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Bio: Nikhil Garg is a PhD candidate in Electrical Engineering at Stanford University, advised by Ashish Goel and Ramesh Johari and part of the Society and Algorithms Lab and Stanford Crowdsourced Democracy Team. He received a M.S. in Electrical Engineering from Stanford in 2017, and a B.S. in Computer Engineering and a B.A. in Plan II (Liberal Arts) in 2015 from the University of Texas at Austin. He has spent time at Uber, NASA, Microsoft, the Texas Senate, and IEEE's policy arm, and has been awarded a NSF Graduate Research Fellowship and McCoy Family Center for Ethics in Society Graduate Fellowship.
Title: Driver Surge pricing.
Abstract: Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study pricing mechanisms for such marketplaces from the perspective of drivers, presenting the theoretical foundation that has informed the design of Uber's new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (non-surge), and so trips of different time lengths vary in the opportunity cost they impose on drivers. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well-approximated by Uber's new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more approximately incentive compatible in practice than multiplicative surge, providing more stable earnings to drivers. Joint work with Hamid Nazerzadeh.