No one doubts the utility of insurance for its ability to spread risk or streamline claims management; much debated is when and how insurance uptake can improve welfare by reducing harm, despite moral hazard. Proponents and dissenters of "regulation by insurance" have now documented a number of cases of insurers succeeding or failing to have such a net regulatory effect (in contrast with a net hazard effect). Collecting these examples together and drawing on an extensive economics literature, this Article develops a principled framework for evaluating insurance uptake's effect in a given context. The presence of certain distortions - including judgment-proofness, competitive dynamics, and behavioral biases - creates potential for a net regulatory effect. How much of that potential gets realized then depends on the type of policyholder, type of risk, type of insurer, and the structure of the insurance market. The analysis suggests regulation by insurance can be particularly effective for catastrophic non-product accidents where market mechanisms provide insufficient discipline and psychological biases are strongest. As a demonstration, the framework is applied to the frontier AI industry, revealing significant potential for a net regulatory effect but also the need for policy intervention to realize that potential. One option is a carefully designed mandate that encourages forming a specialized insurer or mutual, focuses on catastrophic rather than routine risks, and bars pure captives.

翻译：无人质疑保险在分散风险或简化理赔管理方面的效用；但关于保险采纳如何能在存在道德风险的情况下通过减少损害来提高福利，其时机与方式仍备受争议。"保险监管"的支持者与反对者现已记录了大量案例，表明保险公司成功或未能产生此种净监管效应（与净风险效应相对）。本文汇集这些案例，并借鉴广泛的经济学文献，构建了一个原则性框架，用于评估特定情境下保险采纳的影响。某些扭曲因素的存在——包括责任规避、竞争动态和行为偏差——创造了实现净监管效应的可能性。这种潜力能在多大程度上实现，则取决于投保人类型、风险类型、保险公司类型以及保险市场结构。分析表明，对于市场机制约束不足且心理偏差最为显著的灾难性非产品事故，保险监管可能尤为有效。作为例证，该框架被应用于前沿人工智能行业，揭示了实现净监管效应的巨大潜力，同时也表明需要政策干预以实现该潜力。一种可行的方案是精心设计强制机制：鼓励组建专业保险公司或互助组织，聚焦灾难性而非常规风险，并禁止设立纯自保公司。