Randomized FIFO Mechanisms
Video
Team Information
Team Members
Hongyao Ma, Assistant Professor, Decision, Risk & Operations, Graduate School of Business, Columbia University
Francisco Castro, Assistant Professor, Anderson School of Business, UCLA
Hamid Nazerzadeh, Associate Professor in Business Administration, Department of Data Sciences and Operations, Marshall School of Business, USC
Chiwei Yan, Assistant Professor of Operations Research, Department of Industrial and Systems Engineering, University of Washington
Abstract
We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under strict FIFO dispatching, the inequity in earnings from different trips incentivizes drivers to cherrypick, increasing riders’ waiting times for a match, and resulting in a loss of efficiency and reliability. We propose a direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue, where the option to skip the rest of the line incentivizes the drivers to accept. It is a subgame perfect equilibrium for drivers to accept all dispatches, and the equilibrium outcome is envy-free and achieves the second best revenue and throughput. Without using trip-specific information in dispatching, a family of randomized FIFO mechanisms also achieve the second best at the cost of a variance in driver earnings which diminishes as riders’ patience increases. Counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput in comparison to the status-quo strict FIFO dispatching.
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Team Contact: Hongyao Ma (use form to send email)
