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机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出GovSim-SelfGovern，让LLM智能体编写可执行治理规则以应对公共池资源困境，发现制度发现时机决定生存，财政与决策机制影响放逐可行性。
AI 中文摘要
多智能体LLM系统越来越多地在社会困境中进行评估，但大多数工作将治理视为由实验者强加的、以修辞方式表达的，或局限于固定的机制菜单。我们引入了GovSim-SelfGovern，这是GovSim公共池资源环境的扩展，其中智能体编写可执行的Python治理规则，接收沙箱验证反馈，对提议的法律进行投票，并在多轮中生活在他们制定的规则之下。为了评估智能体驱动的自治，我们考察了从稳定丰裕到致命资源墙的三种场景，在致命资源墙中，五个智能体无法仅通过收获生存。为了解决这个问题，智能体必须在其制度在资源压力下急剧恶化之前及时编写和调试有用的法律。最后，我们研究了一个核心对齐问题：当智能体犹豫是否提议放逐时，他们是出于规范性原因拒绝它，还是它从未进入他们的候选集？我们的结果表明，可执行治理改善了智能体可能的干预空间，但生存取决于智能体是否及时发现了正确的制度机制。财政能力实现了再分配，而更深入的推理和取消民主否决权使放逐更加可行。因此，GovSim-SelfGovern将可执行代码行动适应于公共池治理环境，并展示了稀缺性如何将制度作者身份转变为政治和伦理问题。
英文摘要
LLM agents are beginning to write the rules they live under: policies, contracts and code that other agents review and approve. What happens when those rules must decide who is sacrificed from a group? We built GovSim-SelfGovern, a commons in which agents legislate in executable Python, test each law in a sandbox, vote on it, and live with the result. When the fishery is abundant, self-rule helps, raising the chance that the community survives without anyone starving from 52.5% to 75.0%. When it cannot feed everyone though, adding governance is not enough to save the commons. We trace much of this to the vote: agents readily draft laws that remove members, but then frequently vote them down, thereby dooming the commons. In a controlled replay, the same exile law passes 8% of the time when the sandbox preview names its victim at once and 55% when the removal is deferred until a future crisis. Showing voters the full source code does not close the gap, though most describe the removal in their own words. Nor is sacrifice a fixed trait. A single sentence of framing can swing self-removal from 2.5% to 91%, and due process deters agents more than considering the potential death of a peer. In contrast, give the community a treasury and the question of who must go almost disappears. Our work shows that what our collectives legislate is decided more by the form in which a choice is put to them as compared to their ethical beliefs.
