"Pacing Mechanisms For Ad Auctions"
Bio
Nicolas is a Director at Meta and supports the Central Applied Science team. His work and that of the team leverage innovative research to drive impact to the company's products, such as Facebook and Instagram, as well as to improve Meta's infrastructure and processes. The team draws from a rich and diverse set of disciplines including Operations, Algorithms, Machine Learning, Statistics, Economics, Mechanism Design, Experimentation, and Privacy Preserving Machine Learning. Prior to joining Meta, Nicolas was an Associate Professor at the Decision, Risk and Operations Division of Columbia Business School and at the Business School of Universidad Torcuato Di Tella. He received a Ph.D. degree from the Operations Research Center at the Massachusetts Institute of Technology.
Presentation Abstract
Budgets play a significant role in real-world sequential auction markets such as those implemented by Internet companies. To maximize the value provided to auction participants, spending is smoothed across auctions so budgets are used for the best opportunities. Motivated by pacing mechanisms used in practice by online ad auction platforms, we discuss smoothing procedures that ensure that campaign daily budgets are consistent with maximum bids. Reinterpreting this process as a game between bidders, we introduce the notion of pacing equilibrium, and study properties such as existence, uniqueness, complexity and efficiency. Connecting these results to more general notions of market equilibria, we use the models to address experimentation in two sided markets. We propose a parallel budget-controlled A/B testing design where we use market segmentation to identify submarkets in the larger market, and we run parallel budget-split experiments in each submarket.