Sequential optimization for state-dependent opinion dynamics

Stability and analysis of networked decision systems with state-dependent switching typologies have been a fundamental and longstanding challenge in control, social sciences, and many other related fields. These already complex systems become further complicated once one accounts for asymmetry or heterogeneity of the underlying agents/dynamics. Despite extensive progress in analysis of conventional networked decision systems where the network evolution and state dynamics are driven by independent or weakly coupled processes, most of the existing results fail to address decision systems where the network and state dynamics are highly coupled and evolve based on status of heterogeneous agents. Motivated by numerous applications of such dynamics in social sciences, in this paper we provide a new direction toward analysis of dynamic networks of heterogeneous agents under complex environments. As a result we show how Lyapunov stability of several problems from opinion dynamics can be established using a simple application of our framework. Our results provide new insights toward analysis of complex networked multi-agent systems using exciting field of sequential optimization.