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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Abstract
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2024 |
| Creators: | Cui, Kai ; KhudaBukhsh, Wasiur R. ; Koeppl, Heinz |
| 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 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/26621 |
| Corresponding Links: | |
| Origin: | Secondary publication service |
| Abstract: | 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 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications 18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
| Date Deposited: | 30 Apr 2024 09:03 |
| Last Modified: | 13 May 2024 11:26 |
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| Suche nach Titel in: | TUfind oder in Google |
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