Department of Computer ScienceCo-Sponsor(s)
Fuqua School of Business; Duke Computer Science
Lunch will be served at 11:45 AM.
We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, as a simultaneous game. A key component here is the negative externality buyers induce on one another due to data purchases. Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality. While the outcomes are bleak without any intervention, mirroring the limitations of current data markets, we prove that for a standard class of externality functions, platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees. We next consider a more realistic setting where buyers learn their valuations over time through market interactions. Our intervention is feasible here as well, and we consider learning algorithms to achieve low regret concerning both individual and cumulative utility metrics. Lastly, we analyze the promises of this intervention under a much richer externality model.
This is based on joint work with Safwan Hossain. Link to the paper: https://arxiv.org/pdf/2302.08012 .
Yiling Chen is a Gordon McKay Professor of Computer Science at Harvard University. She received her Ph.D. in Information Sciences and Technology from the Pennsylvania State University. Prior to working at Harvard, she spent two years at Yahoo! Research in New York City. Her research lies in the intersection of computer science, economics, and other social sciences, with a focus on social aspects of computational systems. She was a recipient of The Penn State Alumni Association Early Career Award and was selected by IEEE Intelligent Systems as one of "AI's 10 to Watch” early in her career. Her work received best paper awards at ACM EC, AAMAS, ACM FAT* (now ACM FAccT) and ACM CSCW conferences. She was a program co-chair for the 2013 Conference on Web and Internet Economics (WINE’13), the 2016 ACM Conference on Economics and Computation (EC’16), the 2018 AAAI Conference on Human Computation and Crowdsourcing (HCOMP’18) and the 2023 AAAI Conference on Artificial Intelligence (AAAI-23) and has served as an associate editor for several journals.
Fuqua School of Business; Duke Computer Science