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Most of the work in auction design literature assumes that bidders behaverationally based on the information available for every individual auction, andthe revelation principle enables designers to restrict their efforts toincentive compatible (IC) mechanisms. However, in today's online advertisingmarkets, one of the most important real-life applications of auction design,the data and computational power required to bid optimally are only availableto the auction designer, and an advertiser can only participate by settingperformance objectives and constraints for its proxy auto-bidder provided bythe platform.  The prevalence of auto-bidding necessitates a review of auction theory. Inthis paper, we examine properties of auto-bidding markets through the lens ofROI-constrained value-maximizing campaigns, which are widely adopted in manyglobal-scale online advertising platforms. Through theoretical analysis andempirical experiments on both synthetic and realistic data, we find that secondprice auction exhibits many undesirable properties (equilibrium multiplicity,computational hardness, exploitability by bidders and auctioneers, instabilityof bidders' utilities, and interference in A/B testing) and loses its dominanttheoretical advantages in single-item scenarios. Some of these phenomena havebeen identified in literature (for budget-constrained auto-bidders) and widelyobserved in practice, and we show that they are actually deeply rooted in theproperty of (single-round) incentive compatibility. Although many complexdesigns have been proposed in literature, first and second price auctionsremain popular in industry. We hope that our work could bring new perspectivesto the community and benefit practitioners to attain a better grasp ofreal-world markets.

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