Weak quantitative propagation of chaos via differential calculus on the space of measures

Consider the metric space $(\mathcal{P}_2(\mathbb{R}^d),W_2)$ of square integrable laws on $\mathbb{R}^d$ with the topology induced by the 2-Wasserstein distance $W_2$. Let $Φ: \mathcal{P}_2( \mathbb{R}^d) \to \mathbb{R}$ be a function and $μ_N$ be the empirical measure of a sample of $N$ random variables distributed as $μ$. The main result of this paper is to show that under suitable regularity conditions, we have $|Φ(μ) - \mathbb{E}Φ(μ_N)|= \sum_{j=1}^{k-1}\frac{C_j}{N^j} + O(\frac{1}{N^k}),$ for some positive constants $C_1, \ldots, C_{k-1}$ that do not depend on $N$, where $k$ corresponds to the degree of smoothness. We distinguish two cases: a) $μ_N$ is the empirical measure of $N$-samples from $μ$; b) $μ$ is a marginal law of McKean-Vlasov stochastic differential equation in which case $μ_N$ is an empirical law of marginal laws of the corresponding particle system. The first case is studied using functional derivatives on the space of measures. The second case relies on an Itô-type formula for the flow of probability measures and is intimately connected to PDEs on the space of measures, called the master equation in the literature of mean-field games. We state the general regularity conditions required for each case and analyse the regularity in the case of functionals of the laws of McKean-Vlasov SDEs. Ultimately, this work reveals quantitative estimates of propagation of chaos for interacting particle systems. Furthermore, we are able to provide weak propagation of chaos estimates for ensembles of interacting particles and show that these may have some remarkable properties.

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