期刊
OPERATIONS RESEARCH
卷 69, 期 1, 页码 297-314出版社
INFORMS
DOI: 10.1287/opre.2020.1991
关键词
pricing; robust learning; strategic buyers; repeated second-price auctions; online advertising
资金
- Junior Faculty Research Assistance Program at the Massachusetts Institute of Technology
- Google Faculty Research Award
- Outlier Research in Business (iORB) grant from the USC Marshall School of Business
- National Science Foundation [DMS-1844481]
- Office of the Provost at the University of Southern California through the Zumberge Fund Individual Grant Program
The article discusses the problem of learning reserve prices against strategic buyers in ad exchange markets, proposing various learning policies to address this issue. These policies estimate buyers' preferences by observing auction outcomes and control the impact of auction results on future reserve prices.
Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers' valuations for an item depend on the context that describes the item. However, the seller is not aware of the relationship between the context and buyers' valuations (i.e., buyers' preferences). The seller's goal is to design a learning policy to set reserve prices via observing the past sales data, and her objective is to minimize her regret for revenue, where the regret is computed against a clairvoyant policy that knows buyers' heterogeneous preferences. Given the seller's goal, utility-maximizing buyers have the incentive to bid untruthfully in order to manipulate the seller's learning policy. We propose learning policies that are robust to such strategic behavior. These policies use the outcomes of the auctions, rather than the submitted bids, to estimate the preferences while controlling the long-term effect of the outcome of each auction on the future reserve prices. When the market noise distribution is known to the seller, we propose a policy called contextual robust pricing that achieves a T-period regret of O(d log(T d) log(T)), where d is the dimension of the contextual information. When the market noise distribution is unknown to the seller, we propose two policies whose regrets are sublinear in T.
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