Vulnerabilities of Single-Round Incentive Compatibility in Auto-bidding: Theory and Evidence from ROI-Constrained Online Advertising Markets

Most of the work in auction design literature assumes that bidders behave rationally based on the information available for every individual auction, and the revelation principle enables designers to restrict their efforts to incentive compatible (IC) mechanisms. 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 and constraints for its proxy auto-bidder provided by the platform. The prevalence of auto-bidding necessitates a review of auction theory. In this paper, we examine properties of auto-bidding markets through the lens of ROI-constrained value-maximizing campaigns, which are widely adopted in many global-scale online advertising platforms. Through theoretical analysis and empirical experiments on both synthetic and realistic data, we find that second price auction exhibits many undesirable properties (equilibrium multiplicity, computational hardness, exploitability by bidders and auctioneers, instability of bidders' utilities, and interference in A/B testing) and loses its dominant theoretical advantages in single-item scenarios. Some of these phenomena have been identified in literature (for budget-constrained auto-bidders) and widely observed in practice, and we show that they are actually deeply rooted in the property of (single-round) incentive compatibility. Although many complex designs have been proposed in literature, first and second price auctions remain popular in industry. We hope that our work could bring new perspectives to the community and benefit practitioners to attain a better grasp of real-world markets.