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Hypergraphon mean field games

Cui, Kai ; KhudaBukhsh, Wasiur R. ; Koeppl, Heinz (2024)
Hypergraphon mean field games.
In: Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022, 32 (11)
doi: 10.26083/tuprints-00026621
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Hypergraphon mean field games
Language: English
Date: 30 April 2024
Place of Publication: Darmstadt
Year of primary publication: 2022
Place of primary publication: Melville, NY
Publisher: AIP Publishing
Journal or Publication Title: Chaos: An Interdisciplinary Journal of Nonlinear Science
Volume of the journal: 32
Issue Number: 11
Collation: 17 Seiten
DOI: 10.26083/tuprints-00026621
Corresponding Links:
Origin: Secondary publication service

We propose an approach to modeling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and the notion of hypergraphons, which are obtained as limits of large hypergraphs. To the best of our knowledge, ours is the first work on mean field games on hypergraphs. Together with an extension to a multi-layer setup, we obtain limiting descriptions for large systems of non-linear, weakly interacting dynamical agents. On the theoretical side, we prove the well-foundedness of the resulting hypergraphon mean field game, showing both existence and approximate Nash properties. On the applied side, we extend numerical and learning algorithms to compute the hypergraphon mean field equilibria. To verify our approach empirically, we consider a social rumor spreading model, where we give agents intrinsic motivation to spread rumors to unaware agents, and an epidemic control problem.

Uncontrolled Keywords: Ionospheric physics, Theoretical computer science, Agent based models, Game theory, Graph theory, Iteration method, Diseases and conditions, Neuroscience, Epidemiology, Stochastic processes
Identification Number: Artikel-ID: 113129
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-266214
Classification DDC: 500 Science and mathematics > 530 Physics
600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
LOEWE > LOEWE-Zentren > emergenCITY
Date Deposited: 30 Apr 2024 09:03
Last Modified: 30 Apr 2024 09:04
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/26621
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