Price Cycles in Ridesharing Platforms

TL;DR

This paper analyzes how driver behaviors in ridesharing platforms create price cycles, demonstrating their equilibrium nature and proposing price floors to mitigate these fluctuations, thereby improving market stability.

Contribution

It introduces a continuous time model showing online/offline driver strategies can be Nash equilibria and proposes a pricing policy to reduce price cycles.

Findings

Price cycles can be stable equilibria among drivers.

Market density affects the efficiency of driver strategies.

Price floors can effectively mitigate price fluctuations.

Abstract

In ridesharing platforms such as Uber and Lyft, it is observed that drivers sometimes collaboratively go offline when the price is low, and then return after the price has risen due to the perceived lack of supply. This collective strategy leads to cyclic fluctuations in prices and available drivers, resulting in poor reliability and social welfare. We study a continuous time, non-atomic model and prove that such online/offline strategies may form a Nash equilibrium among drivers, but lead to a lower total driver payoff if the market is sufficiently dense. Further, we show how to set price floors that effectively mitigate the emergence and impact of price cycles.