Mean-field stochastic differential equations and associated PDEs
Rainer Buckdahn, Juan Li, Shige Peng, Catherine Rainer

TL;DR
This paper studies mean-field stochastic differential equations (McKean-Vlasov equations) with coefficients depending on the solution and its law, characterizing associated PDEs through derivatives with respect to probability measures.
Contribution
It introduces a novel approach to analyze derivatives of solutions with respect to probability laws, leading to a PDE characterization of the value function.
Findings
Established flow property for solutions with random initial conditions
Characterized the value function as a unique classical solution of a non-local PDE
Extended derivative concepts to second order for probability measures
Abstract
In this paper we consider a mean-field stochastic differential equation, also called Mc Kean-Vlasov equation, with initial data which coefficients depend on both the solution but also its law. By considering square integrable random variables as initial condition for this equation, we can easily show the flow property of the solution of this new equation. Associating it with a process which coincides with , when one substitutes for , but which has the advantage to depend only on the law of , we characterise the function under appropriate regularity conditions on the coefficients of the stochastic differential equation as the unique classical solution of a non local PDE of mean-field type, involving the first and second…
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