Analysis and control of agreement and disagreement opinion cascades

We introduce and analyze a continuous time and state-space model of opinion cascades on networks of large numbers of agents that form opinions about two or more options. By leveraging our recent results on the emergence of agreement and disagreement states, we introduce novel tools to analyze and control agreement and disagreement opinion cascades. New notions of agreement and disagreement centrality, which depend only on network structure, are shown to be key to characterizing the nonlinear behavior of agreement and disagreement opinion formation and cascades. Our results are relevant for the analysis and control of opinion cascades in real-world networks, including biological, social, and artificial networks, and for the design of opinion-forming behaviors in robotic swarms. We illustrate an application of our model to a multi-robot task-allocation problem and discuss extensions and future directions opened by our modeling framework.

Automated summary

The study presents a continuous-time and state-space model of opinion cascades on networks, focusing on the emergence of agreement and disagreement states and introducing new tools to analyze and control these cascades. It highlights the importance of network structure and proposes novel concepts of agreement and disagreement centrality. The results have implications for analyzing and controlling opinion cascades in real-world networks and designing opinion-forming behaviors in robotic swarms.