Mean field social optimization: Feedback person-by-person optimality and the master equation

Minyi Huang, Shuenn Jyi Sheu, Li Hsien Sun

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

Abstract

This paper considers a nonlinear mean field social optimization problem which aims to minimize a social cost. By use of a finite player model, we apply dynamic programming to formalize a person-by-person (PbP) optimality condition in a feedback form. This procedure leads to a new Hamilton-Jacobi-Bellman equation which involves differentiation with respect to probability measure and is called the master equation of social optimization. For the linear-quadratic (LQ) case, an explicit solution of the master equation is obtained.

Original languageEnglish
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4921-4926
Number of pages6
ISBN (Electronic)9781728174471
DOIs
StatePublished - 14 Dec 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Period14/12/2018/12/20

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