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 many undesirable properties and loses its dominant theoretical advantages in single-item scenarios. In particular, second price auction brings equilibrium multiplicity, non-monotonicity, vulnerability to exploitation by both bidders and even auctioneers, and PPAD-hardness for the system to reach a steady-state. 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 advertising slots 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 candidate solutions for practitioners.

翻译：拍卖设计文献中的大多数工作假设竞拍者基于每个单独拍卖中可获取的信息理性行事。然而，在当今在线广告市场这一拍卖设计最重要的现实应用场景中，优化竞标所需的数据和计算能力仅由拍卖设计者掌握，广告主只能通过为广告活动设定效果目标（点击量、转化量等）参与其中。本文聚焦于广泛采用于众多全球级自动竞价平台的、具有投资回报率（ROI）约束的价值最大化广告活动。通过理论分析及在合成数据与真实数据上的实验，我们发现第二价格拍卖展现出许多不良特性，并丧失了其在单物品场景下的显著理论优势。具体而言，第二价格拍卖带来均衡多重性、非单调性、易被竞拍者甚至拍卖者利用的脆弱性，以及系统达到稳态的PPAD难度。我们还进一步探讨了自动竞价机制在效率与策略证明性之外的更广泛影响。其中，均衡多重性和输入敏感性导致广告主效用不稳定。此外，竞拍者之间以及广告位之间的相互干扰给A/B测试引入了偏差，这阻碍了平台中甚至非竞价组件的开发。上述现象在实践中已被广泛观测到，而我们的结果表明其根本原因之一可能在于底层自动竞价机制本身。为应对这些挑战，我们为实践者提供了建议与候选解决方案。