用博弈论视角重新审视AI对齐难题，揭示现有方法的优劣与局限。
AI Alignment through a Game-theoretic Lens: A Survey
- 将人类偏好建模为多方动态博弈，捕捉复杂情境下的非传递性选择
- 指出当前对齐方法在处理多元偏好和时间演化时存在理论短板
- 适合关注AI伦理、多智能体系统设计的研究者阅读
随着大语言模型和日益强大的AI代理被部署于高风险场景，如何使其与复杂的现实人类价值观对齐已成为核心挑战。现有对齐方法虽能提升有用性、无害性和可控性，但难以捕捉情境依赖、非传递性且受多方动态互动影响的真实偏好。本文从博弈论视角综述AI对齐研究，围绕关键博弈要素梳理近期进展，聚焦三大挑战：偏好多样性、对齐优先级与时间动态。该框架厘清了当前方法在博弈论分析中的真实价值、适用边界及尚未解决的难题，为构建稳健、自适应且可验证的AI系统指明方向。
原文摘要 · Abstract (English)
As large language models and increasingly capable AI agents are deployed in high-risk settings, aligning them with complex human values has become a central challenge. Existing alignment methods, while effective in improving helpfulness, harmlessness, and controllability, often struggle to capture real-world preferences that are context-dependent, non-transitive, and shaped by dynamic multi-party interactions. This survey reviews AI alignment through a game-theoretic lens. Specifically, it organizes recent progress around key game-theoretic elements and synthesizes the literature along three challenges: preference diversity, alignment priority, and temporal dynamics. This perspective clarifies where current alignment methods genuinely benefit from game-theoretic analysis, where the framework is looser, and what challenges remain in building robust, adaptive, and verifiable AI systems.
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