发表机构
School of Civil and Environmental Engineering, Cornell University（康奈尔大学土木与环境工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对飓风风险下需求不连续、可行域非凸的保险市场均衡计算难题，提出定价依赖再保险优化算子结合信赖域与阻尼更好响应方案的方法，在北卡飓风保险市场案例中识别出候选均衡且无盈利抽样偏差。
AI 中文摘要
我们研究飓风风险下竞争保险市场中出现的基于模拟的均衡问题。各保险公司通过选择区域定价和再保险决策来最大化自身利润，同时满足破产约束。该问题极具挑战性，因为客户购买决策会产生不连续的需求函数，而破产约束会形成非凸可行域。为应对这些挑战，我们引入了一种依赖定价的再保险优化算子，该算子会为每个候选定价向量重新优化再保险，将各保险公司的联合响应问题转化为结构化定价问题，其中破产可行性通过再保险重新优化处理。这种重新表述避免了在非凸联合可行集上同时优化定价和再保险。我们利用同时利用平滑和原始目标的信赖域框架优化所得的非光滑、非凸简化目标，并结合直接搜索探索。计算得到的近似响应被嵌入阻尼更好响应方案中以稳定均衡迭代。在北卡罗来纳州飓风保险市场的案例研究中，该框架以实际运行时间识别出多个候选均衡，而基于半径的局部纳什测试在测试邻域内未发现可盈利的抽样偏差。
英文摘要
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.
