Matching Impatient and Heterogeneous Demand and Supply

Abstract

Balancing Speed and Value in On-Demand Matching Platforms In “Matching Impatient and Heterogeneous Demand and Supply,” Aveklouris, DeValve, Stock, and Ward consider a fundamental trade-off faced by many platforms (e.g., Uber/Lyft) that match supply (e.g., drivers) and demand (e.g., riders) dynamically over time: making matches quickly capitalizes on the value of current supply and demand in the system, whereas waiting may enable better matches at the risk of losing impatient customers. They show that this trade-off can be balanced by waiting a short amount of time before making matches: long enough to gather enough agents to make valuable matches but not so long that impatient agents are likely to leave. Intuitively, this balance depends on how long agents are willing to wait, on average, but the authors show that it also depends on the full distribution of the willingness to wait (i.e., not only mean, but also variance and higher moments play a role). Thus, approaches that only take into account the mean willingness to wait may perform quite poorly. Further, the authors develop an algorithm to rank matching priorities in order to achieve an optimized trade-off between speed and value of matches.