Air traffic can be significantly disrupted by weather. Pathfinder operations involve assigning a designated aircraft to assess whether airspace that was previously impacted by weather can be safely traversed through. Despite relatively routine use in air traffic control, there is little research on the underlying multi-agent decision-making problem. We seek to address this gap herein by formulating decision models to capture the operational dynamics and implications of pathfinders. Specifically, we construct a Markov chain to represent the stochastic transitions between key operational states (e.g., pathfinder selection). We then analyze its steady-state behavior to understand long-term system dynamics. We also propose models to characterize flight-specific acceptance behaviors (based on utility trade-offs) and pathfinder selection strategies (based on sequential offer allocations). We then conduct a worst-case scenario analysis that highlights risks from collective rejection and explores how selfless behavior and uncertainty affect system resilience. Empirical analysis of data from the US Federal Aviation Administration demonstrates the real-world significance of pathfinder operations and informs future model calibration.

翻译：天气可能导致空中交通严重中断。探路者操作涉及指派指定飞机评估此前受天气影响的空域能否安全通行。尽管该操作在空中交通管制中相对常规，但关于其背后的多智能体决策问题的研究却鲜有涉及。本文旨在通过构建决策模型来弥补这一空白，以捕捉探路者操作的运行动态及其影响。具体而言，我们构建了一个马尔可夫链来表征关键运行状态（例如探路者选择）之间的随机转移，进而通过稳态行为分析理解系统长期动态。我们还提出了模型来描述基于效用权衡的航班特定接受行为以及基于序贯报价分配的探路者选择策略。通过最坏情景分析，我们揭示了集体拒绝带来的风险，并探讨了无私行为与不确定性对系统韧性的影响。基于美国联邦航空管理局数据的实证分析表明了探路者操作的实际意义，并为未来模型校准提供了依据。