Trustworthy Market Design for the Low-Altitude Economy: Economic Theory, Geospatial Simulation, and AI-Agent Experiments

Vickrey’s Nobel-recognized auction insight, generalized by Clarke and Groves, shows how private incentives can support collective welfare. That logic begins with a basic question: what service is the market actually allocating? In the low-altitude economy, users value a complete time-connected journey. Its route-time cells are complements, and an isolated cell may have no value. We show that complementarity can place even bundle-aware VCG outside the core. Selling cells separately creates another problem: users may pay for fragments that form no usable journey. We construct trajectory VCG to allocate and price compatible whole journeys, then compare it with first-come-first-served (FCFS) and per-slot VCG. Across paired synthetic episodes, map-derived networks, seasonal demand, and disruption scenarios, trajectory VCG modestly improves welfare, preserves FCFS-level complete service and lower-tail outcomes, and eliminates payments for incomplete journeys. Per-slot VCG performs worse on welfare, completion, fairness, and payment integrity. A matched $N=4$ experiment asks a second question: do different learning agents respond alike to theoretically truthful incentives? Under trajectory VCG, stateless mean-based learners report 0.981 of value. Stateful Q-learners report 0.929, which is 5.2 percentage points lower. Mixed populations narrow this gap, while a larger Q-learner share reduces revenue before it materially changes welfare. The data do not support the proposed explanation based on foresight and public market history. Our theoretical and geospatial results establish allocation-unit integrity: trustworthy markets should allocate the complete service users value. Our matched-agent experiment identifies algorithm-dependent reporting under theoretically truthful incentives. Together, these results connect economic theory, AI-agent evaluation, and public governance for safety-critical infrastructure.