Quadratic Mean Field Games

Mean field games were introduced independently by J-M. Lasry and P-L. Lions, and by M. Huang, R.P. Malham\'e and P. E. Caines, in order to bring a new approach to optimization problems with a large number of interacting agents. The description of such models split in two parts, one describing the evolution of the density of players in some parameter space, the other the value of a cost functional each player tries to minimize for himself, anticipating on the rational behavior of the others. Quadratic Mean Field Games form a particular class among these systems, in which the dynamics of each player is governed by a controlled Langevin equation with an associated cost functional quadratic in the control parameter. In such cases, there exists a deep relationship with the non-linear Schr\"odinger equation in imaginary time, connexion which lead to effective approximation schemes as well as a better understanding of the behavior of Mean Field Games. The aim of this paper is to serve as an introduction to Quadratic Mean Field Games and their connexion with the non-linear Schr\"odinger equation, providing to physicists a good entry point into this new and exciting field.Comment: 62 pages, 4 figure

'Phase diagram' of a mean field game

Mean field games were introduced by J-M.Lasry and P-L. Lions in the mathematical community, and independently by M. Huang and co-workers in the engineering community, to deal with optimization problems when the number of agents becomes very large. In this article we study in detail a particular example called the 'seminar problem' introduced by O.Gu\'eant, J-M Lasry, and P-L. Lions in 2010. This model contains the main ingredients of any mean field game but has the particular feature that all agent are coupled only through a simple random event (the seminar starting time) that they all contribute to form. In the mean field limit, this event becomes deterministic and its value can be fixed through a self consistent procedure. This allows for a rather thorough understanding of the solutions of the problem, through both exact results and a detailed analysis of various limiting regimes. For a sensible class of initial configurations, distinct behaviors can be associated to different domains in the parameter space . For this reason, the 'seminar problem' appears to be an interesting toy model on which both intuition and technical approaches can be tested as a preliminary study toward more complex mean field game models

Study of some models from Mean Field Games theory

La théorie des jeux en champ moyen constitue un formalisme puissant introduit récemmentpour étudier des problèmes d’optimisation stochastiques avec un grand nombre d’agents. Aprèsavoir rappelé les principes de base de cette théorie et présenté quelques cas d’applicationtypiques, on étudie en détail un modèle stylisé de séminaire, de type champ moyen. Nousdérivons une équation exacte qui permet de prédire l’heure de commencement du séminaire etanalysons différents régimes limites, dans lesquels on parvient à des expressions approchées de lasolution. Ainsi on obtient un "diagramme de phase" du problème. On aborde ensuite un modèleplus complexe de population avec des effets de groupe attractifs. Grâce à une analogie formelleavec l’équation de Schrödinger non linéaire, on met en évidence des lois d’évolutions généralespour les valeurs moyennes du problème, que le système vérifie certaines lois de conservation etl’ on développe des approximations de type variationnel. Cela nous permet de comprendre lecomportement qualitatif du problème dans le régime de fortes interactions.Mean Field Games Theory is a theoretical framework developed recently to deal withstochastic optimization problems when the number of agents is large. First the mathematicaltools are introduced heuristically, step by step, and some examples are presented in finance,economy and social problems. I study then thoroughly a seminar toymodel and derive anequation for the starting time of the meeting. The analysis of the limit regimes allows to builda "phase diagram" of the problem. In a second time, a herding problem, where individualshave their own preferences and are attracted by the group, is tackled. Thanks to a formal analogywith the Non Linear Schrödinger equation, some explicit solutions, conservation laws andso-called variational approximations are derived. Finally I use these tools to get a qualitativeunderstanding of the solution’s behaviour in the strong interaction regime

Physica A ''Phase diagram'' of a mean field game

h i g h l i g h t s • We study a simple model of ''mean field game''. • We provide an exact solution of the associated system of coupled differential equations. • We analyze the resulting self-consistent equation in various limiting regimes, resulting in the construction of a ''phase diagram'' of the considered mean field game. a r t i c l e i n f o t r a c t Mean field games were introduced by J-M. Lasry and P-L. Lions in the mathematical community, and independently by M. Huang and co-workers in the engineering community, to deal with optimization problems when the number of agents becomes very large. In this article we study in detail a particular example called the ''seminar problem'' introduced by O. Guéant, J-M. Lasry, and P-L. Lions in 2010. This model contains the main ingredients of any mean field game but has the particular feature that all agents are coupled only through a simple random event (the seminar starting time) that they all contribute to form. In the mean field limit, this event becomes deterministic and its value can be fixed through a self consistent procedure. This allows for a rather thorough understanding of the solutions of the problem, through both exact results and a detailed analysis of various limiting regimes. For a sensible class of initial configurations, distinct behaviors can be associated to different domains in the parameter space. For this reason, the ''seminar problem'' appears to be an interesting toy model on which both intuition and technical approaches can be tested as a preliminary study toward more complex mean field game models

Schrödinger approach to mean field games

5 pages, 1 figure, should interest people working in both the physical and economical communityInternational audienceMean Field Games (MFG) provide a theoretical frame to model socio-economic systems. In this letter, we study a particular class of MFG which shows strong analogies with the {\em non-linear Schr\"odinger and Gross-Pitaevski equations} introduced in physics to describe a variety of physical phenomena ranging from deep-water waves to interacting bosons. Using this bridge many results and techniques developed along the years in the latter context can be transferred to the former. As an illustration, we study in some details an example in which the "players" in the mean field game are under a strong incentive to coordinate themselves