Computer Science Department Colloquium
AI for Market and Policy Design: Integrating Data, Algorithms, and Economic Modeling
Tuesday, March 21, 2023, 10:30am - 11:30am
Speaker: Xintong Wang
Xintong Wang is a postdoctoral fellow at Harvard University, School of Engineering and Applied Sciences, working with David Parkes. Her research interests lie in understanding agent incentives and behaviors for the efficient design of multi-agent systems, using tools from AI and economics.
Xintong received her Ph.D. in Computer Science from the University of Michigan, advised by Michael Wellman. She has worked as a research intern at Microsoft Research and J.P. Morgan AI Research. She was selected as a Rising Star in EECS by UIUC in 2019 and a Rising Star in Data Science by the University of Chicago in 2022. Previously, Xintong received her B.S. with honors from Washington University in St. Louis in 2015.
Location : Core 301
Event Type: Computer Science Department Colloquium
Abstract: Today's markets have become increasingly algorithmic, with participants (i.e., agents) using algorithms to interact with each other at an unprecedented complexity, speed, and scale. Prominent examples of such algorithms include dynamic pricing algorithms, recommender systems, advertising technology, and high-frequency trading. Despite their effectiveness in achieving individual goals, the algorithmic nature poses challenges in designing economic systems that can align individual behavior to social objectives.This talk will highlight our work that tackles these challenges using tools from AI and economics, towards a vision of constructing efficient and healthy market-based, multi-agent systems. I will describe how we combine machine learning with economic modeling to understand strategic behaviors observed in real-world markets, analyze incentives behind such behaviors under game-theoretic considerations, and reason about how agents will behave differently in the face of new designs or environments. I will discuss the use of our method in two settings: (1) understanding and deterring manipulation practices in financial markets and (2) informing regulatory interventions that can incentivize a platform (e.g., Uber Eats) to act, in the forms of fee-setting and matching, to promote the efficiency, user diversity, and resilience of the overall economy. I will conclude by discussing future directions (e.g., model calibration, interpretability, scalability, and behavioral vs. rational assumptions) in using AI for the modeling and design of multi-agent systems.
Contact David Pennock