Computing Equilibria in Simulation-Based Insurance Markets with Discontinuous Demand
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.
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BibTeX
@misc{ha2026computing,
title = {{Computing Equilibria in Simulation-Based Insurance Markets with Discontinuous Demand}},
author = {Yunsoo Ha and Linda Nozick},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.30814},
eprint = {2608.30814},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.30814},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Computing Equilibria in Simulation-Based Insurance Markets with Discontinuous Demand
AU - Yunsoo Ha
AU - Linda Nozick
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2608.30814
UR - https://arxiv.org/abs/2608.30814
AB - 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.
ER -