网络互助的优化设计

IF 0.7 3区工程技术 Q4 ENGINEERING, INDUSTRIAL

Probability in the Engineering and Informational Sciences Pub Date : 2022-10-31 DOI:10.1017/S0269964822000341

Jingchao Li, Zichen Fang, Ciyu Nie, Sizhe Chen

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引用次数: 1

摘要

网络互助平台是近年来流行的风险分担模式之一，在中国拥有近2亿会员。然而，目前的互助平台无论是在分摊方式上还是在定价原则上都不符合精算规则。因此，本文提出了多种互助模式，使具有不同风险的互助成员能够以透明和精算公平的方式交换其风险。构建了参与者选择互助平台与同类保险产品、不选择风险共担的决策框架。决策是基于期望效用最大化的原则做出的。并分别构造了利润最大化和风险最小化的优化问题。通过个人公平和相对公平的原则，也可以减少平台逆向选择的问题。最后，将实际互助方案与同类保险产品进行比较，探讨优化方案的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optimal design for network mutual aid

Abstract Network mutual aid platforms is one of the popular risk-sharing models in recent years, and they have almost 200 million members in China. However, current mutual aid platforms does not satisfy the actuarial rules in either the apportionment method or the pricing principle. Hence, a variety of mutual aid models which enable mutual aid members with different risks to exchange their risks in a transparent and actuarial fair way have been proposed in this paper. Besides, the decision-making frameworks for participants choosing between the mutual aid platform and similar insurance products, or choosing no risk sharing are constructed, respectively. Decisions are made based on the principle of maximizing expected utility. Moreover, the optimization problems of maximizing profit and minimizing risk are constructed, respectively. Through the principle of individual fairness and relative fairness, the problem of adverse selection of the platform can also be reduced. Finally, the actual mutual aid plan is compared with similar insurance products to discuss the advantages of the optimized plan.

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来源期刊

Probability in the Engineering and Informational Sciences 数学-工程：工业

CiteScore

2.20

自引率

18.20%

发文量

审稿时长

>12 weeks

期刊介绍： The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.