标题： 推进广告拍卖的真实性：实用见解与建模影响 显示英文标题

标题： Advancing Ad Auction Realism: Practical Insights & Modeling Implications

摘要： 现代现实世界的在线广告拍卖与经典的模型[Edelman等人，2007；Varian，2009]至少有四个方面不同：(1) 价值和点击率可能取决于用户的搜索查询，但广告商只能部分地“调整”他们的出价以适应特定的查询；(2) 广告商不知道竞争者的数量、身份和精确的价值分布；(3) 广告商只收到部分的、聚合的反馈；(4) 支付规则仅部分为竞标者所知。这些特性使得完全描述均衡出价行为几乎不可能。本文表明，尽管如此，仍然可以通过将广告商建模为由对抗性多臂老虎机算法支配的代理来获得对现代广告拍卖的有用见解，而无需考虑拍卖机制的复杂性。为了展示我们的方法，我们首先模拟“软底价”拍卖[Zeithammer，2019]，这是一种复杂的现实世界定价规则，目前尚无完整的均衡描述。我们发现，(i) 当价值和点击率依赖于查询时，即使投标者类型是从同一分布中抽取的，软底价相对于标准拍卖格式可以提高收入；并且(ii) 在反映相关现实场景的分布不对称情况下，我们发现软底价的收入低于适当选择的保留价格，即使仅关注一个查询。然后，我们演示如何从观察到的出价中推断广告商的价值分布，并用来自电子商务网站的聚合数据来说明我们的方法。 显示英文摘要

摘要： Contemporary real-world online ad auctions differ from canonical models [Edelman et al., 2007; Varian, 2009] in at least four ways: (1) values and click-through rates can depend upon users' search queries, but advertisers can only partially "tune" their bids to specific queries; (2) advertisers do not know the number, identity, and precise value distribution of competing bidders; (3) advertisers only receive partial, aggregated feedback, and (4) payment rules are only partially known to bidders. These features make it virtually impossible to fully characterize equilibrium bidding behavior. This paper shows that, nevertheless, one can still gain useful insight into modern ad auctions by modeling advertisers as agents governed by an adversarial bandit algorithm, independent of auction mechanism intricacies. To demonstrate our approach, we first simulate "soft-floor" auctions [Zeithammer, 2019], a complex, real-world pricing rule for which no complete equilibrium characterization is known. We find that (i) when values and click-through rates are query-dependent, soft floors can improve revenues relative to standard auction formats even if bidder types are drawn from the same distribution; and (ii) with distributional asymmetries that reflect relevant real-world scenario, we find that soft floors yield lower revenues than suitably chosen reserve prices, even restricting attention to a single query. We then demonstrate how to infer advertiser value distributions from observed bids for a variety of pricing rules, and illustrate our approach with aggregate data from an e-commerce website.