Analysis and control of agreement and disagreement opinion cascades

TL;DR

This paper presents a novel continuous-time model for opinion cascades on large networks, introducing new tools and centrality measures to analyze and control agreement and disagreement dynamics, with applications in social, biological, and robotic systems.

Contribution

It introduces a new continuous-time, state-space model for opinion cascades and develops agreement/disagreement centrality measures based solely on network structure.

Findings

New centrality measures characterize opinion cascade behavior.

Model applicable to biological, social, and robotic networks.

Demonstrated in a multi-robot task allocation scenario.

Abstract

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.