Budget-feasible procurement auctions play a pivotal role in various AI-driven marketplaces, such as data acquisition and crowdsourcing, where a buyer with a limited budget seeks to procure services from strategic sellers with private costs. While numerous budget-feasible mechanisms have been proposed for the classic objective of maximizing the buyer's valuation, the more challenging and economically significant objective of social welfare maximization has only recently been studied, and existing approaches still sacrifice budget feasibility, thereby limiting their practical applicability. In this paper, we bridge this gap by proposing BFM-SWM, the first budget-feasible mechanism with provable approximation guarantees for submodular welfare maximization in procurement auctions. Our mechanism satisfies standard economic properties, including truthfulness, individual rationality, and non-negative auctioneer surplus. As a by-product, we develop BFM-VM, a variant tailored for valuation maximization, which achieves a deterministic approximation ratio of $1/(12+4\sqrt{3})$ for general submodular functions, substantially improving upon the best-known deterministic ratio of $1/64$ established by [Balkanski et al., SODA 2022], while reducing the running time from $\mathcal{O}(n^2\log n)$ to $\mathcal{O}(n\log n)$. Extensive experiments demonstrate the efficiency and effectiveness of our mechanisms.

翻译：预算可行采购拍卖在数据采集、众包等人工智能驱动的市场中扮演关键角色，其中预算有限的买家需从拥有私有成本的策略性卖家处采购服务。尽管针对买家估值最大化这一经典目标已提出众多预算可行机制，但更具挑战性且具有经济重要性的社会总福利最大化目标近期才被研究，现有方法仍需牺牲预算可行性，从而限制了其实际应用。本文通过提出BFM-SWM填补了这一空白，该机制是首个针对采购拍卖中子模福利最大化问题具有可证明近似保证的预算可行机制。我们的机制满足标准经济性质，包括真实性、个体理性及非负拍卖方盈余。作为副产品，我们开发了BFM-VM——面向估值最大化优化的变体，对一般子模函数实现$1/(12+4\sqrt{3})$的确定性近似比，较[Balkanski等人，SODA 2022]建立的已知最佳确定性比$1/64$有显著提升，同时将运行时间从$\mathcal{O}(n^2\log n)$降至$\mathcal{O}(n\log n)$。大量实验证明了我们机制的高效性与有效性。