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

Filippo Fabiani; Barbara Franci

doi:10.1109/CDC51059.2022.9993244

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

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 language	English
Title of host publication	2022 IEEE 61st Conference on Decision and Control (CDC)
Pages	4455-4460
Number of pages	6
DOIs	https://doi.org/10.1109/CDC51059.2022.9993244
Publication status	Published - 2022
Event	2022 IEEE 61st Conference on Decision and Control - Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022

Conference

Conference	2022 IEEE 61st Conference on Decision and Control
Abbreviated title	CDC 2022
Country/Territory	Mexico
City	Cancun
Period	6/12/22 → 9/12/22

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10.1109/CDC51059.2022.9993244

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title = "A stochastic generalized Nash equilibrium model for platforms competition in the ride-hail market",

abstract = "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.",

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AU - Franci, Barbara

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N2 - 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.

AB - 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.

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DO - 10.1109/CDC51059.2022.9993244

M3 - Conference article in proceeding

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EP - 4460

BT - 2022 IEEE 61st Conference on Decision and Control (CDC)

T2 - 2022 IEEE 61st Conference on Decision and Control

Y2 - 6 December 2022 through 9 December 2022

ER -

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

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