Joint Incentive Contract and Assignment Design for Mobile Networked Platforms with Endogenous Supply

Abstract

Mobile networked platforms such as crowdsensing markets, edge-computing exchanges, vehicular service systems, and sponsored-content programs must coordinate two coupled levers: an assignment rule that decides who serves each request and an incentive contract that determines how realized value is shared. In all four settings, supply is endogenous, that is, it depends on the platform's own decisions: providers remain active only when expected earnings cover their private participation cost. We study a discounted platform problem in which assignment and revenue sharing jointly shape future supply. We calibrate the tractable one-period objective using an auxiliary memoryless discounted surrogate: it maximizes a global lower bound on that surrogate objective, and near high-survival stationary operating points its retention coefficient becomes the local shadow price of discounted future welfare within the surrogate model. This yields an Equimarginal Principle requiring efficient platforms to equalize the marginal retention efficiency of assignment and contracting. Under complete information, we obtain an urgency-adjusted assignment score, a KKT condition for the contract, and a Frank-Wolfe coordinate-ascent benchmark with an O(t/K) joint stationarity rate. Under hidden provider quality, we develop SE-RW-UCB, a subsidized-exploration algorithm with retention-weighted contextual assignment and a learned safe contract floor. A KuaiRec-based online recommendation benchmark shows that separated designs leave a sizable retention gap. In that benchmark, Oracle Fixed is the profit leader, whereas SE-RW-UCB attains the strongest high-type and all-provider retention at an interior contract. The service-quality differences are comparatively small, indicating that the main leverage of co-design in this calibration lies in supply preservation rather than per-request quality gains.