Date: Wednesday, April 12th, 2023
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
Title: Individually-Fair Auctions for Multi-Slot Sponsored Search
We design fair sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan and Chawla et al., who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click through rates, our auctions achieve the same near-optimal tradeoff between fairness and quality as in this work. When similar users can have different advertiser specific preferences, we show that a preference-based fairness guarantee holds. Finally, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work, resolving another open direction from Chawla and Jagadeesan.
Rojin Rezvan is a fourth-year PhD student at the University of Texas at Austin, advised by Shuchi Chawla. She received her masters degree from the University of Wisconsin-Madison. She is broadly interested in algorithmic game theory and mechanism design. More specifically, She has done research in multi-dimensional mechanism design in the paradigm of simple vs. optimal, fairness in auctions and fair allocation. She is generally interested in the intersection of mechanism design and other fields such as fairness and decentralized systems.