Contextual Procurement Auctions with Bandit Learning

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.