Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption

[2608.15181] Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Multiagent Systems arXiv:2608.15181 (cs) [Submitted on 15 Aug 2026] Title: Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption Authors: Yixuan Yuan , Dedai Wei , Chudong Qian , Jielin Feng , Ziyue Lin , Yuheng Zhao , He Cao , Erasmo Purificato , Xinwu Ye View a PDF of the paper titled Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption, by Yixuan Yuan and 8 other authors View PDF HTML (experimental) Abstract: 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. Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2608.15181 [cs.MA] (or arXiv:2608.15181v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2608.15181 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yixuan Yuan [ view email ] [v1] Sat, 15 Aug 2026 11:45:51 UTC (1,819 KB) Full-text links: Access Paper: View a PDF of the paper titled Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption, by Yixuan Yuan and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.MA < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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