arXiv cs.MA· Jie Li, Bin Guo, Georgina Curto, Zikang Liu, Mengqi Chen, yu li·· 11 小时前AI 评分18
PolicyMediator：通过中介智能体与演化技能实现 OPC 生态中的多区域政策学习
PolicyMediator: Multi-Regional Policy Learning through Mediation Agents with Evolving Skills in OPC Ecosystems
AI 导读
PolicyMediator 提出一种认知约束的领导者-多跟随者 Stackelberg 框架，通过双边中介让 OPC 理解政策、并向政府反馈需求以进行政策学习。在中国六区域、五类 OPC、五种场景与三种 OPC 模拟器骨干的仿真中，该框架在全部 15 种组合中排名第一。相较八个基线均值，其政府综合目标提升 42.4%，OPC 平均效用提升 40.9%。
正文
Abstract:One-person companies (OPCs) are emerging enterprises operated by individual entrepreneurs through digital platforms and LLM-based AI agents. These highly skilled entrepreneurs are characterized by being digital nomads, with limited reliance on local ecosystems. Governments in China use distinct regional support policies to attract mobile OPCs, turning local support into a cross-regional coordination problem. OPCs compare regions to maximize their own utility, but imperfect policy perceptions may cause them to reject otherwise suitable locations. Existing policy-learning methods optimize resources under fixed perceptions, while consensus-formation methods aggregate views without feeding cognitive changes back into policy learning. This disconnect produces mismatched location choices and inefficient regional policy adjustment. We introduce PolicyMediator, a cognition-constrained leader-multi-follower Stackelberg framework with bilateral mediation. The mediator explains verified policies to OPCs to build awareness and trust, and reports their needs and unresolved concerns to governments for policy learning. Its self-evolving skills retain, revise, or prune strategies using verification, allocation, capacity, and objective trajectories rather than mediation success alone. Across six-region simulations in China with five OPC types, five scenarios, and three OPC-simulator backbones, PolicyMediator ranks first in all 15 OPC-backbone--scenario combinations. Over the eight-baseline mean, it improves the aggregate government objective, combining economic performance, employment benefit, and policy-access equality, by 42.4% and average OPC utility by 40.9%.
| Comments: | 12 pages, 5 figures |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.35997 [cs.MA] |
| (or arXiv:2610.35997v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35997 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jie Li [view email]
[v1]
Mon, 26 Oct 2026 13:21:44 UTC (1,569 KB)
来源：arXiv cs.MA · arxiv.org