Solving mean field rough differential equations

Abstract

We provide in this work a robust solution theory for random rough differential equations of mean field type $$d{X}_t=V(X_t,\mathcal{L}(X_t))dt+F(X_t,\mathcal{L}(X_t))d{W}_t,$$ where $W$ is a random rough path and $\mathcal{L}(X_t)$ stands for the law of $X_t$, with mean field interaction in both the drift and diffusivity. The analysis requires the introduction of a new rough path-like setting and an associated notion of controlled path. We use crucially Lions' approach to differential calculus on Wasserstein space along the way.

arXiv comment63 pages; v2: Version 1 of this work has been split in two seperate works, the first part of which is the present work. A number of arguments in both parts have been reworked in depth, and improved

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• Large deviation properties of weakly interacting processes via weak convergence methods
• Integrability and tail estimates for Gaussian rough differential equations
• On the mean field games with common noise and the McKean-Vlasov SPDEs
• Evolving Communities with Individual Preferences
• The enhanced Sanov theorem and propagation of chaos

Cited by in corpus (1)

• Propagation of chaos for mean field rough differential equations