University of Michigan – Ann Arbor
Xintong Wang is a Ph.D. candidate in the Computer Science Department at the University of Michigan, advised by Prof. Michael Wellman. Her research lies in the intersection of computer science and economics. She is particularly interested in market mechanism design and the modeling/understanding of strategic interactions among agents. Prior to Michigan, she received her Bachelor’s degree from Washington University in St. Louis in 2015. She was a research intern at Microsoft Research NYC in summer 2018 and J.P. Morgan AI Research in summer 2019.
Financial markets are places where people gather to trade assets and their derivatives. The design of mechanism or auction rules underlying markets plays a key role that can influence the decision-making of agents and the settlement of prices. With the prevalence of electronic marketplaces and the automation of trading, autonomous agents are developed to make decisions and execute actions at an unprecedented speed, complexity, and scale, bringing new phenomena and challenges to traditional market designs.
My research aims to design robust and intelligent markets that consider the strategic responses of agents to facilitate the efficient aggregation and fair allocation of economic resources, by applying techniques from game theory, multi-agent system, machine learning, and optimization. I build computational models that capture key aspects of dynamic markets to understand the strategic inter-
actions among agents, perform game-theoretic analysis to evaluate market outcomes in equilibrium, and propose mechanisms to align agent incentives and maximize market surplus.