Adaptive Interventions for Social Welfare Maximization in Network Games

Mehran Shakarami; Ashish Cherukuri; Nima Monshizadeh

doi:10.1109/CDC45484.2021.9682855

Adaptive Interventions for Social Welfare Maximization in Network Games

Mehran Shakarami, Ashish Cherukuri, Nima Monshizadeh

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

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Abstract

We consider the problem of steering the actions of noncooperative players in quadratic network games to the social optimum. To this end, a central regulator modifies the marginal returns of the players, while the players’ strategies are determined by continuous pseudo-gradient dynamics. Depending on the available information on the players parameters and network quantities, suitable static and dynamic intervention protocols are devised that maximize the social welfare at steady-state. We show that adaptive interventions can compensate for the lack of knowledge on network topology and coupling weights. Numerical examples are provided to demonstrate the effectiveness of the proposed interventions.

Original language	English
Title of host publication	2021 60th IEEE Conference on Decision and Control (CDC)
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-6654-3659-5, 978-1-6654-3658-8
ISBN (Print)	978-1-6654-3660-1
DOIs	https://doi.org/10.1109/CDC45484.2021.9682855
Publication status	Published - 1-Feb-2022
Event	The 60th IEEE conference on Decision and Control (CDC 2021) - Austin, Texas, United States Duration: 13-Dec-2021 → 17-Dec-2021

Conference

Conference	The 60th IEEE conference on Decision and Control (CDC 2021)
Country/Territory	United States
City	Austin, Texas
Period	13/12/2021 → 17/12/2021

Keywords

Game theory, Control of networks, Adaptive control

Access to Document

10.1109/CDC45484.2021.9682855

Adaptive Interventions for Social Welfare Maximization in Network GamesFinal publisher's version, 732 KBLicence: Taverne

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Cite this

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title = "Adaptive Interventions for Social Welfare Maximization in Network Games",

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AU - Monshizadeh, Nima

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N2 - We consider the problem of steering the actions of noncooperative players in quadratic network games to the social optimum. To this end, a central regulator modifies the marginal returns of the players, while the players’ strategies are determined by continuous pseudo-gradient dynamics. Depending on the available information on the players parameters and network quantities, suitable static and dynamic intervention protocols are devised that maximize the social welfare at steady-state. We show that adaptive interventions can compensate for the lack of knowledge on network topology and coupling weights. Numerical examples are provided to demonstrate the effectiveness of the proposed interventions.

AB - We consider the problem of steering the actions of noncooperative players in quadratic network games to the social optimum. To this end, a central regulator modifies the marginal returns of the players, while the players’ strategies are determined by continuous pseudo-gradient dynamics. Depending on the available information on the players parameters and network quantities, suitable static and dynamic intervention protocols are devised that maximize the social welfare at steady-state. We show that adaptive interventions can compensate for the lack of knowledge on network topology and coupling weights. Numerical examples are provided to demonstrate the effectiveness of the proposed interventions.

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