NDLI: Dynamic pricing in ridesharing platforms

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Dynamic pricing in ridesharing platforms

Content Provider	ACM Digital Library
Author	Banerjee, Siddhartha Johari, Ramesh Riquelme, Carlos
Abstract	We study dynamic pricing policies for ridesharing platforms such as Lyft and Uber. On one hand these platforms are two-sided: this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support high temporal-resolution for data collection and pricing: this requires stochastic models that capture the dynamics of drivers and passengers in the system. We summarize our main results from [Banerjee et al. 2015], in which we study the role of dynamic pricing in ridesharing platforms using a queueing-theoretic economic model. We build a model of two-sided ridesharing platforms that captures both the stochastic dynamics of the marketplace and the strategic decisions of drivers, passengers and the platform. We show how our model can help explain the success of dynamic pricing in practice: in particular, we argue that the benefit of dynamic pricing over static pricing is not in the optimal performance, but rather, in the robustness of its performance to uncertainty in system parameters.
Starting Page	65
Ending Page	70
Page Count	6
File Format	PDF
ISSN	15519031
DOI	10.1145/2994501.2994505
Journal	ACM SIGecom Exchanges (SECO)
Volume Number	15
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-03-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pricing Ridesharing Dynamic pricing Two-sided platforms Market design
Content Type	Text
Resource Type	Article

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