{"title":"Mitigating moral hazard in insurance contracts using risk preference design","authors":"Shutian Liu , Quanyan Zhu","doi":"10.1016/j.orl.2025.107322","DOIUrl":null,"url":null,"abstract":"<div><div>Preferences might be affected by exogenous inputs, such as information generated by advanced technologies. This paper proposes a thought experiment called risk preference design to investigate this influence within a class of principal-agent problems. Risk preference design has potential to mitigate moral hazard in insurance contracting. A quantitative approach is introduced to study the effects. We use a linear contract case study to demonstrate the role of risk preference design in strengthening financial security.</div></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":"62 ","pages":"Article 107322"},"PeriodicalIF":0.8000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research Letters","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167637725000835","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Preferences might be affected by exogenous inputs, such as information generated by advanced technologies. This paper proposes a thought experiment called risk preference design to investigate this influence within a class of principal-agent problems. Risk preference design has potential to mitigate moral hazard in insurance contracting. A quantitative approach is introduced to study the effects. We use a linear contract case study to demonstrate the role of risk preference design in strengthening financial security.
期刊介绍:
Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.