research-article

Towards Efficient Auctions in an Auto-bidding World

Authors:

Vahab Mirrokni,

Song ZuoAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 3965 - 3973

https://doi.org/10.1145/3442381.3450052

Published: 03 June 2021 Publication History

Abstract

Auto-bidding has become one of the main options for bidding in online advertisements, in which advertisers only need to specify high-level objectives and leave the complex task of bidding to auto-bidders. In this paper, we propose a family of auctions with boosts to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints. Our empirical results validate our theoretical findings and show that both the welfare and revenue can be improved by selecting the weight of the boosts properly.

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Cited By

Balseiro SBhawalkar KFeng ZLu HMirrokni VSivan BWang DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)A field guide for pacing budget and ROS constraintsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692175(2607-2638)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692175
Tang XLv HGao YWu FLiu LCui LDastani MSichman JAlechina NDignum V(2024)Towards Efficient Auction Design with ROI ConstraintsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663044(1818-1826)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663044
Tewolde EConitzer VLarson K(2024)Game transformations that preserve Nash equilibria or best-response setsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/331(2984-2993)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/331
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Towards Efficient Auctions in an Auto-bidding World
1. Applied computing
  1. Law, social and behavioral sciences

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Published In

cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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Cited By

Balseiro SBhawalkar KFeng ZLu HMirrokni VSivan BWang DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)A field guide for pacing budget and ROS constraintsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692175(2607-2638)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692175
Tang XLv HGao YWu FLiu LCui LDastani MSichman JAlechina NDignum V(2024)Towards Efficient Auction Design with ROI ConstraintsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663044(1818-1826)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663044
Tewolde EConitzer VLarson K(2024)Game transformations that preserve Nash equilibria or best-response setsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/331(2984-2993)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/331
Liu XFan ZDing YGuo YZhang LLi CKong DLi HShen WLarson K(2024)Optimal auction design with user coupons in advertising systemsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/322(2904-2912)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/322
Li JTang PLarson K(2024)Vulnerabilities of single-round incentive compatibility in auto-biddingProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/320(2886-2894)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/320
Bergemann DBonatti A(2024)Data, Competition, and Digital PlatformsAmerican Economic Review10.1257/aer.20230478114:8(2553-2595)Online publication date: 1-Aug-2024
https://doi.org/10.1257/aer.20230478
Paes Leme RPiliouras GSchneider JSpendlove KZuo SKleinberg RSaban DBergemann D(2024)Complex Dynamics in Autobidding SystemsProceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673551(75-100)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3670865.3673551
Guo JHuo YZhang ZWang TYu CXu JZheng BZhang YBaeza-Yates RBonchi F(2024)Generative Auto-bidding via Conditional Diffusion ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671526(5038-5049)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671526
Feng YLucier BSlivkins AMohar BShinkar IO'Donnell R(2024)Strategic Budget Selection in a Competitive Autobidding WorldProceedings of the 56th Annual ACM Symposium on Theory of Computing10.1145/3618260.3649688(213-224)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3618260.3649688
Deng YGolrezaei NJaillet PLiang JMirrokni VChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Individual Welfare Guarantees in the Autobidding World with Machine-learned AdviceProceedings of the ACM Web Conference 202410.1145/3589334.3645660(267-275)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645660
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