Decentralized air traffic management systems offer a scalable alternative to centralized control, but often assume high levels of cooperation. In practice, such assumptions frequently break down since airspace sectors operate independently and prioritize local objectives. We address the problem of sector overload in decentralized air traffic management by proposing a mechanism that models self-interested behaviors based on best response dynamics. Each sector adjusts the departure times of flights under its control to reduce its own congestion, without any shared decision making. A tunable cooperativeness factor models the degree to which each sector is willing to reduce overload in other sectors. We prove that the proposed mechanism satisfies a potential game structure, ensuring that best response dynamics converge to a pure Nash equilibrium, under a mild restriction. In addition, we identify a sufficient condition under which an overload-free solution corresponds to a global minimizer of the potential function. Numerical experiments using 24 hours of European flight data demonstrate that the proposed algorithm substantially reduces overload even with only minimal cooperation between sectors, while maintaining scalability and matching the solution quality of centralized solvers.

翻译：分散式空中交通管理系统为集中式控制提供了可扩展的替代方案，但通常假设存在高度协作。实际上，由于空域扇区独立运行且优先考虑本地目标，此类假设往往难以成立。本文通过提出一种基于最优响应动态的自利行为建模机制，解决分散式空中交通管理中的扇区过载问题。每个扇区在不进行任何共享决策的情况下，调整其管辖航班的起飞时间以降低自身拥堵程度。可调节的协作因子用于建模各扇区愿意缓解其他扇区过载的意愿程度。我们证明该机制满足势博弈结构，在温和限制条件下确保最优响应动态收敛至纯纳什均衡。此外，我们确定了无过载解对应势函数全局最小值的充分条件。基于24小时欧洲航班数据的数值实验表明，即使扇区间仅存在最小程度的协作，所提算法仍能显著降低过载，同时保持可扩展性，并达到与集中式求解器相当的解质量。