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AI Alignment through a Game-theoretic Lens: A Survey — arXiv:2608.27910

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27910
License (as archived): CC0-1.0
Archived: 2026-10-17T10:47:18.660Z
SHA-256 of the archived text: 9078a0c67e368b0d3e8635c374aa9d65a24809d3ec1cc71cf5bd2285bb00a4a3
Stable URL of this page: https://metraipolis.com/documento/dc_de0df005d47a

How to cite

AI Alignment through a Game-theoretic Lens: A Survey — arXiv:2608.27910 — arXiv (en), https://arxiv.org/abs/2608.27910 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_de0df005d47a (Metraipolis archive dc_de0df005d47a, sha256 9078a0c67e368b0d3e8635c374aa9d65a24809d3ec1cc71cf5bd2285bb00a4a3)

BibTeX

@misc{dcde0df005d47a,
  title = {AI Alignment through a Game-theoretic Lens: A Survey — arXiv:2608.27910},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27910},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_de0df005d47a; sha256 9078a0c67e368b0d3e8635c374aa9d65a24809d3ec1cc71cf5bd2285bb00a4a3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27910. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT).
Pagina del lavoro: https://arxiv.org/abs/2608.27910 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:33:42.366Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).