This paper proposes a learning model of online ad auctions that allows for the following four key realistic characteristics of contemporary online auctions: (1) ad slots can have different values and click-through rates depending on users' search queries, (2) the number and identity of competing advertisers are unobserved and change with each auction, (3) advertisers only receive partial, aggregated feedback, and (4) payment rules are only partially specified. We model advertisers as agents governed by an adversarial bandit algorithm, independent of auction mechanism intricacies. Our objective is to simulate the behavior of advertisers for counterfactual analysis, prediction, and inference purposes. Our findings reveal that, in such richer environments, "soft floors" can enhance key performance metrics even when bidders are drawn from the same population. We further demonstrate how to infer advertiser value distributions from observed bids, thereby affirming the practical efficacy of our approach even in a more realistic auction setting.

翻译：本文提出一种在线广告拍卖的学习模型，该模型允许当代在线拍卖具备以下四个关键现实特征：（1）广告位可根据用户搜索查询具有不同价值和点击率；（2）竞争广告主的数量和身份不可观测且随每次拍卖而变化；（3）广告主仅接收部分聚合反馈；（4）支付规则仅部分明确。我们将广告主建模为受对抗性赌博机算法支配的智能体，独立于拍卖机制细节。我们的目标是模拟广告主行为以进行反事实分析、预测与推断。研究结果表明，在此类更丰富的环境中，即使竞标者来自同一群体，“软底价”也能提升关键性能指标。我们进一步证明了如何从观测到的出价中推断广告主价值分布，从而验证了本方法在更现实拍卖场景中的实践有效性。