Dynamic Matching Under Capacity Constraints: Integrating Gravity Models and Strategic Queuing in Gig Economy Platforms

Online talent markets (OTMs) are capacity-constrained service platforms in which heterogeneous consumer preferences interact with finite provider time. This article develops an agent-based simulation that couples a gravity-style provider-choice rule with queueing behaviors, including congestion-induced rerouting, reneging, and experience-based updating. The model identifies a “busy-but-brittle” regime in which utilization remains high while feasible matching slack collapses. To diagnose this state, the article introduces the platform dynamism index (PDI), a composite of feasible optionality and allocation dispersion. Monte Carlo results indicate that feasibility is the dominant separator in the main regime comparison, while a dispersion-stress robustness test demonstrates that entropy becomes informative when concentrated targeting persists despite ample spare capacity.