Governing common-pool resources requires agents to develop enduring strategies through cooperation and self-governance to avoid collective failure. While foundation models have shown potential for cooperation in these settings, existing multi-agent research provides little insight into whether structured leadership and election mechanisms can improve collective decision making. The lack of such a critical organizational feature ubiquitous in human society presents a significant shortcoming of the current methods. In this work we aim to directly address whether leadership and elections can support improved social welfare and cooperation through multi-agent simulation with LLMs. We present our open-source framework that simulates leadership through elected personas and candidate-driven agendas and carry out an empirical study of LLMs under controlled governance conditions. Our experiments demonstrate that having elected leadership improves social welfare scores by 55.4% and survival time by 128.6% across a range of high performing LLMs. Through the construction of an agent social graph we compute centrality metrics to assess the social influence of leader personas and also analyze rhetorical and cooperative tendencies revealed through a sentiment analysis on leader utterances. This work lays the foundation for further study of election mechanisms in multi-agent systems toward navigating complex social dilemmas.

翻译：治理公共池塘资源需要智能体通过合作与自治发展持久策略，以避免集体失败。尽管基础模型在此类情境中已展现出合作潜力，现有多智能体研究对结构化领导力与选举机制能否改善集体决策的探讨仍十分有限。这种在人类社会中普遍存在的关键组织特征的缺失，暴露了当前方法的重大缺陷。本研究旨在通过多智能体模拟（基于LLM）直接探究领导力与选举是否能促进社会福利与合作的提升。我们提出开源框架，通过选举角色与候选驱动的议程模拟领导力，并在受控治理条件下对LLM开展实证研究。实验表明，在一系列高性能LLM中，设立选举领导使社会福利得分提升55.4%，生存时间延长128.6%。通过构建智能体社会网络图，我们计算中心性指标以评估领导角色的社会影响力，同时基于对领导言论的情感分析揭示其修辞与协作倾向。本研究为多智能体系统中选举机制的进一步探索奠定了基础，以应对复杂的社会困境。