{
  "abstract": "We study equilibrium computation in simulation-based games motivated by competition in natural-hazard insurance markets. The motivating application considers a multi-region market in which each insurer selects regional pricing and reinsurance decisions to maximize expected financial performance subject to multi-period insolvency constraints. Competitors' decisions affect both profitability and insolvency feasibility through the insured portfolio. Consequently, the strategic interaction defines a generalized Nash game with discontinuous player objectives induced by discrete household purchase decisions and potentially nonconvex feasible sets generated by the insolvency constraints. Discontinuity, nonconvex feasibility, and simulation-defined objectives and constraints complicate the direct use of standard optimality-based equilibrium formulations. Moreover, repeated evaluation of the multi-period simulator makes equilibrium search computationally expensive. To address these computational challenges, we develop an evaluation-efficient framework for computing approximate local equilibria, which combines a response optimizer tailored to the distinct structures of pricing and reinsurance with damped better-response dynamics to mitigate cycling. In a case study of the North Carolina hurricane insurance market, the framework identifies multiple approximate local equilibrium candidates both with and without a household affordability cap and finds no profitable sampled unilateral deviations within the tested neighborhoods under the original simulation model. A representative equilibrium candidate is computed in approximately 5--10 minutes on a single GPU, making repeated equilibrium analyses computationally practical.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.30814",
  "author_total": 2,
  "authors": [
    {
      "name": "Yunsoo Ha"
    },
    {
      "name": "Linda Nozick"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3gy12vxpraqra.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30814",
  "fulltext_url": null,
  "id": "3gy12vxpraqra",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30814",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2002|Insurance and Financial Risk Management"
    ]
  ],
  "openalex": "W7204867559",
  "pdf_url": "https://arxiv.org/pdf/2608.30814",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3gy12vxpraqra.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:57Z",
      "native_id": "2608.30814",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30814"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Computing Equilibria in Simulation-Based Insurance Markets with Discontinuous Demand",
  "title_text": "Computing Equilibria in Simulation-Based Insurance Markets with Discontinuous Demand",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:57Z",
  "url": "https://research.draconitic.com/paper/3gy12vxpraqra",
  "venue": "arXiv",
  "versions": []
}