Analysis shows solutions of multivalued distribution-dependent stochastic differential equations can be approximated by averaged SDEs, indicating new methods for effective solution estimation.
In this work, we mainly consider a class of multivalued distribution‐dependent stochastic differential equations (MDDSDEs, in short). With some assumptions, we prove that the solutions of MDDSDEs can be approximated by the solutions of associated averaged multivalued SDEs in two senses of convergence in mean square and convergence in probability, respectively.
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Ren et al. (2025) studied this question.
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