Automated auction design aims to find empirically high-revenue mechanisms through machine learning. Existing works on multi item auction scenarios can be roughly divided into RegretNet-like and affine maximizer auctions (AMAs) approaches. However, the former cannot strictly ensure dominant strategy incentive compatibility (DSIC), while the latter faces scalability issue due to the large number of allocation candidates. To address these limitations, we propose AMenuNet, a scalable neural network that constructs the AMA parameters (even including the allocation menu) from bidder and item representations. AMenuNet is always DSIC and individually rational (IR) due to the properties of AMAs, and it enhances scalability by generating candidate allocations through a neural network. Additionally, AMenuNet is permutation equivariant, and its number of parameters is independent of auction scale. We conduct extensive experiments to demonstrate that AMenuNet outperforms strong baselines in both contextual and non-contextual multi-item auctions, scales well to larger auctions, generalizes well to different settings, and identifies useful deterministic allocations. Overall, our proposed approach offers an effective solution to automated DSIC auction design, with improved scalability and strong revenue performance in various settings.

翻译：自动拍卖设计旨在通过机器学习寻找经验上高收益的机制。现有的多物品拍卖场景研究大致可分为类似RegretNet的方法和仿射最大化拍卖方法。然而，前者无法严格保证占优策略激励相容性，后者则因候选分配数量庞大而面临可扩展性问题。为解决这些局限性，我们提出AMenuNet——一种通过投标者和物品表示来构建AMA参数（甚至包括分配菜单）的可扩展神经网络。基于AMA的特性，AMenuNet始终满足DSIC和个体理性，并通过神经网络生成候选分配来增强可扩展性。此外，AMenuNet具有排列等变性，其参数数量与拍卖规模无关。我们通过大量实验证明，AMenuNet在上下文相关和无关的多物品拍卖中均优于强基线方法，能良好扩展至更大规模的拍卖场景，在不同设置下具有出色的泛化能力，并能识别出有用的确定性分配。总体而言，本文提出的方法为自动化DSIC拍卖设计提供了有效解决方案，在多种场景下兼具卓越的可扩展性与强收益表现。