With the advancement of machine learning, an increasing number of studies are employing automated mechanism design (AMD) methods for optimal auction design. However, all previous AMD architectures designed to generate optimal mechanisms that satisfy near dominant strategy incentive compatibility (DSIC) fail to achieve deterministic allocation, and some also lack anonymity, thereby impacting the efficiency and fairness of advertising allocation. This has resulted in a notable discrepancy between the previous AMD architectures for generating near-DSIC optimal mechanisms and the demands of real-world advertising scenarios. In this paper, we prove that in all online advertising scenarios, when all ad slots must be allocated, previous non-deterministic allocation AMD methods lead to the non-existence of feasible solutions in the vast majority of cases, resulting in a gap between the rounded solution and the optimal solution. Furthermore, we propose JTransNet, a transformer-based neural network architecture, designed for optimal deterministic-allocation and anonymous joint auction design. Although the deterministic allocation module in JTransNet is designed for the latest joint auction scenarios, it can be applied to other non-deterministic AMD architectures with minor modifications. Additionally, our offline and online data experiments demonstrate that, in joint auction scenarios, JTransNet significantly outperforms the considered baselines in terms of platform revenue.

翻译：随着机器学习的发展，越来越多的研究采用自动化机制设计（AMD）方法进行最优拍卖设计。然而，先前所有旨在生成满足近似占优策略激励相容（DSIC）的最优机制的AMD架构均未能实现确定性分配，部分架构还缺乏匿名性，从而影响了广告分配的效率与公平性。这导致先前用于生成近似DSIC最优机制的AMD架构与现实广告场景需求之间存在显著差距。本文证明，在所有在线广告场景中，当所有广告位必须被分配时，先前非确定性分配的AMD方法在绝大多数情况下会导致可行解不存在，从而在舍入解与最优解之间产生偏差。此外，我们提出JTransNet——一种基于Transformer的神经网络架构，旨在实现最优确定性分配与匿名联合拍卖设计。尽管JTransNet中的确定性分配模块是为最新的联合拍卖场景设计的，但经过少量修改即可应用于其他非确定性AMD架构。同时，我们的线下与线上数据实验表明，在联合拍卖场景中，JTransNet在平台收入方面显著优于所考虑的基线方法。