Sequential Individual Rationality in Dynamic Ridesharing

Abstract

In dynamic ridesharing systems, both operational and economic policies impact Quality-of-Service (QoS). Field experiments have found that firms benefit from proactively compensating users whose QoS expectations are violated. This motivates a broader analytical study of how behavioral perceptions of QoS impact operational and economic policy design in ridesharing systems. We introduce a novel, QoS-centric framework with the following elements: (a) users’ state-dependent utility model that bridges operational (detours) and economic effects (prices or cost shares), (b) dynamic QoS notion, sequential individual rationality, defined on the sequence of (dis)utilities from successive stages of a shared ride, influenced by behavioral drivers (reference/recency effects, loss aversion), and (c) QoS-sensitive economic objectives (profit or fairness) that endogenize users’ choices and QoS constraints. Our framework can be used to extract key operational insights from QoS-sensitive economic objectives in two different environments: (i) commercial ridesharing, which involves pricing exclusive/shared service to maximize profit (considering penalties for QoS-violations), and (ii) community carpooling, which involves designing fair cost sharing schemes.

In the commercial setting, we characterize a ride’s optimal shareable region, and show that it may be optimal for a QoS-sensitive service provider to violate QoS and get penalized, no matter how strong the users’ loss aversion. In the carpooling setting, we characterize routes that admit budget-balanced, QoS-compliant cost sharing schemes, resulting in a ride’s QoS-compliant shareable region. We define sequential fairness and characterize fair, QoS-compliant schemes that bring out insightful structural properties, including a strong requirement that commuters must compensate each other for the detour-inconveniences they cause.

Keywords: ridesharing, carpooling, pricing, cost sharing, individual rationality, fairness, quality of service, behavioral operations