A Consensus Policy for Heterogeneous Opinion Dynamics

A typical emerging behavior in opinion dynamics is the convergence of all agents attitudes to a consensus. In homogeneous networks this situation is a 'regression to the mean' of the agents initial conditions. In the presence of heterogeneity more complex behaviors occur. In this paper a strategy for the convergence to a consensus in the bounded confidence Hegselmann-Krause model is proposed. The policy is based on variations of the agents confidence thresholds such that 'the richest looks for someone richer and the poorest for someone poorer'. Numerical results show the effectiveness of the proposed idea and the effects in the presence of stubborns.