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arXiv · 2109.02256

Particle approximation of one-dimensional Mean-Field-Games with local interactions

Marco Di Francesco·Serikbolsyn Duisembay·Diogo Aguiar Gomes·Ricardo Ribeiro
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Abstract

We study a particle approximation for one-dimensional first-order Mean-Field-Games (MFGs) with local interactions with planning conditions. Our problem comprises a system of a Hamilton-Jacobi equation coupled with a transport equation. As we deal with the planning problem, we prescribe initial and terminal distributions for the transport equation. The particle approximation builds on a semi-discrete variational problem. First, we address the existence and uniqueness of a solution to the semi-discrete variational problem. Next, we show that our discretization preserves some previously identified conserved quantities. Finally, we prove that the approximation by particle systems preserves displacement convexity. We use this last property to establish uniform estimates for the discrete problem. We illustrate our results for the discrete problem with numerical examples.

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Marco Di Francesco, Serikbolsyn Duisembay, Diogo Aguiar Gomes, Ricardo Ribeiro. 2021-09-06. Particle approximation of one-dimensional Mean-Field-Games with local interactions. https://arxiv.org/abs/2109.02256

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