The Value of Knowing Drivers' Reservation Wage in Ride Sharing Systems

Consider a ride sharing platform, and a large population of strategic potential drivers, heterogeneous in terms of their reservation wages (i.e., income goals). Drivers choose whether or not to work for the platform. The platform is assumed knowledgeable of the different drivers' reservation wages. How can the platform implement a matching policy that leverages this information in order to improve system efficiency? Can such improvement be quantified? In this work we adopt a mean-field approach to model the dynamics of drivers' spatial location, revenue, and availability status. We compare drivers' equilibrium participation under two different matching policies: an oblivious one that treats drivers symmetrically, and a reservation-aware one that prioritizes drivers according to their reservation wage. We show that the reservation-aware policy outperforms the oblivious, and can do up to twice as good, both in attracting driver supply and in responding to passenger demand. We demonstrate by simulation that the mean-field model provides an accurate approximation for a corresponding (stochastic) discrete model, in which the discussed improvement is observed empirically