Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

Negin Golrezaei (), Adel Javanmard () and Vahab Mirrokni ()
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Negin Golrezaei: Operations Management Group, MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Adel Javanmard: Data Sciences and Operations Department, USCMarshall School of Business, University of Southern California, Los Angeles, California 90089
Vahab Mirrokni: Google Research, New York, New York 10011

Operations Research, 2021, vol. 69, issue 1, 297-314

Abstract: 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 .

Keywords: pricing: robust learning; strategic buyers; repeated second-price auctions; online advertising (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:69:y:2021:i:1:p:297-314

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