The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectrum allocation, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson's 1981 work characterizing single-item optimal auctions, there has been limited progress outside of restricted settings. A recent paper by D\"utting et al. circumvents analytic difficulties by applying deep learning techniques to, instead, approximate optimal auctions. In parallel, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.

翻译：以强有力的奖励保证实现收入最大化的拍卖设计是经济理论的一个核心关切。 计算拍卖使在线广告、采购、频谱分配和众多金融市场成为可能。 分析这一空间的进展十分困难;自Myerson1981年将单项最佳拍卖定性为单一项目最佳拍卖以来,在限制环境之外进展有限。 D\"utting et al.最近发表的论文绕过分析困难,将深层学习技术应用于近似最佳的拍卖。 与此同时,Ilvento et al.和其他团体的新研究在拍卖设计方面形成了公平的概念。受这些进展的启发,我们在本文中推广了近似拍卖的技术,利用深层学习来解决对公平的关切,同时保持高收入和强有力的奖励保证。