Relative Best Response Dynamics in Finite and Convex Network Games

Alain Govaert, Carlo Cenedese, Sergio Grammatico, Ming Cao

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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 58th IEEE Conference on Decision and Control
PublisherIEEE
Pages3134-3139
Number of pages6
ISBN (Print)978-1-7281-1398-2
DOIs
Publication statusPublished - 12-Mar-2020
Event58th Conference on Decision and Control (CDC2019) - Nice, France
Duration: 11-Dec-201913-Dec-2019

Conference

Conference58th Conference on Decision and Control (CDC2019)
CountryFrance
CityNice
Period11/12/201913/12/2019

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