62 research outputs found
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
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