发表机构
MIT; IIT Bombay（麻省理工学院; 印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对策略性智能体网络中的干预问题，提出Poll轮询算法，通过基于中心性的价格分解实现高效、可扩展且激励相容的福利最大化。
AI 中文摘要
一个在策略性智能体网络中的规划者面临三个相互交织的挑战：最优方案依赖于智能体的私有信息，被查询的智能体可能虚报信息以引导结果，以及精确计算无法扩展。我们在具有异质私有技术的多活动网络博弈中研究这些挑战，其中规划者设定非歧视性价格。我们证明，最优价格允许基于中心性的福利核分解：每个智能体的贡献与其在按智能体跨活动偏好重新加权的网络中的平方中心性成比例。这一分解催生了Poll算法，一种轮询算法，其中规划者每轮采样一个智能体，在其邻域内短暂行走，并根据局部报告更新价格。从同一分解中衍生出三种效率形式：在计算上，Poll使用的操作数显著少于精确计算和其他分布式方法，在一个拥有超过300,000个智能体的真实世界网络上，所需通信量最多减少三个数量级；在统计上，其查询复杂度随拓扑和偏好异质性扩展，而非显式随人口规模扩展；在经济上，它收敛到福利最大化价格，同时允许特定行为的实现，这些实现诱导真实报告并检测对抗性偏差。
英文摘要
A planner in a network of strategic agents faces three entangled challenges: the optimum depends on agents' private information, queried agents may misreport to steer the outcome, and exact computation does not scale. We study these challenges in multi-activity network games with heterogeneous private technologies, in which the planner sets non-discriminatory prices. We show that the optimal prices admit a centrality-based decomposition of the welfare kernel: each agent's contribution scales with its squared centrality in a network reweighted by agents' preferences across activities. This decomposition motivates Poll, a polling algorithm in which the planner samples one agent per round, walks briefly through the agent's neighborhood, and updates the price from a local report. From the same decomposition flow three forms of efficiency: computationally, Poll uses significantly fewer operations than exact computation and other distributed methods, requiring up to three orders of magnitude less communication on a real-world network with over 300,000 agents; statistically, its query complexity scales with topology and preference heterogeneity rather than explicitly with population size; and economically, it converges to welfare-maximizing prices while admitting behavior-specific implementations that induce truthful reports and detect adversarial deviations.
CommentsPublished as a conference paper at NeurIPS 2026
