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.

在线广告拍卖的学习模型揭示了竞价人行为对关键绩效指标的影响，包括广告槽位置、竞争广告商数量和身份、广告商接收的反馈以及支付规则等特征。同时证明了在更现实的拍卖环境中，即使竞标人来自同一人群，提供 “软底价” 也能提升关键绩效指标。