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部分工作假设竞拍者基于每次拍卖可获得的信息理性行事。然而，在当今在线广告市场（拍卖设计最重要的现实应用之一）中，最优化出价所需的数据和计算能力仅对拍卖设计者可用，广告主只能通过为广告活动设置性能目标（点击量、转化量等）来参与。本文聚焦于采用投资回报率约束的价值最大化广告活动，该机制被广泛用于全球许多大规模自动出价平台。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呈现出诸多不良性质，并在单品场景中丧失了其理论优势。具体而言，第二价格拍卖导致均衡多重性、非单调性、易受竞拍者乃至拍卖者的利用，且系统达到稳态的PPAD硬度问题。我们还探讨了自动出价机制在效率和策略防护之外更广泛的影响。特别是，均衡多重性与输入敏感性导致广告主效用不稳定。此外，竞拍者之间及广告位间的干扰为A/B测试引入偏差，阻碍平台非出价组件的开发。上述现象在实践中已被广泛观察，而我们的结果表明，其中原因之一可能源于自动出价机制的内在特性。为应对这些挑战，我们为从业者提供了建议与候选解决方案。