A stochastic generalized Nash equilibrium model for platforms competition in the ride-hail market

Filippo Fabiani, Barbara Franci

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


The inherent uncertainties in the ride-hailing market complicate the pricing strategies of on-demand platforms that compete each other to offer a mobility service while striving to maximize their profit. Looking at this problem as a stochastic generalized Nash equilibrium problem (SGNEP), we design a distributed, stochastic equilibrium seeking algorithm with Tikhonov regularization to find an optimal pricing strategy. The proposed iterative scheme does not require a potentially infinite number of samples of the random variable to perform the stochastic approximation, thus making it appealing from a practical perspective. Moreover, we show that the algorithm returns a Nash equilibrium under mere monotonicity assumptions and a careful choice of the step size sequence, obtained by exploiting the specific structure of the SGNEP at hand.
Original languageEnglish
Title of host publication2022 IEEE 61st Conference on Decision and Control (CDC)
Number of pages6
Publication statusPublished - 2022
Event2022 IEEE 61st Conference on Decision and Control - Cancun, Mexico
Duration: 6 Dec 20229 Dec 2022


Conference2022 IEEE 61st Conference on Decision and Control
Abbreviated titleCDC 2022

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