机制设计还不够：面向合作式AI的亲社会智能体

Computer Science > Computer Science and Game Theory arXiv:2605.08426 (cs) [Submitted on 8 May 2026 (v1), last revised 22 Sep 2026 (this version, v3)] Title:Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI Authors:Xuanqiang Angelo Huang, Charlie Tharas, Samuele Marro, Van Q. Truong, Bernhard Schölkopf, Emanuele La Malfa, Zhijing Jin View a PDF of the paper titled Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI, by Xuanqiang Angelo Huang and 6 other authors View PDF HTML (experimental) Abstract:Ensuring that AI agents behave safely and beneficially when interacting with other parties has emerged as one of the central challenges of modern AI safety. While mechanism design, as the theory of designing rules to align individual and collective objectives, can incentivize cooperative behavior, it is still an open question whether it alone is sufficient to maximize LLM agents' social welfare. This work proves that the answer is negative: drawing from incomplete contract theory, we formally show that when contracts cannot distinguish all relevant future contingencies, there is a strictly positive welfare loss that no realistic mechanism can eliminate. We show that prosocial agents, who weigh others' welfare alongside their own, can close this gap and achieve outcomes that are socially superior and individually beneficial. Experimentally, we show that in multi-agent resource-allocation environments and canonical social dilemmas where agents are powered by large language models, prosociality is beneficial. The implication for AI safety is clear: to enable cooperative interactions at scale, designing adequate mechanisms is not sufficient; agents must be built to be intrinsically prosocial. Comments: Subjects: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI) Cite as: arXiv:2605.08426 [cs.GT] (or arXiv:2605.08426v3 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2605.08426 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xuanqiang 'Angelo' Huang [view email] [v1] Fri, 8 May 2026 19:37:07 UTC (3,121 KB) [v2] Tue, 2 Jun 2026 11:37:31 UTC (3,121 KB) [v3] Tue, 22 Sep 2026 14:24:36 UTC (3,121 KB) Full-text links: Access Paper: View a PDF of the paper titled Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI, by Xuanqiang Angelo Huang and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.GT new | recent | 2026-05 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)