The Impact of Automation on Workers When Workers Are Strategic: The Case of Ride-Hailing

Problem definition: Motivated by the behavior of drivers on ride-hailing platforms (individual drivers decide whether to work based on the offered wage and where to locate themselves in anticipation of future fares), we examine how the introduction of autonomous vehicles impacts the strategic behavior of human drivers and driver welfare. Specifically, we consider a setting in which a ride-hailing platform deploys a mixed fleet of conventional vehicles (CVs) and autonomous vehicles (AVs). 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 the AVs are positioned spatially when they are idle. The platform can also make decisions on whether to prioritize the AVs or the CVs in assigning vehicles to customer requests. Methodology/results: 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 strategic drivers and the ride-hailing platform. We show that, although the introduction of AVs can displace drivers and depress effective wages, there are settings in which the introduction of AVs leads to higher effective wages and more drivers being hired. We discuss how these results can, in part, be explained by the interplay of two counteracting effects: (i) the introduction of AVs provides the platform with an additional source of supply and renders human driver substitutable (displacement effect), and (ii) having access to and control over AVs enables the platform to influence the strategic behavior of CVs, thereby reducing the inefficiency from self-interested behavior (incentive effect). The relative strength of these two effects depends on the cost of AVs and the vehicle dispatching policy. Managerial implications: Our results uncover a new effect through which the introduction of AVs affects the welfare of human drivers (the incentive effect) and another mechanism to mitigate inefficiencies because of human drivers acting strategically. Our results have potentially broader applications to other areas in which automation is introduced and workers are strategic. Funding: This work was supported by the National Science Foundation [Grant SCC-1831140]. The Guangdong (China) Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [2023B1212010001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0416 .