报告题目：AI for Incentive-Compatible Insurance Design: Learning Optimal Indemnity with Lipschitz Networks
时 间：2026年09月02日（星期三）14:00
地 点：科研楼18号楼1102
主 办：数学与统计学院
参加对象：感兴趣的老师和学生
报告摘要：Many optimal insurance design problems with the incentive-compatible (IC) condition lack closed-form solutions and efficient numerical methods. This talk proposes a novel algorithm to obtain numerical solutions of optimal insurance by parameterizing the indemnity function using a Lipschitz Multilayer Perceptron (MLP) architecture. To incorporate the principle of indemnity and the IC condition, we introduce constrained affine transformation layers and a special activation function while preserving the MLP’s universal approximation. We provide a theoretical justification for the proposed architecture and develop a gradient-based algorithm that efficiently approximates optimal indemnity functions with very high accuracy. We validate the algorithm’s accuracy against classical benchmarks with known analytical solutions, and demonstrate its applicability to several economically important yet previously intractable settings including optimal insurance with a dependent background risk, loss ambiguity, or rank-dependent utility.
报告人简介：池义春，中央财经大学龙马特聘教授，博士生导师。2009年博士毕业于北京大学，在加拿大多伦多大学做过一年的博士后研究，为加拿大滑铁卢大学访问教授，2015年在中央财经大学破格晋升为研究员。现主要从事精算学和风险管理理论研究，先后主持四项国家自然科学基金项目和两项教育部人文社科重点研究基地重大课题，在国际著名的精算学杂志ASTIN Bulletin、Insurance: Mathematics and Economics、North American Actuarial Journal、Scandinavian Actuarial Journal，经济学期刊Economic Theory、Journal of Economic Behavior and Organization，金融数学杂志 Finance and Stochastics 和运筹学杂志 European Journal of Operational Research 等发表学术论文四十多篇，荣获2012年北美非寿险精算协会的Charles A. Hachemeister 奖，2018年入选中央财经大学首届青年龙马学者项目。目前担任中国现场统计研究会风险管理与精算分会副理事长、中国工业与应用数学学会金融数学与工程和精算保险专业委员会副主任、中国保险学会智库专家库专家等。