Publication

Relative Best Response Dynamics in Finite and Convex Network Games

Govaert, A., Cenedese, C., Grammatico, S. & Cao, M., 12-Mar-2020, Proceedings of the 58th IEEE Conference on Decision and Control. IEEE, p. 3134-3139 6 p. 9029821

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

APA

Govaert, A., Cenedese, C., Grammatico, S., & Cao, M. (2020). Relative Best Response Dynamics in Finite and Convex Network Games. In Proceedings of the 58th IEEE Conference on Decision and Control (pp. 3134-3139). [9029821] IEEE. https://doi.org/10.1109/CDC40024.2019.9029821

Author

Govaert, Alain ; Cenedese, Carlo ; Grammatico, Sergio ; Cao, Ming. / Relative Best Response Dynamics in Finite and Convex Network Games. Proceedings of the 58th IEEE Conference on Decision and Control. IEEE, 2020. pp. 3134-3139

Harvard

Govaert, A, Cenedese, C, Grammatico, S & Cao, M 2020, Relative Best Response Dynamics in Finite and Convex Network Games. in Proceedings of the 58th IEEE Conference on Decision and Control., 9029821, IEEE, pp. 3134-3139, 58th Conference on Decision and Control (CDC2019), Nice, France, 11/12/2019. https://doi.org/10.1109/CDC40024.2019.9029821

Standard

Relative Best Response Dynamics in Finite and Convex Network Games. / Govaert, Alain; Cenedese, Carlo; Grammatico, Sergio; Cao, Ming.

Proceedings of the 58th IEEE Conference on Decision and Control. IEEE, 2020. p. 3134-3139 9029821.

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

Vancouver

Govaert A, Cenedese C, Grammatico S, Cao M. Relative Best Response Dynamics in Finite and Convex Network Games. In Proceedings of the 58th IEEE Conference on Decision and Control. IEEE. 2020. p. 3134-3139. 9029821 https://doi.org/10.1109/CDC40024.2019.9029821


BibTeX

@inproceedings{eba82dacbfec4cb78b0d49fe83bc5d86,
title = "Relative Best Response Dynamics in Finite and Convex Network Games",
abstract = "Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h–RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time–varying subset of their neighbors. As such, the h-RBR dynamics share the defining non–innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h–RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite–time convergence to a generalized Nash equilibrium. ",
author = "Alain Govaert and Carlo Cenedese and Sergio Grammatico and Ming Cao",
year = "2020",
month = mar,
day = "12",
doi = "10.1109/CDC40024.2019.9029821",
language = "English",
isbn = "978-1-7281-1398-2",
pages = "3134--3139",
booktitle = "Proceedings of the 58th IEEE Conference on Decision and Control",
publisher = "IEEE",
note = "58th Conference on Decision and Control (CDC2019) ; Conference date: 11-12-2019 Through 13-12-2019",

}

RIS

TY - GEN

T1 - Relative Best Response Dynamics in Finite and Convex Network Games

AU - Govaert, Alain

AU - Cenedese, Carlo

AU - Grammatico, Sergio

AU - Cao, Ming

PY - 2020/3/12

Y1 - 2020/3/12

N2 - Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h–RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time–varying subset of their neighbors. As such, the h-RBR dynamics share the defining non–innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h–RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite–time convergence to a generalized Nash equilibrium.

AB - Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h–RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time–varying subset of their neighbors. As such, the h-RBR dynamics share the defining non–innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h–RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite–time convergence to a generalized Nash equilibrium.

U2 - 10.1109/CDC40024.2019.9029821

DO - 10.1109/CDC40024.2019.9029821

M3 - Conference contribution

SN - 978-1-7281-1398-2

SP - 3134

EP - 3139

BT - Proceedings of the 58th IEEE Conference on Decision and Control

PB - IEEE

T2 - 58th Conference on Decision and Control (CDC2019)

Y2 - 11 December 2019 through 13 December 2019

ER -

ID: 109250413