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This research addresses the intersection of mechanism design and reinforcement learning by optimizing welfare regret in repeated procurement auctions. The authors propose a "frozen-payment UCB" algorithm that balances truthful bidding incentives with efficient context-dependent allocation, achieving optimal theoretical bounds under specific margin conditions. This work advances machine learning applications in economic systems where trust and incentive compatibility are critical for deployment.
Contextual Procurement Auctions with Bandit Learning
arXiv:2607.05813v3 Announce Type: replace-cross Abstract: We study repeated procurement auctions in which producers have private costs and the platform must learn the context-dependent value of selecting each producer. We evaluate performance by welfare regret: the cumulative loss in total surplus relative to the full-information efficient rule that knows the context-dependent values and true producer costs. The natural UCB allocation rule achieves $\widetilde O(\sqrt{ngT})$ welfare regret under truthful bids, but its adaptive, bid-dependent learning path does not by itself ensure truthfulness. To obtain exact incentives, we first design a bid-independent explore-then-commit mechanism with empirical threshold payments; it is dominant-strategy truthful and has $\widetilde O((ng)^{1/3}T^{2/3})$ regret. We then introduce frozen-payment UCB, which estimates payments from initial bid-independent exploration but continues allocation learning by UCB. Under a truthful-path margin condition, the frozen-payment UCB is approximately truthful with an average per-round deviation gain $\widetilde O(T^{-1/4})$ for fixed $n$, $g$. Under truthful bidding, it achieves $\widetilde O(\sqrt{ngT})$ welfare regret, matching the UCB rate. A lower bound shows that this regret-incentive tradeoff is tight within the frozen critical-payment class.
Abstract:We study repeated procurement auctions in which producers have private costs and the platform must learn the context-dependent value of selecting each producer. We evaluate performance by welfare regret: the cumulative loss in total surplus relative to the full-information efficient rule that knows the context-dependent values and true producer costs. The natural UCB allocation rule achieves $\widetilde O(\sqrt{ngT})$ welfare regret under truthful bids, but its adaptive, bid-dependent learning path does not by itself ensure truthfulness. To obtain exact incentives, we first design a bid-independent explore-then-commit mechanism with empirical threshold payments; it is dominant-strategy truthful and has $\widetilde O((ng)^{1/3}T^{2/3})$ regret. We then introduce frozen-payment UCB, which estimates payments from initial bid-independent exploration but continues allocation learning by UCB. Under a truthful-path margin condition, the frozen-payment UCB is approximately truthful with an average per-round deviation gain $\widetilde O(T^{-1/4})$ for fixed $n$, $g$. Under truthful bidding, it achieves $\widetilde O(\sqrt{ngT})$ welfare regret, matching the UCB rate. A lower bound shows that this regret-incentive tradeoff is tight within the frozen critical-payment class.
Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
arXiv:2607.05813 [cs.GT]
(or arXiv:2607.05813v3 [cs.GT] for this version)
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