兰德-计算治理博弈论模型中的策略和检测差距（英）

ALVIN MOON, PADMAJA VEDULA, JESSE GENESON, SIMON BAR-ONStrategies and Detection Gaps in a Game-Theoretic Model of Compute GovernanceResearch ReportFor more information on this publication, visit www.rand.org/t/RRA3686-1.About RANDRAND is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. To learn more about RAND, visit www.rand.org.Research IntegrityOur mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit www.rand.org/about/research-integrity.RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors.Published by the RAND Corporation, Santa Monica, Calif.© 2025 RAND Corporation is a registered trademark.Limited Print and Electronic Distribution RightsThis publication and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to its webpage on rand.org is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research products for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/about/publishing/permissions. iii About This Report This report documents research and analysis conducted as part of a study entitled “Broadly Capable AI [Artificial Intelligence] Threats and Mitigation (BCAIT): How Can a Cloud Compute Provider Know If a Big AI Training Run Is Happening on Their System?” sponsored by the Technology and Security Policy Center (TASP) in the RAND Global and Emerging Risks division. The purpose of the study was to investigate detection and monitoring mechanisms that cloud service providers could employ to identify large AI training runs. The intended audience of this report is national policymakers interested in compute-based AI governance. This report may be of interest to cloud service providers and companies providing infrastructure as a service. Technology and Security Policy Cente