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拍卖机制。现有的多物品拍卖场景研究大致可分为基于RegretNet的方法和仿射最大化器拍卖方法。然而，前者无法严格保证占优策略激励相容性，而后者因分配候选数量庞大面临可扩展性问题。为解决这些局限，我们提出AMenuNet——一种可扩展的神经网络，它通过竞标者和物品表示构建AMA参数（甚至包含分配菜单）。凭借AMA属性，AMenuNet始终满足占优策略激励相容和个体理性，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在上下文和非上下文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，对不同设置具有出色泛化能力，并能识别出有效的确定性分配。总体而言，我们的方法为自动化DSIC拍卖设计提供了有效解决方案，在多种设置下兼具改进的可扩展性和强劲的收益表现。