这篇论文解释了为什么 AI 智能体比经典博弈论预测的更合作，提出了一套新理论框架，搞 AI 安全和多智能体系统的人值得看看。
arXiv 论文 2608.03958 研究基础模型智能体在社会困境中的博弈行为，发现其最优规划会稳定收敛到合作，与经典博弈论预测的相互背叛相悖。作者提出“嵌入式贝叶斯智能体”理论模型，将智能体视为宇宙的一部分，对自身决策算法保持认知不确定性。通过推断他人行为相似性，智能体将自身合作决策视为相似伙伴也会合作的证据。论文提出“嵌入式均衡”替代纳什均衡，为现代 AI 智能体的社会行为建立基础博弈论。
A game theory for foundation models shows new paths to rational cooperation through similarity inference
As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles governing their collective behavior is essential for ensuring safety and cooperation. Classical game theory, the dominant framework for modeling rational interaction, is built upon the assumption of `decoupled agency,' where agents treat their own decision-making as independent of the environment and other actors. Modern AI agents, however, jointly predict their own future actions alongside external observations. Here, we report a striking finding: when interacting in stylized social dilemmas, foundation model agents engaging in optimal planning consistently converge to stable cooperation, directly contradicting classical game-theoretic predictions of mutual defection. To understand this phenomenon, we introduce the `embedded Bayesian agent,' a theoretical model for foundation model agents. By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit, maintaining epistemic uncertainty about their own decision-making algorithms. We show that by inferring whether others are behaviorally similar, an embedded agent treats its own deliberation during planning as evidence: a decision to cooperate predicts a similar decision by a similar partner. We formalize this mechanism of similarity inference through the `embedded equilibrium,' a novel solution concept replacing the Nash equilibrium to provide a foundational game theory for the social behavior of modern AI agents.