@inproceedings{cai-etal-2026-ai,
title = "{AI} Alignment through a Game-theoretic Lens: A Survey",
author = "Cai, Yanan and
Zhao, Zhongrui and
Lu, Zhigang and
Lee, Ickjai and
Zhang, Wei Emma and
Xue, Jason and
Zhang, Yihong and
Pang, Shuchao and
Xiang, Wei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2607/",
pages = "59233--59253",
ISBN = "979-8-89176-459-0",
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."
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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.</abstract>
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%0 Conference Proceedings
%T AI Alignment through a Game-theoretic Lens: A Survey
%A Cai, Yanan
%A Zhao, Zhongrui
%A Lu, Zhigang
%A Lee, Ickjai
%A Zhang, Wei Emma
%A Xue, Jason
%A Zhang, Yihong
%A Pang, Shuchao
%A Xiang, Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F cai-etal-2026-ai
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2607/
%P 59233-59253
Markdown (Informal)
[AI Alignment through a Game-theoretic Lens: A Survey](https://aclanthology.org/2026.emnlp-main.2607/) (Cai et al., EMNLP 2026)
ACL
- Yanan Cai, Zhongrui Zhao, Zhigang Lu, Ickjai Lee, Wei Emma Zhang, Jason Xue, Yihong Zhang, Shuchao Pang, and Wei Xiang. 2026. AI Alignment through a Game-theoretic Lens: A Survey. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 59233–59253, Budapest, Hungary. Association for Computational Linguistics.