Title: Design Future Mobility Systems: Information, Markets, and Algorithms
Abstract: Today, emerging technologies and AI-powered platforms are transforming the operations of mobility systems by providing real-time information, personalized recommendations, and services to countless users. How to model the strategic interactions between humans and technologies under the physical infrastructure constraints, and how to integrate these technologies into our mobility systems? In this talk, I will present an approach to tackle this question. First, I present my works on designing information and incentive mechanisms to improve resource utilization in urban infrastructure. I will focus on the study of how ride-hailing platforms can leverage information about uncertain demand to influence drivers’ repositioning decisions and improve their operations. We establish that in many practically relevant settings a simple monotone partitional information mechanism is optimal. We also provide algorithmic approaches for obtaining (near-)optimal information structures in general settings. Second, I present my works on designing scalable algorithms and markets for future mobility systems. I will focus on an algorithmic design of a dynamic market mechanism that enables efficient resource sharing in transportation network. I characterize sufficient conditions under which the mechanism is socially optimal and satisfies incentive constraints for riders with heterogeneous preference, and provide extensions beyond the tractable case.
Bio: Manxi Wu is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering. Her area of research includes information and market design, and multi-agent learning in societal networks. Manxi earned her Ph.D. from MIT IDSS in 2021. She was a research fellow at the Simons Program on Learning and Games, and a postdoctoral scholar at EECS, University of California, Berkeley from 2021-2022.
