Relative Best Response Dynamics in finite and convex Network Games

Alain Govaert, Carlo Cenedese, Sergio Grammatico, Ming Cao

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationproceedings of the IEEE 58th Conference on Decision and Control, CDC 2019
PublisherIEEE
Pages3134-3139
ISBN (Electronic)978-1-7281-1398-2
DOIs
Publication statusPublished - 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: 11 Dec 201913 Dec 2019

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
Country/TerritoryFrance
CityNice
Period11/12/1913/12/19

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