Auto-bidding and Auctions in Online Advertising: A Survey
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andres Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, Song Zuo
TL;DR
This survey analyzes autobidding in online advertising, focusing on how automated bids interact with auction design, budgets, and RoS constraints. It develops a unified framework for bidder and auctioneer problems, derives optimal bidding rules via LP duality, and surveys online learning algorithms under truthful and non-truthful auctions. The paper compiles results on equilibria, PoA across basic and enhanced formats, and revenue-optimal auction design under various information structures, including ML-advice and interdependent settings. It also discusses multi-channel and empirical aspects, highlighting practical impacts on welfare, revenue, and platform strategies. Overall, the work offers a cohesive view of theory and empirical findings for designing efficient autobidding-enabled markets.
Abstract
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opportunities that have arisen as markets embrace this autobidding and cover a range of topics in this area, including bidding algorithms, equilibrium analysis and efficiency of common auction formats, and optimal auction design.