Robust Repeated Auctions under Heterogeneous Buyer Behavior
Name
1803.00494.pdf
Description
Accepted version
Size
307.8 KB
Format
Adobe PDF
Checksum (MD5)
fc1a8f473d811dd2dc31f5d45c92a4e2
Author(s)
Agrawal, Shipra
Daskalakis, Constantinos
Mirrokni, Vahab S.
Sivan, Balasubramanian
Date Issued
June 2018
Publisher
Association for Computing Machinery (ACM)
Citation
Agrawal, Shipra, Daskalakis, Constantinos, Mirrokni, Vahab S. and Sivan, Balasubramanian. 2018. "Robust Repeated Auctions under Heterogeneous Buyer Behavior."
Version
Author's final manuscript
Abstract
We study revenue optimization in a repeated auction between a single seller and a single buyer. Traditionally, the design of repeated auctions requires strong modeling assumptions about the bidder behavior, such as it being myopic, infinite lookahead, or some specific form of learning behavior. Is it possible to design mechanisms which are simultaneously optimal against a multitude of possible buyer behaviors? We answer this question by designing a simple state-based mechanism that is simultaneously approximately optimal against a k-lookahead buyer for all k, a buyer who is a no-regret learner, and a buyer who is a policy-regret learner. Against each type of buyer our mechanism attains a constant fraction of the optimal revenue attainable against that type of buyer. We complement our positive results with almost tight impossibility results, showing that the revenue approximation tradeoffs achieved by our mechanism for different look ahead attitudes are near-optimal.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/3219166.3219234
