Abstract

Ridesharing platforms match riders and drivers, using dynamic prices to balance supply and demand. The origin-based "surge pricing", however, does not depend on the market condition of trip destinations, leading to inefficient trip flows in space and incentivizes drivers to strategize. In this work, we introduce the Iterative Network Pricing mechanism, addressing a main challenge in the practical implementation of optimal origin-destination (OD) based prices, that the model for rider-demand is hard to estimate. Assuming that the platform's surge algorithm clears the market for each origin in real-time, our mechanism updates the destination-based price adjustments week-over-week, using only information immediately observable during the same time window in the prior week. We prove that our mechanism converges to an outcome that is approximately welfare-optimal and incentive-aligned. Using data made public by the City of Chicago, we illustrate (via simulation) the proper destination-based price adjustments in space, and the resulting substantial improvements in social welfare, trip throughput, and incentive alignment.

Biography

Hongyao Ma is an Assistant Professor of Business in the Decision, Risk, and Operations division at Columbia Business School. Her research is situated at the interface of computer science, economics and operations, with a particular focus on market design. Hongyao completed her Ph.D. in Computer Science at Harvard University in 2019, and worked as a postdoctoral researcher at Uber and then Caltech during 2019-2020. She obtained her M.S. in 2014 at Harvard, and B.E. in 2012 at Xi'an Jiaotong University, both in Electrical Engineering. She received the ACM SIGecom Doctoral Dissertation Award in 2020, a Siebel Scholarship 2017-2018, and a Certificate of Distinction in Teaching at Harvard in 2014.

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