Propagation of Chaos for Doubly Mean Reflected BSDEs

Abstract In this paper, we establish propagation of chaos (POC) for doubly mean reflected backward stochastic differential equations (MRBSDEs). MRBSDEs differ from typical RBSDEs in that the constraint is not on the paths of the solution but on its law. We focus on approximating MRBSDEs by interacting particle systems (IPS) and achieve distinct convergence speeds under different scenarios. We propose two sets of IPS having mean-field Skorokhod problems, capturing the dynamics of IPS reflected in a mean-field way. As the dimension of the IPS tends to infinity, the system converges to a limit with independent particles, where each solves the MRBSDE.

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Publication Details

Journal
Journal of Theoretical Probability
Published
2026-08-27
DOI
https://doi.org/10.1007/s10959-026-01546-w
Citations
2
Primary Topic
Nonlinear Dynamics and Pattern Formation
Type
article
Field-Weighted Citation Impact
0.00

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article

Propagation of Chaos for Doubly Mean Reflected BSDEs

Hanwu Li, Ning Ning
2 citations
Journal of Theoretical Probability
Nonlinear Dynamics and Pattern Formation
article

Propagation of Chaos for Doubly Mean Reflected BSDEs

Hanwu Li, Ning Ning
article en
2 citations

Abstract

Abstract In this paper, we establish propagation of chaos (POC) for doubly mean reflected backward stochastic differential equations (MRBSDEs). MRBSDEs differ from typical RBSDEs in that the constraint is not on the paths of the solution but on its law. We focus on approximating MRBSDEs by interacting particle systems (IPS) and achieve distinct convergence speeds under different scenarios. We propose two sets of IPS having mean-field Skorokhod problems, capturing the dynamics of IPS reflected in a mean-field way. As the dimension of the IPS tends to infinity, the system converges to a limit with independent particles, where each solves the MRBSDE.

Journal of Theoretical ProbabilityVol. 39(4)
Shandong University of Science and Technology (CN), Texas A&M University (US)
Shandong University, National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Young Scientists Fund, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 100%
Nonlinear Dynamics and Pattern Formation
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