**报告题目**：Strategic Budget Selection in a Competitive Autobidding World

**讲座时间**：2023年8月30日（周三）10:00-11:30

**讲座地点**：立德楼1826报告厅

**报告摘要**

We study a game played between advertisers in an online ad platform. The platform sells ad impressions by first-price auction and provides autobidding algorithms that optimize bids on each advertiser's behalf. Each advertiser strategically declares a budget constraint (and possibly a maximum bid) to their autobidder. The chosen constraints define an "inner" budget-pacing game for the autobidders, who compete to maximize the total value received subject to the constraints. Advertiser payoffs in the constraint-choosing "metagame" are determined by the equilibrium reached by the autobidders.

Advertisers only specify budgets and linear values to their autobidders, but their true preferences can be more general: we assume only that they have weakly decreasing marginal value for clicks and weakly increasing marginal disutility for spending money. Our main result is that despite this gap between general preferences and simple autobidder constraints, the allocations at equilibrium are approximately efficient. Specifically, at any pure Nash equilibrium of the metagame, the resulting allocation obtains at least half of the liquid welfare of any allocation and this bound is tight. We also obtain a 4-approximation for any mixed Nash equilibrium, and this result extends also to Bayes-Nash equilibria. These results rely on the power to declare budgets: if advertisers can specify only a (linear) value per click but not a budget constraint, the approximation factor at equilibrium can be as bad as linear in the number of advertisers.

**主讲嘉宾**

冯逸丁 University of Chicago Postdoctoral Principal Researcher

Yiding Feng is currently a postdoctoral principal researcher at the University of Chicago Booth School of Business, working with Rad Niazadeh and Vahideh Manshadi. He worked as a postdoctoral researcher at MicrosoftResearch New England from 2021 to 2023. He previously received his PhD fromDepartment of Computer Science, Northwestern University in 2021where his advisor was Jason D. Hartline. Before that, he received hisBS degree from ACM Honors Class at Shanghai Jiao Tong UniversityHis current research focuses on examining commonly usedalgorithms/mechanisms and developing new ones in various onlinemarketplaces (e.g., advertising, ride hailing, cloud computing,vacation rental, video recommendation), taking into account potentiaconcerns or features from uncertainties (due to the stochastic natureor real-time aspect of the application), incentives (due to the strategicbehavior of users), competition (across platforms), or presence ofhighly detailed user data. As an interdisciplinary researcher, heapproach these marketplaces with a fresh perspective as well asnovel ideas from operations, economics, and computer science.

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