Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 4, 2022 erstellt
Description:
Problem definition: After autonomous vehicles (AVs) are deployed for ride-hailing platforms but before their costs decrease enough to push human drivers off the road entirely, human drivers will compete for rides with AVs. We consider a ride-hailing platform’s strategy to recruit human drivers while also operating a private fleet of AVs. Methodology/results: We formulate and solve a game-theoretic model of a ride-hailing platform with a private AV fleet that also recruits self-interested human drivers. The platform sets the human-driver wage and the AV deployment quantity, and human drivers make strategic joining decisions based on a rational anticipation of their expected earnings. We show that growing its AV fleet too quickly while the AV cost is still relatively high can be a costly mistake for the platform. Doing so triggers a feedback loop of increasing wages and increasing AV deployment, such that the platform prices itself out of the market for human drivers to the detriment of its own profits. Managerial implications: This "race to the top" effectively prevents the platform from attracting more than a limited number of human drivers, and it increases the cost of attracting a given number. Nonetheless, we prove that the platform can break the feedback loop by optimally tuning its AV fleet size, tempering the competition for rides and achieving a profitable balance of AVs and human drivers. Our findings suggest that even while their costs remain high, AVs can be a valuable tool for ride-hailing platforms, as long as the fleet size is carefully set