  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.

Machine Learning;Computer Science and Game Theory;General Economics;Economics