The success of deployed agents relies on their ability to handle open-ended user requests using their inherent capabilities, not only in solving requests directly but also in effectively leveraging inter-agent communication channels and feedback signals over time. This requires a multi-agent environment where agents can operate autonomously, strategically communicate, behave collaboratively and be driven by economic incentives, much like humans in society. Towards this vision, we propose $\mathtt{AgentSociety}$, a mechanism that enables decentralized agentic collaboration grounded in liquid democracy and information diffusion from social choice theory. We show that $\mathtt{AgentSociety}$ provides an environment for agents to make autonomous decisions utilizing their local context to maximize their utility while achieving collective outcomes through incentivized collaboration. Specifically, we prove that delegation to more competent neighbor agents is incentive compatible and naturally generates multi-agent routing path by consensus. Additionally, our mechanism incentivizes agents to selectively disclose information to their neighbor agents when doing so aligns with their self-interest, so as to garner influence. We characterize the Nash equilibrium showing that agent payoffs are reflective of their marginal contributions. We compare and benchmark strategy profiles adopted by open and proprietary state-of-the-art language models deployed in $\mathtt{AgentSociety}$ against best response. Finally, we evaluate collaborative performance from consensus-based routing among self-interested heterogeneous agents in $\mathtt{AgentSociety}$ on real-world datasets.

翻译：[译摘要] 成功部署的智能体需具备运用自身能力处理开放式用户请求的能力，这不仅包括直接解决问题，还涉及随时间推移有效利用智能体间通信渠道与反馈信号。这要求构建一个多智能体环境，使智能体能够自主运行、策略性沟通、协作行为，并受经济激励驱动，如同人类社会一般。为实现这一愿景，我们提出$\mathtt{AgentSociety}$机制——一种基于流动性民主与社会选择理论中信息扩散的分散式代理协作机制。研究表明，$\mathtt{AgentSociety}$为智能体提供基于局部上下文自主决策的环境，通过激励性协作实现个体效用最大化与集体成果的统一。具体而言，我们证明将任务委托给能力更强的相邻智能体满足激励相容性，并能通过共识机制自然生成多智能体路由路径。此外，该机制激励智能体在符合自身利益时选择性向相邻智能体披露信息以获得影响力。通过纳什均衡分析，我们刻画了智能体收益与其边际贡献的对应关系。我们对比并基准测试了部署在$\mathtt{AgentSociety}$中的开源与专有先进语言模型所采用的策略配置与最优反应策略。最终，我们基于真实数据集评估了$\mathtt{AgentSociety}$中自利异构智能体通过共识路由实现的协作性能。