用大模型模拟选举领导，显著提升群体合作与生存能力。
Evaluating Cooperation in LLM Social Groups through Elected Leadership
- 通过选举产生领导者和候选人议程，构建多智能体治理框架。
- 领导机制使社会福利提升55.4%，生存时间延长128.6%。
- 适合研究群体协作、社会决策与大模型治理的学者参考。
管理共用资源需要个体通过合作与自我治理制定长期策略，以避免集体失败。尽管基础模型在合作场景中展现出潜力，但现有多智能体研究极少探讨结构化领导与选举机制是否能提升集体决策质量。人类社会普遍存在的这一组织特征缺失，成为当前方法的重大短板。本文通过基于大模型的多智能体仿真，直接检验领导与选举能否促进社会福祉与合作。我们提出一个开源框架，模拟选举产生的角色与候选议程，并在受控治理条件下开展实证研究。实验表明，采用选举领导机制后，社会福利得分提升55.4%，生存时间延长128.6%，覆盖多个高性能大模型。通过构建智能体社交图并计算中心性指标，评估领导者社会影响力；结合对领导人言辞的情感分析，揭示其修辞风格与合作倾向。本工作为未来研究多智能体系统中的选举机制奠定了基础，以应对复杂社会困境。
原文摘要 · Abstract (English)
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.
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