February 14, 2020, Webb 1100

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 discus 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, both for the case of second and first price auctions. In addition, we connect these equilibria to more general notions of market equilibria, and study how compact representations of a market lead to more efficient approaches to compute approximate equilibria.

Nicolas Stier is a Co-Director of Facebook Core Data Science. His work leverages innovative research to drive impact to the products, infrastructure and processes at Facebook, the company. The group draws inspiration from a rich and diverse set of disciplines including Operations, Statistics, Economics, Mechanism Design, Machine Learning, Experimentation, Algorithms, and Computational Social Science (in no particular order). Between 2014 and 2017, he supported the Economics, Algorithms and Optimization team, which is one the areas of focus of Core Data Science. Prior to coming to Facebook, 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.