Robust Mean Field Social Control Problems with Applications in Analysis of Opinion Dynamics

TL;DR

This paper develops robust mean field control strategies for social systems with unmodeled dynamics, demonstrating their effectiveness in opinion consensus and multi-population interactions within social networks.

Contribution

It introduces a novel robust control framework for mean field social control problems, including decentralized strategies and applications to opinion dynamics and graphon-based multi-population analysis.

Findings

Opinions reach consensus with the average in probabilistic sense.

Decentralized strategies are asymptotically socially optimal.

Applications to opinion dynamics and multi-population networks are demonstrated.

Abstract

This paper investigates the social optimality of linear quadratic mean field control systems with unmodeled dynamics. The objective of agents is to optimize the social cost, which is the sum of costs of all agents. By variational analysis and direct decoupling methods, the social optimal control problem is analyzed, and two equivalent auxiliary robust optimal control problems are obtained for a representative agent. By solving the auxiliary problem with consistent mean field approximations, a set of decentralized strategies is designed, and its asymptotic social optimality is further proved. Next, the results are applied into the study of opinion dynamics in social networks. The evolution of opinions is analyzed over finite and infinite horizons, respectively. All opinions are shown to reach agreement with the average opinion in a probabilistic sense. Finally, local interactions among multiple populations are examined via graphon theory.