Mean field games of controls: Finite difference approximations

We consider a class of mean field games in which the agents interact through both their states and controls, and we focus on situations in which a generic agent tries to adjust her speed (control) to an average speed (the average is made in a neighborhood in the state space). In such cases, the mono...

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Main Authors: Yves Achdou, Ziad Kobeissi
Format: Article
Language:English
Published: AIMS Press 2021-03-01
Series:Mathematics in Engineering
Subjects:
Online Access:http://www.aimspress.com/article/doi/10.3934/mine.2021024?viewType=HTML
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spelling doaj-764b164aa71b48cda97d0425773046652021-03-11T01:19:37ZengAIMS PressMathematics in Engineering2640-35012021-03-013313510.3934/mine.2021024Mean field games of controls: Finite difference approximationsYves Achdou0Ziad Kobeissi1Université de Paris and Sorbonne Université, CNRS, Laboratoire Jacques-Louis Lions, (LJLL), F-75006 Paris, FranceUniversité de Paris and Sorbonne Université, CNRS, Laboratoire Jacques-Louis Lions, (LJLL), F-75006 Paris, FranceWe consider a class of mean field games in which the agents interact through both their states and controls, and we focus on situations in which a generic agent tries to adjust her speed (control) to an average speed (the average is made in a neighborhood in the state space). In such cases, the monotonicity assumptions that are frequently made in the theory of mean field games do not hold, and uniqueness cannot be expected in general. Such model lead to systems of forward-backward nonlinear nonlocal parabolic equations; the latter are supplemented with various kinds of boundary conditions, in particular Neumann-like boundary conditions stemming from reflection conditions on the underlying controled stochastic processes. The present work deals with numerical approximations of the above megntioned systems. After describing the finite difference scheme, we propose an iterative method for solving the systems of nonlinear equations that arise in the discrete setting; it combines a continuation method, Newton iterations and inner loops of a bigradient like solver. The numerical method is used for simulating two examples. We also make experiments on the behaviour of the iterative algorithm when the parameters of the model vary.http://www.aimspress.com/article/doi/10.3934/mine.2021024?viewType=HTMLmean field gamesinteractions via controlscrowd motionnumerical simulationsfinite difference method
collection DOAJ
language English
format Article
sources DOAJ
author Yves Achdou
Ziad Kobeissi
spellingShingle Yves Achdou
Ziad Kobeissi
Mean field games of controls: Finite difference approximations
Mathematics in Engineering
mean field games
interactions via controls
crowd motion
numerical simulations
finite difference method
author_facet Yves Achdou
Ziad Kobeissi
author_sort Yves Achdou
title Mean field games of controls: Finite difference approximations
title_short Mean field games of controls: Finite difference approximations
title_full Mean field games of controls: Finite difference approximations
title_fullStr Mean field games of controls: Finite difference approximations
title_full_unstemmed Mean field games of controls: Finite difference approximations
title_sort mean field games of controls: finite difference approximations
publisher AIMS Press
series Mathematics in Engineering
issn 2640-3501
publishDate 2021-03-01
description We consider a class of mean field games in which the agents interact through both their states and controls, and we focus on situations in which a generic agent tries to adjust her speed (control) to an average speed (the average is made in a neighborhood in the state space). In such cases, the monotonicity assumptions that are frequently made in the theory of mean field games do not hold, and uniqueness cannot be expected in general. Such model lead to systems of forward-backward nonlinear nonlocal parabolic equations; the latter are supplemented with various kinds of boundary conditions, in particular Neumann-like boundary conditions stemming from reflection conditions on the underlying controled stochastic processes. The present work deals with numerical approximations of the above megntioned systems. After describing the finite difference scheme, we propose an iterative method for solving the systems of nonlinear equations that arise in the discrete setting; it combines a continuation method, Newton iterations and inner loops of a bigradient like solver. The numerical method is used for simulating two examples. We also make experiments on the behaviour of the iterative algorithm when the parameters of the model vary.
topic mean field games
interactions via controls
crowd motion
numerical simulations
finite difference method
url http://www.aimspress.com/article/doi/10.3934/mine.2021024?viewType=HTML
work_keys_str_mv AT yvesachdou meanfieldgamesofcontrolsfinitedifferenceapproximations
AT ziadkobeissi meanfieldgamesofcontrolsfinitedifferenceapproximations
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