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.

翻译：在线广告渠道通常专注于最大化广告主总价值（或福利），以提升长期留存和渠道健康度。先前文献通过多种形式整合关于广告主价值的机器学习预测（亦称机器学习建议）来设计拍卖机制，从而改善总福利。然而，这种改善可能以个体竞价者的福利为代价，且未阐明特定广告主竞价策略如何影响福利。受此启发，我们对有/无机器学习建议的自动出价拍卖中个体竞价者的福利损失进行了分析，并揭示了广告主策略与这种损失之间的关系。具体而言，我们展示了广告平台如何通过将机器学习建议设置为个性化保留价，在由最大化价值且遵守广告支出回报约束的自动出价者组成的平台中，利用机器学习建议改善总体及个体竞价者层面的福利保障。在采用此类基于机器学习建议的保留价的并行VCG拍卖中，我们给出了单个自动出价者的最坏情况福利下限保障，并表明该下限保障与机器学习建议质量以及自动出价者策略所引致的出价规模正相关。此外，我们证明了一个不可能性结果：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，没有诚实的（且可能是随机的）匿名分配机制能够实现比VCG更优的普遍个体福利保障。最后，我们将个体福利保障结果推广至广义第一价格和广义第二价格拍卖。