Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Name
2002.11137.pdf
Description
Accepted version
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1021.65 KB
Format
Adobe PDF
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ffafae0500ab5851e460bb8d8dafa98f
Author(s)
Golrezaei, Negin
Javanmard, Adel
Mirrokni, Vahab
Date Issued
2021
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Version
Author's final manuscript
Abstract
© 2020 INFORMS 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.
MIT Department
Sloan School of Management
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/OPRE.2020.1991
