rich setting, in which all agents initially share an initial opinion representing the status quo. The system evolves in steps. In each step, one agent selected uniformly at ran -dom follows an underlying update rule to revise its opinion on the basis of those held by its neighbors, but with a probabilistic bias towards the superior alternative. We analyze con-vergence of the resulting process under well-known update rules. The framework we pro -pose is simple and modular, but at the same time complex enough to highlight a nonobvious interplay between topology and underlying update rule.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).