Contextual Procurement Auctions with Bandit Learning

arXiv:2607.05813v1 Announce Type: cross Abstract: We study repeated contextual procurement auctions in which the platform must learn context-dependent product values from bandit feedback. We give an exactly truthful explore-then-commit mechanism with widetilde O((ng)^{1/3}T^{2/3}) regret. We also give a frozen-payment UCB mechanism with a regret-incentive tradeoff: the near-UCB tuning attains (widetilde O(sqrt{ngT})) welfare regret, while for fixed (n,g) its total incentive error is (widetilde O(T^{3/4})); the balanced tuning gives (widetilde O(T^{2/3})) on both scales. Regret is measured as welfare loss relative to the full-information efficient allocation. We prove a matching lower bound for the frozen-payment regret-incentive tradeoff.

Source: arXiv cs.LG | 2026-07-08