Speaker: Dr. Xintong Wang, Rutgers University

Time: 16:00 p.m., November 29, 2023, GMT+8

Venue: Room 204, Courtyard No.5, Jingyuan, PKU

Abstract: 

Today's markets have become increasingly algorithmic, with participants using algorithms to interact with each other at an unprecedented complexity, speed, and scale. Prominent examples include dynamic pricing, recommender systems, advertising technology, and high-frequency trading. These algorithmic behaviors pose challenges in designing market-based systems that can align individual behavior with broader, system-wide objectives.
This talk will highlight our work that tackles these challenges using tools from AI, 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 behavior will change in the face of new designs. I will discuss two settings: (1) understanding and deterring manipulation by algorithmic traders in financial markets, and (2) informing regulatory interventions that can incentivize platforms such as Uber Eats to promote efficiency, merchant diversity, and resilience.
I will conclude by discussing future directions in using AI for the modeling and design of multi-agent systems, including model calibration, interpretability, scalability, and behavioral vs. rational assumptions.

Source: Center on Frontiers of Computing Studies, PKU