AI Alignment through a Game-theoretic Lens: A Survey
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Computer Science > Artificial Intelligence
arXiv:2608.27910 (cs)
[Submitted on 28 Aug 2026]
Title:AI Alignment through a Game-theoretic Lens: A Survey
Authors:Yanan Cai, Zhongrui Zhao, Zhigang Lu, Ickjai Lee, Wei Emma Zhang, Minhui Xue, Yihong Zhang, Shuchao Pang, Wei Xiang
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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.
| Comments: | This paper has been accepted by EMNLP-2026 as a main conference paper |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2608.27910 [cs.AI] |
| (or arXiv:2608.27910v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.27910
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled AI Alignment through a Game-theoretic Lens: A Survey, by Yanan Cai and 8 other authors
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