带机器学习建议的自动竞价场景下的个体福利保障
Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice
AI总结：
该研究针对自动竞价场景下总福利提升可能损害个体竞价者福利的问题，分析了有无机器学习建议时的个体福利损失，提出将机器学习建议设为个性化保留价的VCG拍卖机制，给出个体福利下界并证明其最优性，还将结果拓展至GFP和GSP拍卖。
摘要：
在线广告渠道通常聚焦于最大化广告主总价值（或总福利），以提升长期留存率与渠道健康度。已有文献通过多种形式融入对广告主价值的机器学习预测（也称为机器学习建议）来研究拍卖设计，以提升总福利。然而，这类提升可能以个体竞价者的福利为代价，且未阐明特定广告主的竞价策略会如何影响福利。\n受此启发，我们针对有无机器学习建议的拍卖场景，分析了自动竞价环境下个体竞价者的福利损失，并揭示了广告主策略与这类损失之间的关联。具体而言，我们展示了当平台上的自动竞价者在遵守广告支出回报率（ROAS）约束的前提下最大化价值时，广告平台如何利用机器学习建议，将其设为个性化保留价，从而在总体和个体竞价者层面提升福利保障。在采用此类基于机器学习建议的保留价的并行VCG拍卖下，我们给出了个体自动竞价者的最坏情况福利下界保障，并证明该下界保障与机器学习建议的质量、以及自动竞价者竞价策略所引发的竞价规模呈正相关。此外，我们证明了一项不可能性结果：在存在基于同等质量机器学习建议的个性化保留价的情况下，任何具备匿名分配性质的 truthful（ truthful，即如实报价）机制——甚至可能是随机化机制——都无法实现比VCG普遍更优的个体福利保障。我们还将个体福利保障结果拓展至广义第一价格（GFP）和广义第二价格（GSP）拍卖。
英文摘要：
Online advertising channels have commonly focused on maximizing total advertiser value (or welfare) to enhance long-run retention and channel healthiness. Previous literature has studied auction design by incorporating machine learning predictions on advertiser values (also known as machine-learned advice) through various forms to improve total welfare. Yet, such improvements could come at the cost of individual bidders' welfare and do not shed light on how particular advertiser bidding strategies impact welfare. Motivated by this, we present an analysis on an individual bidder's welfare loss in the autobidding world for auctions with and without machine-learned advice, and also uncover how advertiser strategies relate to such losses. In particular, we demonstrate how ad platforms can utilize ML advice to improve welfare guarantee on the aggregate and individual bidder level by setting ML advice as personalized reserve prices when the platform consists of autobidders who maximize value while respecting a return-on-ad spent (ROAS) constraint. Under parallel VCG auctions with such ML advice-based reserves, we present a worst-case welfare lower-bound guarantee for an individual autobidder, and show that the lower-bound guarantee is positively correlated with ML advice quality as well the scale of bids induced by the autobidder's bidding strategies. Further, we prove an impossibility result showing that no truthful, and possibly randomized mechanism with anonymous allocations can achieve universally better individual welfare guarantees than VCG, in presence of personalized reserves based on ML-advice of equal quality. Moreover, we extend our individual welfare guarantee results to generalized first price (GFP) and generalized second price (GSP) auctions.
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