Constrained Mean-Field-Type Games: Stationary Case

Julian Barreiro-Gomez, Hamidou Tembine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we study a class of constrained mean-field-type games with several decision-makers interacting in a stationary environment. The payoff functionals depend not only on the strategy profile but also on the first moment. This novel static game approach leads to risk-aware solution concepts. We introduce several solution concepts: mean-field-type best-response strategies, mean-field-type generalized Nash equilibria, mean-field-type variational equilibria. We provide a structure of payoffs with a decomposition that includes variance-aware cost. We discuss some learning algorithms to reach mean-field-type best Nash equilibria under constraints. The methodology is extended to include migration costs and migration incentives for each decision-maker and each choice component.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2208-2213
Number of pages6
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
CountryFrance
CityNice
Period12/11/1912/13/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint Dive into the research topics of 'Constrained Mean-Field-Type Games: Stationary Case'. Together they form a unique fingerprint.

Cite this