作为AI风险基础设施的保险：AI采纳的生成智能体模拟
Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption
AI总结：
本文提出将保险作为AI风险基础设施，开发LLM驱动的基于智能体的社会模拟系统，验证其可降低企业财务风险，加速AI工具采纳并提升企业偿付能力与总体资本。
AI中文摘要：
人工智能（AI）工具的快速发展已展现出提升社会福祉与运营效率的巨大潜力，但现代AI系统（以大语言模型LLMs为典型）固有的不可靠性及不确定的运营后果，成为企业深度采纳的重大障碍。众多企业因担忧不可预测的损失与责任风险，仍犹豫是否将这些工具深度融入工作流程。现有技术防护措施主要旨在降低AI驱动工作流故障的概率或严重程度，但当残余的金钱尾部损失发生时，无法单独提供事后财务保护。本文提出一种社会经济框架，通过保险转移并吸收AI采纳的残余财务后果，以补充上述防护措施。为评估该框架，我们开发了LLM驱动的基于智能体的社会模拟（LABSS）系统，运用成熟的经济与社会学理论评估模拟的行为有效性。分析表明，所提出的保险框架可降低企业层面的财务风险敞口，从而加速AI工具的整体采纳，提升企业偿付能力与总体资本。
英文摘要：
The rapid evolution of artificial intelligence (AI) tools has demonstrated immense potential to enhance societal well-being and operational efficiency. However, the inherent unreliability and uncertain operational consequences of modern AI systems, typified by large language models (LLMs), have created a significant barrier to enterprise adoption. Many enterprises remain hesitant to integrate these tools deeply into their workflows due to concerns about unpredictable losses and liability exposure. While existing technical safeguards primarily seek to reduce the likelihood or severity of AI-enabled workflow failures, they do not by themselves provide ex post financial protection when residual pecuniary tail losses materialize. In this paper, we introduce a socio-economic framework that complements these safeguards by transferring and absorbing the residual financial consequences of AI adoption through insurance. To evaluate this framework, we develop an LLM-driven agent-based social simulation (LABSS) system. We assess the behavioral validity of the simulation using established economic and sociological theories. Our analysis demonstrates that the proposed insurance framework reduces firm-level financial exposure, thereby accelerating the aggregate adoption of AI tools and improving firm solvency and aggregate capital.
