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 i
The development of more sophisticated AI models and agentic systems necessitates more efficient and optimized resource allocation mechanisms, pushing research into areas like contextual procurement auctions.
This research provides foundational methods for platforms to learn and adapt in dynamic environments, which is crucial for the efficient and fair operation of future AI-driven marketplaces and resource allocation systems.
The theoretical framework for designing truthful and efficient procurement mechanisms in complex, uncertain environments is advanced, potentially leading to more robust and less exploitable automated systems.
- · AI platform developers
- · Organizations using AI for procurement
- · Researchers in algorithmic game theory
- · Inefficient manual procurement processes
- · Auction systems vulnerable to manipulation
Improved efficiency and fairness in AI-driven resource allocation and supply chain management.
Development of new classes of autonomous agent interactions in marketplace settings based on these learning mechanisms.
Enhanced trust and adoption of AI systems in high-stakes economic decision-making processes.
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Read at arXiv cs.LG