{
  "abstract": "Strategic financial markets can admit multiple approximate Nash equilibria with materially different liquidation and price-impact regimes. Returning one equilibrium cannot exclude a more stressed alternative, yet complete enclosure becomes expensive when the profitable deviator changes inside a search region. We propose EquiPartition, a learning-augmented branch-and-bound method based on player-adaptive deviation certificates. The certificate combines institution-specific unilateral-deviation atoms through state-dependent Bernstein weights, while an outward-rounded verifier alone authorizes exclusion. Inconclusive proposals undergo bounded repair or conservative subdivision, so prediction quality affects efficiency but not the complete ε-Nash outer-enclosure guarantee. On the controlled and multi-institution synthetic one-period fire-sale markets, the finite recorded-reference audit retained all recorded high-precision reference-equilibrium components and found no accepted exclusion intersecting them. Relative to Nash-B&B, Predict+Repair reduced proof-tree nodes by 83.3% and 83.9%, unresolved volume by 75.8% and 69.0%, and mean capped runtime by 40.5% and 20.2%; it completed 113 of 120 multi-institution instances versus 103 for Nash-B&B. The same finite recorded-reference audit retained all recorded crisis components under the financial stress shifts. Runtime decomposition showed that amortized proposals avoided repeated neutral-start optimization and offset repair and verification costs. EquiPartition reduces proof fragmentation while preserving verifier-based completeness.",
  "abstract_shown": true,
  "affiliations": [
    "Binary Business School, Binary University of Management & Entrepreneurship, Puchong 47100, Malaysia",
    "Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan",
    "Graduate School of Business and Finance, Waseda University, Tokyo 169-8050, Japan",
    "School of Intelligent Manufacturing and Electrical Engineering, Xinjiang College of Science & Technology, Korla 841000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinjia Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Kun Yang",
      "orcid": "0009-0005-2500-3907"
    },
    {
      "affs": [
        2
      ],
      "name": "Danhua Liu"
    },
    {
      "affs": [
        3
      ],
      "name": "Yingchao Wang",
      "orcid": "0000-0002-7945-1212"
    },
    {
      "affs": [
        1
      ],
      "name": "Feng Xue",
      "orcid": "0009-0008-0501-2824"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/62yjgegchenrz.bib",
  "cited_by": 0,
  "doi": "10.3390/math14214029",
  "fulltext_url": null,
  "id": "62yjgegchenrz",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/4029",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/62yjgegchenrz.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:49Z",
      "native_id": "10.3390/math14214029",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/4029"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "EquiPartition: Learning Player-Adaptive Deviation Certificates for Complete Enclosure of Approximate Nash Equilibria in Strategic Financial Markets",
  "title_text": "EquiPartition: Learning Player-Adaptive Deviation Certificates for Complete Enclosure of Approximate Nash Equilibria in Strategic Financial Markets",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:21:06Z",
  "url": "https://research.draconitic.com/paper/62yjgegchenrz",
  "venue": "Mathematics",
  "versions": []
}