Towards Efficient Auctions in an Auto-bidding World
Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo
TL;DR
This paper introduces a new family of auction mechanisms with boosts designed to enhance welfare and revenue in online advertising auto-bidding environments, validated through empirical and theoretical analysis.
Contribution
It proposes a novel auction family with boost features tailored for auto-bidding, addressing welfare and revenue optimization under constraints.
Findings
Boosted auctions improve welfare and revenue
Proper weight selection of boosts is crucial
Empirical results validate theoretical predictions
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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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing · Digital Platforms and Economics