Towards Efficient Auctions in an Auto-bidding World
Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo
2021年份
87被引次数
37顶会引用
摘要
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. CCS Concepts: • Theory of computation → Algorithmic game theory and mechanism design.
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引用它的顶会 Paper37
- Auctions between Regret-Minimizing AgentsYoav Kolumbus, Noam NisanWWW 2022 · 被引用 47 次
- Auction Design in an Auto-bidding Setting: Randomization Improves Efficiency Beyond VCGAranyak MehtaWWW 2022 · 被引用 41 次
- Online Bidding Algorithms for Return-on-Spend Constrained Advertisers✱Zhe Feng, Swati Padmanabhan, Di WangWWW 2023 · 被引用 38 次
- Multi-channel Autobidding with Budget and ROI ConstraintsYuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang 等ICML 2023 · 被引用 34 次
- How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning AgentsYoav Kolumbus, Noam NisanNeurIPS 2022 · 被引用 28 次
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