Synchronization in a Kuramoto Mean Field Game
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Abstract (arXiv)
The classical Kuramoto model is studied in the setting of an infinite horizon mean field game. The system is shown to exhibit both synchronization and phase transition. Incoherence below a critical value of the interaction parameter is demonstrated by the stability of the uniform distribution. Above this value, the game bifurcates and develops self-organizing time homogeneous Nash equilibria. As interactions become stronger, these stationary solutions become fully synchronized. Results are proved by an amalgam of techniques from nonlinear partial differential equations, viscosity solutions, stochastic optimal control and stochastic processes.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2022.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behaviorcited by 6
- 2023Major-Minor Mean Field Multi-Agent Reinforcement Learningcited by 4
- 2026Learning Distributed Equilibria in Linear-Quadratic Stochastic Differential Games: An α-Potential Approachcited by 1
- 2025Functional role of synchronization: A mean-field control perspectivecited by 0
- 2026Learning Distributed Equilibria in Linear-Quadratic Stochastic Differential Games: An α-Potential Approach18 Feb 2026
- 2025Functional role of synchronization: A mean-field control perspective1 Feb 2025
- 2023Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior12 Jul 2023
- 2023Major-Minor Mean Field Multi-Agent Reinforcement Learning19 Mar 2023