{
  "abstract": "Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through representative agents and their associated mean-field distributions. However, existing approaches do not explicitly account for uncertainty in the behavior of other populations. To this end, we introduce a new paradigm: risk-averse multi-population mean-field games, where each population optimizes a worst-case expected reward over dynamically feasible ambiguity sets of mean-field flows of a subset of the other populations. Employing an occupation-measure formulation along with tools from set-valued analysis, we establish, under mild assumptions, several theoretical properties of the multi-population game, including the geometric properties of the ambiguity sets and the existence of a novel risk-averse multi-population mean-field equilibrium. Further, we derive contractivity results of the fixed-point operator under entropy regularization and show that it can be utilized to learn the equilibrium. Finally, we propose a risk-averse fictitious-play scheme and show that exploitability decays to zero, despite the additional nonlinearity introduced by the worst-case objective. We report several numerical experiments to illustrate convergence and risk-averse behavior.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09244",
  "author_total": 3,
  "authors": [
    {
      "name": "Bhavini Jeloka"
    },
    {
      "name": "Siddhartha Ganguly"
    },
    {
      "name": "Panagiotis Tsiotras"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4d9wf3m8qe4q9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09244",
  "fulltext_url": null,
  "id": "4d9wf3m8qe4q9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09244",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.GT"
    ],
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      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
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      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2003|Stochastic processes and financial applications"
    ]
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  "openalex": "W7221033544",
  "pdf_url": "https://arxiv.org/pdf/2610.09244",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4d9wf3m8qe4q9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:42Z",
      "native_id": "2610.09244",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09244"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games",
  "title_text": "Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:42Z",
  "url": "https://research.draconitic.com/paper/4d9wf3m8qe4q9",
  "venue": "arXiv",
  "versions": []
}