Most of the work in auction design literature assumes that bidders behave rationally based on the information available for each individual auction. However, in today's online advertising markets, one of the most important real-life applications of auction design, the data and computational power required to bid optimally are only available to the auction designer, and an advertiser can only participate by setting performance objectives (clicks, conversions, etc.) for the campaign. In this paper, we focus on value-maximizing campaigns with return-on-investment (ROI) constraints, which is widely adopted in many global-scale auto-bidding platforms. Through theoretical analysis and empirical experiments on both synthetic and realistic data, we find that second price auction exhibits counterintuitive behaviors in the resulted equilibrium and loses its dominant theoretical advantages in single-item scenarios. At the market scale, the equilibrium structure is complicated and opens up space for bidders and even auctioneers to exploit. We also explore the broader impacts of the auto-bidding mechanism beyond efficiency and strategyproofness. In particular, the multiplicity of equilibria and the input sensitivity make advertisers' utilities unstable. In addition, the interference among both bidders and goods introduces bias into A/B testing, which hinders the development of even non-bidding components of the platform. The aforementioned phenomena have been widely observed in practice, and our results indicate that one of the reasons might be intrinsic to the underlying auto-bidding mechanism. To deal with these challenges, we provide suggestions and potential solutions for practitioners.

翻译：拍卖设计文献中的大多数工作假设竞标者基于每场拍卖的可得信息理性行事。然而，在当今的在线广告市场（拍卖设计最重要的现实应用之一）中，最优出价所需的数据和计算能力仅掌握在拍卖设计者手中，广告主只能通过为广告活动设定绩效目标（点击量、转化量等）来参与。本文聚焦于具有投资回报率（ROI）约束的价值最大化广告活动——这一模式被众多全球规模的自动竞价平台广泛采用。通过理论分析及在合成数据与真实数据上的实证实验，我们发现：第二价格拍卖在所达成的均衡中呈现出反直觉行为，并丧失了其在单物品场景中的主导性理论优势。在市场层面，均衡结构复杂化，为竞标者乃至拍卖者留下了可操作空间。我们还探讨了自动竞价机制对效率和策略证明性之外的更广泛影响。具体而言，均衡的多重性和输入敏感性导致广告主效用不稳定；此外，竞标者与商品之间的相互干扰为A/B测试引入偏差，阻碍了平台中甚至非竞价组件的开发。上述现象在实际中已被广泛观测到，而我们的结果表明，其原因之一可能源于底层自动竞价机制的内在特性。针对这些挑战，我们为从业者提供了建议与潜在解决方案。