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讲座:The Impact of Automation on Workers when Workers are Strategic: The Case of Ride-Hailing 2022-11-01

题 目:The Impact of Automation on Workers when Workers are Strategic: The Case of Ride-Hailing

嘉 宾:Zicheng Wang, Ph.D., University of Minnesota

主持人:花成 助理教授 上海交通大学安泰经济与管理学院

时 间:2022年11月3日(周四)21:00-22:30pm

地 点:腾讯会议(校内师生如需获取会议号和密码,请于11月3日下午17点前发送电邮至


We consider a setting in which a ride-hailing platform operates a mixed fleet of conventional vehicles (CVs) and autonomous vehicles (AVs) over locations distributed spatially.  The CVs are operated by human drivers who make independent decisions about whether to work for the platform and where to position themselves when they become idle. The AVs are under the control of the platform. The platform decides on the wage it pays the drivers, the size of the AV fleet and how AVs are positioned spatially when they are idle. The platform can also make decisions on whether to prioritize AVs or CVs in assigning vehicles to customer requests. We use a fluid model to characterize the optimal decisions of the platform and contrast those with the optimal decisions in the absence of AVs. We examine the impact of automation on the ride-hailing platform and strategic drivers. Among our findings, we show that it is optimal for the platform to prioritize AVs in assigning vehicles to customer requests. The platform, whenever possible, would deploy the AVs in such a way as to increase repositioning by CVs from the low-demand location to the high-demand location. Even though demand is no longer exclusively fulfilled by CVs, we show that the introduction of AVs may not always be harmful to drivers, and driver welfare may in fact improve. Our results uncover important ways the introduction of AVs affects the operation of a ride-hailing platform and highlight the nuanced impact of AVs on human drivers. Our results are potentially useful to  policy makers in deciding on regulatory interventions that can induce more socially desirable outcomes with the introduction of AVs.


Zicheng Wang is a Post-Doctoral Associate at the Department of Laboratory Medicine and Pathology at the University of Minnesota, supervised by Doctor Ruping Sun. Before joining SunPath Lab, Zicheng received a Ph.D. from the University of Minnesota - Twin Cities, advised by Doctor Kevin Leder. His research interests lie in the interface between theories in stochastic processes and cancer data science (and operations management). His research aims to analyze the evolution of stochastic systems using both a theoretical and a data science approach and solving related optimization problems.