Title: Tests and Metrics for Static and Dynamic Incentive Compatibility
Abstract: An incentive-compatible auction incentivizes buyers to truthfully reveal their private valuations. However, many ad auction mechanisms deployed in practice are not incentive-compatible, such as first-price auctions (for display advertising) and the generalized second-price auction (for search advertising). Furthermore, even if each auction is truthful in isolation, common practices like dynamic reserve pricing can induce buyers to shade bids across repeated auctions. On the buy-side, we provide novel statistical tests that allow buyers to confirm, using simple bid perturbations, whether the auction they are bidding into is (dynamic) incentive-compatible. On the sell-side, we introduce a new metric to quantify incentive compatibility in both static and dynamic environments. We provide interpretable characterizations of our metric and prove that it is monotone in auction parameters for several mechanisms used in practice, such as soft floors and dynamic reserve prices. We illustrate these ideas on real ad auction data and show how they can be used to quantify the revenue-incentive tradeoffs of different reserve pricing schemes. This is joint work with Yuan Deng, Song Zuo, Vahab Mirrokni.
Bio: Sébastien Lahaie is a research scientist in the Market Algorithms at Google Research in New York City. He received his PhD in Computer Science from Harvard in 2007 and was previously a research scientist at Yahoo and Microsoft. His research focuses on computational aspects of market design, with applications to sponsored search and display advertising. He is interested in designing market algorithms that scale well and properly anticipate user behavior. Other interests include preference modeling and elicitation, combinatorial auctions, and prediction markets. He has served as a co-editor for Economic Inquiry and was previously a program chair for the conference on Auctions, Market Mechanisms, and their Applications (AMMA).
