AI Alignment through a Game-theoretic Lens: A Survey

2026-08-28

AI Summary

本文通过博弈论视角审视AI对齐问题，针对偏好多样性、对齐优先级和时间动态性三大挑战，综述了现有方法并探讨了构建鲁棒、自适应及可验证AI系统所面临的难题。

Abstract

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.

Problem

• AI Alignment
• Game-theoretic Lens
• Preference Diversity
• Dynamic Interactions
• Complex Human Values

Innovation

• Game-theoretic Analysis
• Preference Diversity
• Temporal Dynamics