A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021

Expectations versus fundamentals: does the cause of banking panics matter for prudential policy?

There is a longstanding debate about whether banking panics and other financial crises always have fundamental causes or are sometimes the result of self-fulfilling beliefs. Disagreement on this point would seem to present a serious obstacle to designing policies that promote financial stability. However, we show that the appropriate choice of policy is invariant to the underlying cause of banking panics in some situations. In our model, the anticipation of being bailed out in the event of a crisis distorts the incentives of financial institutions and their investors. Two policies that aim to correct this distortion are compared: restricting policymakers from engaging in bailouts, and allowing bailouts but taxing the short-term liabilities of financial institutions. We find that the latter policy yields higher equilibrium welfare regardless of whether panics are sometimes caused by self-fulfilling beliefs.Financial crises ; Financial stability ; Monetary policy ; Economic policy