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

arXiv: https://arxiv.org/abs/2002.12040 · HTML: https://mlsanity.com/paper/2002.12040/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Mean Field LQG Social Optimization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2410.15119/) (2024)
- [Deep Graphic FBSDEs for Opinion Dynamics Stochastic Control](https://mlsanity.com/paper/2204.02506/) (2022)
- [Reinforcement Learning for Discrete-time LQG Mean Field Social Control Problems with Unknown Dynamics](https://mlsanity.com/paper/2507.01420/) (2025)

## Abstract (arXiv)

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.