Issue |
ESAIM: COCV
Volume 30, 2024
|
|
---|---|---|
Article Number | 35 | |
Number of page(s) | 59 | |
DOI | https://doi.org/10.1051/cocv/2024023 | |
Published online | 22 April 2024 |
The Sharp Interface Limit of an Ising Game
1
University of Utah, Salt Lake City, USA
2
UCLA, Los Angeles, USA
* Corresponding author: azp@math.ucla.edu
Received:
20
March
2023
Accepted:
16
March
2024
The Ising model of statistical physics has served as a keystone example of phase transitions, thermodynamic limits, scaling laws, and many other phenomena and mathematical methods. We introduce and explore an Ising game, a variant of the Ising model that features competing agents influencing the behavior of the spins. With long-range interactions, we consider a mean-field limit resulting in a nonlocal potential game at the mesoscopic scale. This game exhibits a phase transition and multiple constant Nash-equilibria in the supercritical regime. Our analysis focuses on a sharp interface limit for which potential minimizing solutions to the Ising game concentrate on two of the constant Nash-equilibria. We show that the mesoscopic problem can be recast as a mixed local/nonlocal space-time Allen-Cahn type minimization problem. We prove, using a Γ-convergence argument, that the limiting interface minimizes a space-time anisotropic perimeter type energy functional. This macroscopic scale problem could also be viewed as a problem of optimal control of interface motion. Sharp interface limits of Allen-Cahn type functionals have been well studied. We build on that literature with new techniques to handle a mixture of local derivative terms and nonlocal interactions. The boundary conditions imposed by the game theoretic considerations also appear as novel terms and require special treatment.
Mathematics Subject Classification: 49N80 / 82C20 / 49Q20
Key words: Spin System / Mean Field Games / Gamma Convergence
© The authors. Published by EDP Sciences, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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