Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice
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3589334.3645660.pdf
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1.08 MB
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Adobe PDF
Checksum (MD5)
f880010a1d10043c6405282b9ffde2e5
Author(s)
Deng, Yuan
Golrezaei, Negin
Jaillet, Patrick
Liang, Jason Cheuk Nam
Mirrokni, Vahab
Date Issued
May 13, 2024
Publisher
ACM
Citation
Deng, Yuan, Golrezaei, Negin, Jaillet, Patrick, Liang, Jason Cheuk Nam and Mirrokni, Vahab. 2024. "Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice."
Version
Final published version
Abstract
nline advertising channels commonly focus on maximizing total advertiser welfare to enhance channel health, and previous literature has studied augmenting ad auctions with machine learning predictions on advertiser values (also known asmachine-learned advice ) to improve total welfare. Yet, such improvements could come at the cost of individual bidders' welfare and do not shed light on how particular advertiser bidding strategies impact welfare. Motivated by this, we present an analysis on an individual bidder's welfare loss in the autobidding world for auctions with and without machine-learned advice, and also uncover how advertiser strategies relate to such losses. In particular, we demonstrate how ad platforms can utilize ML advice to improve welfare guarantee on the aggregate and individual bidder level by setting ML advice as personalized reserve prices when the platform consists ofautobidders who maximize value while respecting a return on ad spend (ROAS) constraint. Under parallel VCG auctions with such ML advice-based reserves, we present a worst-case welfare lower-bound guarantee for an individual autobidder, and show that the lower-bound guarantee is positively correlated with ML advice quality as well as the scale of bids induced by the autobidder's bidding strategies. Further, we show that no truthful, and possibly randomized mechanism with anonymous allocations can achieve universally better individual welfare guarantees than VCG, in the presence of personalized reserves based on ML-advice of equal quality. Moreover, we extend our individual welfare guarantee results to generalized first price (GFP) and generalized second price (GSP) auctions. Finally, we present numerical studies using semi-synthetic data derived from ad auction logs of a search ad platform to showcase improvements in individual welfare when setting personalized reserve prices with ML-advice.
Description
WWW '24: Proceedings of the ACM on Web Conference May 13–17, 2024, Singapore, Singapore
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3589334.3645660
