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An introduction to mean field game theory. (English) Zbl 1457.91058

Cardaliaguet, Pierre (ed.) et al., Mean field games. Cetraro, Italy, June 10–14, 2019. Lecture notes given at the summer school. Cham: Springer. Lect. Notes Math. 2281, 1-158 (2020).
Summary: These notes are an introduction to mean field game (MFG) theory, which models differential games involving infinitely many interacting players. We focus here on the partial differential equations (PDEs) approach to MFGs. The two main parts of the text correspond to the two emblematic equations in MFG theory: the first part is dedicated to the MFG system, while the second part is devoted to the master equation. The MFG system describes Nash equilibrium configurations in the mean field approach to differential games with infinitely many players. It consists in the coupling between a backward Hamilton-Jacobi equation (for the value function of a single player) and a forward Fokker-Planck equation (for the distribution law of the individual states). We discuss the existence and the uniqueness of the solution to the MFG system in several frameworks, depending on the presence or not of a diffusion term and on the nature of the interactions between the players (local or nonlocal coupling). We also explain how these different frameworks are related to each other. As an application, we show how to use the MFG system to find approximate Nash equilibria in games with a finite number of players and we discuss the asymptotic behavior of the MFG system. The master equation is a PDE in infinite space dimension: more precisely it is a kind of transport equation in the space of measures. The interest of this equation is that it allows to handle more complex MFG problems as, for instance, MFG problems involving a randomness affecting all the players. To analyse this equation, we first discuss the notion of derivative of maps defined on the space of measures; then we present the master equation in several frameworks (classical form, case of finite state space and case with common noise); finally we explain how to use the master equation to prove the convergence of Nash equilibria of games with finitely many players as the number of players tends to infinity. As the works on MFGs are largely inspired by P.L. Lions’ courses held at the Collège de France in the years 2007–2012, we complete the text with an appendix describing the organization of these courses.
For the entire collection see [Zbl 1456.49002].

MSC:

91A16 Mean field games (aspects of game theory)
35Q91 PDEs in connection with game theory, economics, social and behavioral sciences
49N80 Mean field games and control
Full Text: DOI

References:

[1] Y. Achdou, Finite difference methods for mean field games, in Hamilton-Jacobi Equations: Approximations, Numerical Analysis and Applications, ed. by P. Loreti, N.A. Tchou. Lecture Notes in Mathematics, vol. 2074 (Springer, Heidelberg, 2013), pp. 1-47 · Zbl 1271.65120
[2] Achdou, Y.; Capuzzo Dolcetta, I., Mean field games: numerical methods, SIAM J. Numer. Anal., 48, 1136-1162 (2010) · Zbl 1217.91019 · doi:10.1137/090758477
[3] Y. Achdou, Z. Kobeissi, Mean Field Games of Controls: Finite Difference Approximations (2020). Preprint arXiv:2003.03968
[4] Achdou, Y.; Porretta, A., Convergence of a finite difference scheme to weak solutions of the system of partial differential equation arising in mean field games, SIAM J. Numer. Anal., 54, 161-186 (2016) · Zbl 1382.65273 · doi:10.1137/15M1015455
[5] Achdou, Y.; Porretta, A., Mean field games with congestion, Ann. I. H. Poincaré, 35, 443-480 (2018) · Zbl 1476.35100 · doi:10.1016/j.anihpc.2017.06.001
[6] Achdou, Y.; Camilli, F.; Capuzzo Dolcetta, I., Mean field games: numerical methods for the planning problem, SIAM J. Control Opt., 50, 77-109 (2012) · Zbl 1242.91014 · doi:10.1137/100790069
[7] Achdou, Y.; Buera, FJ; Lasry, J-M; Lions, P-L; Moll, B., Partial differential equation models in macroeconomics, Philos. Trans. R. Soc. A, 372, 20130397 (2014) · Zbl 1353.91027 · doi:10.1098/rsta.2013.0397
[8] Achdou, Y.; Bardi, M.; Cirant, M., Mean field games models of segregation, Math. Models Methods Appl. Sci., 27, 75-113 (2017) · Zbl 1355.91003 · doi:10.1142/S0218202517400036
[9] Y. Achdou, J. Han, J.-M. Lasry, P.-L. Lions, B. Moll, Income and wealth distribution in macroeconomics: a continuous-time approach. Technical report, National Bureau of Economic Research, 2017
[10] Achdou, Y.; Dao, MK; Ley, O.; Tchou, N., A class of infinite horizon mean field games on networks, Netw. Heterog. Media, 14, 537-566 (2019) · Zbl 1423.35372 · doi:10.3934/nhm.2019021
[11] Ahuja, S., Well-posedness of mean field games with common noise under a weak monotonicity condition, SIAM J. Control Optim., 54, 30-48 (2016) · Zbl 1327.93403 · doi:10.1137/140974730
[12] Ahuja, S.; Ren, W.; Yang, T-W, Asymptotic analysis of mean field games with small common noise, Asymptot. Anal., 106, 205-232 (2018) · Zbl 1392.35314 · doi:10.3233/ASY-171446
[13] Aiyagari, SR, Uninsured idiosyncratic risk and aggregate saving, Quart. J. Econ., 109, 659-684 (1994) · doi:10.2307/2118417
[14] Albeverio, S.; Kondratiev, YG; Röckner, M., Analysis and geometry on configuration spaces, J. Funct. Anal., 154, 444-500 (1998) · Zbl 0914.58028 · doi:10.1006/jfan.1997.3183
[15] Ambrosio, L., Transport equation and cauchy problem for BV vector fields, Invent. Math., 158, 227-260 (2004) · Zbl 1075.35087 · doi:10.1007/s00222-004-0367-2
[16] L. Ambrosio, Transport equation and cauchy problem for non-smooth vector fields, in Calculus of Variations and Nonlinear Partial Differential Equations. Lecture Notes in Mathematics, vol. 1927 (Springer, Berlin, 2008), pp. 1-41 · Zbl 1159.35041
[17] L. Ambrosio, W. Gangbo, Hamiltonian odes in the Wasserstein space of probability measures. Commun. Pure Appl. Math.: J. Issued Courant Inst. Math. Sci. 61, 18-53 (2008) · Zbl 1132.37028
[18] Ambrosio, L.; Gigli, N.; Savaré, G., Gradient Flows: In Metric Spaces and in the Space of Probability Measures (2006), New York: Springer, New York · Zbl 1145.35001
[19] A. Alfonsi, B. Jourdain, Lifted and geometric differentiability of the squared quadratic Wasserstein distance (2018). Preprint. arXiv:1811.07787 · Zbl 1454.90028
[20] Aumann, RJ, Markets with a continuum of traders, Econometrica: J. Econ. Soc., 1964, 39-50 (1964) · Zbl 0137.39003 · doi:10.2307/1913732
[21] Balandat, M.; Tomlin, CJ, On efficiency in mean field differential games, in 2013 American Control Conference, June 2013, 2527-2532 (2013), New York: IEEE, New York
[22] J.M. Ball, A Version of the Fundamental Theorem for Young Measures, PDE’s and Continuum Models of Phase Transitions, ed. by Rascle, M., Serre, D., Slemrod, M. Lecture Notes in Physics, vol. 344 (Springer, Berlin, 1989), pp. 207-215 · Zbl 0991.49500
[23] M. Bardi, P. Cardaliaguet, Convergence of some Mean Field Games systems to aggregation and flocking models (2020). Preprint. arXiv:2004.04403 · Zbl 1460.35349
[24] Bardi, M.; Cirant, M., Uniqueness of solutions in mean field games with several populations and Neumann conditions, in PDE Models for Multi-agent Phenomena, 1-20 (2018), New York: Springer, New York · Zbl 1414.35107
[25] Bardi, M.; Fischer, M., On non-uniqueness and uniqueness of solutions in finite-horizon mean field games, ESAIM: Control Optim. Calculus Var., 25, 44 (2019) · Zbl 1437.91049
[26] Bayraktar, E.; Cohen, A., Analysis of a finite state many player game using its master equation, SIAM J. Control Optim., 56, 3538-3568 (2018) · Zbl 1416.91013 · doi:10.1137/17M113887X
[27] E. Bayraktar, A. Cecchin, A. Cohen, F. Delarue, Finite state mean field games with Wright-Fisher common noise (2019). Preprint. arXiv:1912.06701 · Zbl 1459.91012
[28] Benamou, J-D; Brenier, Y., A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem, Numer. Math., 84, 375-393 (2000) · Zbl 0968.76069 · doi:10.1007/s002110050002
[29] Benamou, JD; Carlier, G.; Santambrogio, F., Variational mean field games, in Active Particles,, Birkhäuser. Cham, vol. 1, 141-171 (2017)
[30] Bensoussan, A.; Frehse, J.; Yam, P., Mean Field Games and Mean Field Type Control Theory (2013), New York: Springer, New York · Zbl 1287.93002 · doi:10.1007/978-1-4614-8508-7
[31] Bensoussan, A.; Chau, M.; Yam, S., Mean field games with a dominating player, Appl. Math. Optim., 74, 91-128 (2016) · Zbl 1348.49031 · doi:10.1007/s00245-015-9309-1
[32] Bertucci, C., Optimal stopping in mean field games, an obstacle problem approach, J. Math. Pures Appl., 120, 165-194 (2018) · Zbl 1406.35448 · doi:10.1016/j.matpur.2017.09.016
[33] C. Bertucci, Fokker-Planck equations of jumping particles and mean field games of impulse control. In Annales de l’Institut Henri Poincaré C, Analyse non linéaire (2020) · Zbl 1456.49030
[34] Bertucci, C.; Lasry, J-M; Lions, P-L, Some remarks on mean field games, Comm. Part. Differ. Equ., 44, 205-227 (2019) · Zbl 1411.91100 · doi:10.1080/03605302.2018.1542438
[35] C. Bertucci, J.-M. Lasry, P.-L. Lions, Master equation for the finite state space planning problem (2020). Preprint arXiv:2002.09330
[36] C. Bertucci, J.-M. Lasry, P.-L. Lions, Strategic advantages in mean field games with a major player (2020). Preprint arXiv:2002.07034 · Zbl 1445.91002
[37] Billingsley, P., Probability and Measure (2008), New York: Wiley, New York · Zbl 0411.60001
[38] Bouveret, G.; Dumitrescu, R.; Tankov, P., Mean-field games of optimal stopping: a relaxed solution approach, Siam J. Control Optim., 58, 1795-1821 (2020) · Zbl 1452.91031 · doi:10.1137/18M1233480
[39] Brenier, Y., Polar factorization and monotone rearrangement of vector-valued functions, Commun. Pure Appl. Math., 44, 375-417 (1991) · Zbl 0738.46011 · doi:10.1002/cpa.3160440402
[40] Briani, A.; Cardaliaguet, P., Stable solutions in potential mean field game systems, Nonlinear Differ. Equ. Appl., 25, 1 (2018) · Zbl 1390.35365 · doi:10.1007/s00030-017-0493-3
[41] Buckdahn, R.; Li, J.; Peng, S.; Rainer, C., Mean-field stochastic differential equations and associated PDEs, Ann. Probab., 45, 824-878 (2017) · Zbl 1402.60070 · doi:10.1214/15-AOP1076
[42] Burger, M.; Lorz, A.; Wolfram, MT, On a Boltzmann mean field model for knowledge growth, SIAM J. Appl. Math., 76, 5, 1799-1818 (2016) · Zbl 1347.49057 · doi:10.1137/15M1018599
[43] Burger, M.; Lorz, A.; Wolfram, MT, Balanced growth path solutions of a Boltzmann mean field game model for knowledge growth, Kinet. Relat. Models, 10, 1, 117-140 (2017) · Zbl 1352.49005 · doi:10.3934/krm.2017005
[44] Camilli, F.; De Maio, R., A time-fractional mean field game, Adv. Differ. Equ., 24, 9-10, 531-554 (2019) · Zbl 1442.35502
[45] Camilli, F.; Marchi, C., Stationary mean field games systems defined on networks, SIAM J. Control Optim., 54, 2, 1085-1103 (2016) · Zbl 1382.91015 · doi:10.1137/15M1022082
[46] L. Campi, M. Fischer, n-player games and mean-field games with absorption. Ann. Appl. Probab. 28, 2188-2242 (2018) · Zbl 1420.91020
[47] Cannarsa, P.; Capuani, R., Existence and uniqueness for mean field games with state constraints, in PDE Models for Multi-agent Phenomena, 49-71 (2018), Cham: Springer, Cham · Zbl 1419.49047 · doi:10.1007/978-3-030-01947-1_3
[48] Cannarsa, P.; Sinestrari, C., Semiconcave functions, in Hamilton-Jacobi Equations and Optimal Control (2004), Boston: Birkhauser, Boston · Zbl 1095.49003 · doi:10.1007/b138356
[49] Cannarsa, P.; Capuani, R.; Cardaliaguet, P., C^1, 1-smoothness of constrained solutions in the calculus of variations with application to mean field games, Math. Eng., 1, 174-203 (2019) · Zbl 1433.49002 · doi:10.3934/Mine.2018.1.174
[50] P. Cannarsa, R. Capuani, P. Cardaliaguet, Mean field games with state constraints: from mild to pointwise solutions of the pde system (2018). Preprint. arXiv:1812.11374 · Zbl 1467.49023
[51] P. Cardaliaguet, Notes on mean field games, Technical report, 2010
[52] Cardaliaguet, P., Long time average of first order mean field games and weak KAM theory, Dynam. Games Appl., 3, 473-488 (2013) · Zbl 1314.91043 · doi:10.1007/s13235-013-0091-x
[53] Cardaliaguet, P., Weak solutions for first order mean field games with local coupling, in Analysis and Geometry in Control Theory and Its Applications, 111-158 (2015), Cham: Springer, Cham · Zbl 1329.49068
[54] Cardaliaguet, P.; Graber, PJ, Mean field games systems of first order, ESAIM: Control Optim. Calc. Var., 21, 690-722 (2015) · Zbl 1319.35273
[55] Cardaliaguet, P.; Hadikhanloo, S., Learning in mean field games: the fictitious play, ESAIM: Control Optim. Calc. Var., 23, 2, 569-591 (2017) · Zbl 1365.35183
[56] Cardaliaguet, P.; Lehalle, C-A, Mean field game of controls and an application to trade crowding, Math. Finan. Econ., 12, 335-363 (2018) · Zbl 1397.91084 · doi:10.1007/s11579-017-0206-z
[57] Cardaliaguet, P.; Masoero, M., Weak KAM theory for potential MFG, J. Differ. Equ., 268, 3255-3298 (2020) · Zbl 1437.37098 · doi:10.1016/j.jde.2019.09.060
[58] Cardaliaguet, P.; Porretta, A., Long time behavior of the master equation in mean field game theory, Anal. PDE, 12, 1397-1453 (2019) · Zbl 1428.35607 · doi:10.2140/apde.2019.12.1397
[59] Cardaliaguet, P.; Rainer, C., On the (in) efficiency of MFG equilibria, SIAM J. Control Optim., 57, 4, 2292-2314 (2019) · Zbl 1422.91079 · doi:10.1137/18M1172363
[60] Cardaliaguet, P.; Lasry, J-M; Lions, P-L; Porretta, A., Long time average of mean field games, Netw. Heterog. Media, 7, 279-301 (2012) · Zbl 1270.35098 · doi:10.3934/nhm.2012.7.279
[61] Cardaliaguet, P.; Lasry, J-M; Lions, P-L; Porretta, A., Long time average of mean field games with a nonlocal coupling, SIAM J. Control Optim., 51, 3558-3591 (2013) · Zbl 1332.35364 · doi:10.1137/120904184
[62] Cardaliaguet, P.; Graber, PJ; Porretta, A.; Tonon, D., Second order mean field games with degenerate diffusion and local coupling, Nonlinear Differ. Equ. Appl., 22, 1287-1317 (2015) · Zbl 1344.49061 · doi:10.1007/s00030-015-0323-4
[63] Cardaliaguet, P.; Mészáros, AR; Santambrogio, F., First order mean field games with density constraints: pressure equals price, SIAM J. Control Optim., 54, 2672-2709 (2016) · Zbl 1356.35257 · doi:10.1137/15M1029849
[64] Cardaliaguet, P.; Cirant, M.; Porretta, A., Remarks on Nash equilibria in mean field game models with a major player, Proceedings of the American Math. Society, 148, 4241-4255 (2020) · Zbl 1446.35213 · doi:10.1090/proc/15135
[65] Cardaliaguet, P.; Delarue, F.; Lasry, J-M; Lions, P-L, The Master Equation and the Convergence Problem in Mean Field Games:(AMS-201) (2019), Princeton: Princeton University Press, Princeton · Zbl 1430.91002 · doi:10.1515/9780691193717
[66] P. Cardaliaguet, M. Cirant, A. Porretta, Splitting methods and short time existence for the master equations in mean field games (2020). Preprint. arXiv:2001.10406 · Zbl 1446.35213
[67] Carmona, R.; Delarue, F., Probabilistic analysis of mean-field games, SIAM J. Control Optim., 51, 2705-2734 (2013) · Zbl 1275.93065 · doi:10.1137/120883499
[68] Carmona, R.; Delarue, F., Probabilistic Theory of Mean Field Games with Applications (2017), New York: Springer Verlag, New York · Zbl 1422.91014
[69] Carmona, R.; Lacker, D., A probabilistic weak formulation of mean field games and applications, Ann. Appl. Probab., 25, 1189-1231 (2015) · Zbl 1332.60100 · doi:10.1214/14-AAP1020
[70] R. Carmona, P. Wang, Finite state mean field games with major and minor players (2016). Preprint. arXiv:1610.05408
[71] Carmona, R.; Wang, P., An alternative approach to mean field game with major and minor players, and applications to herders impacts, Appl. Math. Optim., 76, 5-27 (2017) · Zbl 1378.49036 · doi:10.1007/s00245-017-9430-4
[72] Carmona, R.; Zhu, X., A probabilistic approach to mean field games with major and minor players, Ann. Appl. Probab., 26, 1535-1580 (2016) · Zbl 1342.93121 · doi:10.1214/15-AAP1125
[73] Carmona, R.; Delarue, F.; Lacker, D., Mean field games with common noise, Ann. Probab., 44, 3740-3803 (2016) · Zbl 1422.91083 · doi:10.1214/15-AOP1060
[74] Carmona, R.; Delarue, F.; Lacker, D., Mean field games of timing and models for bank runs, Appl. Math. Optim., 76, 217-260 (2017) · Zbl 1411.91102 · doi:10.1007/s00245-017-9435-z
[75] Carmona, R.; Graves, CV; Tan, Z., Price of anarchy for mean field games, ESAIM: Proc. Surv., 65, 349-383 (2019) · Zbl 1417.91071 · doi:10.1051/proc/201965349
[76] R. Carmona, M. Laurière, Z. Tan, Linear-quadratic mean-field reinforcement learning: convergence of policy gradient methods (2019). Preprint. arXiv:1910.04295
[77] R. Carmona, M. Laurière, Z. Tan, Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learning (2019). Preprint. arXiv:1910.12802
[78] Cecchin, A.; Fischer, M., Probabilistic approach to finite state mean field games, Appl. Math. Optim., 2018, 1-48 (2018) · Zbl 1434.60198
[79] Cecchin, A.; Pelino, G., Convergence, fluctuations and large deviations for finite state mean field games via the master equation, Stoch. Process. Appl., 129, 4510-4555 (2019) · Zbl 1450.60015 · doi:10.1016/j.spa.2018.12.002
[80] Cecchin, A.; Pra, PD; Fischer, M.; Pelino, G., On the convergence problem in mean field games: a two state model without uniqueness, SIAM J. Control Optim., 57, 2443-2466 (2019) · Zbl 1426.91025 · doi:10.1137/18M1222454
[81] A. Cesaroni, M. Cirant, Concentration of ground states in stationary MFG systems. Analysis PDE (2019) · Zbl 1404.35155
[82] Cesaroni, A.; Dirr, N.; Marchi, C., Homogenization of a mean field game system in the small noise limit, SIAM J. Math. Anal., 48, 2701-2729 (2016) · Zbl 1347.35028 · doi:10.1137/16M1063459
[83] Cesaroni, A.; Cirant, M.; Dipierro, S.; Novaga, M.; Valdinoci, E., On stationary fractional mean field games, J. Math. Pures Appl., 122, 1-22 (2019) · Zbl 1405.35246 · doi:10.1016/j.matpur.2017.10.013
[84] Chan, P.; Sircar, R., Fracking, renewables, and mean field games, SIAM Rev., 59, 3, 588-615 (2017) · Zbl 1369.91146 · doi:10.1137/15M1031424
[85] J.-F. Chassagneux, D. Crisan, F. Delarue, Classical solutions to the master equation for large population equilibria. Preprint arXiv:1411.3009
[86] Cirant, M., Multi-population mean field games systems with Neumann boundary conditions, J. Math. Pures Appl., 103, 1294-1315 (2015) · Zbl 1320.35347 · doi:10.1016/j.matpur.2014.10.013
[87] Cirant, M., Stationary focusing mean-field games, Commun. Part. Differ. Equ., 41, 1324-1346 (2016) · Zbl 1352.49040 · doi:10.1080/03605302.2016.1192647
[88] Cirant, M., On the existence of oscillating solutions in non-monotone mean-field games, J. Differ. Equ., 266, 8067-8093 (2019) · Zbl 1408.35075 · doi:10.1016/j.jde.2018.12.025
[89] Cirant, M.; Goffi, A., On the existence and uniqueness of solutions to time-dependent fractional MFG, SIAM J. Math. Anal., 51, 2, 913-954 (2019) · Zbl 1408.35211 · doi:10.1137/18M1216420
[90] Cirant, M.; Nurbekyan, L., The variational structure and time-periodic solutions for mean-field games systems, Minimax Theory Appl., 3, 227-260 (2018) · Zbl 1406.35417
[91] M. Cirant, A. Porretta, Long time behavior and turnpike solutions in non monotone mean field games. Preprint · Zbl 1471.91028
[92] Cirant, M.; Tonon, D., Time-dependent focusing mean-field games: the sub-critical case, J. Dyn. Differ. Equ., 31, 49-79 (2019) · Zbl 1408.35076 · doi:10.1007/s10884-018-9667-x
[93] Cirant, M.; Verzini, G., Bifurcation and segregation in quadratic two-populations mean field games systems, ESAIM: Control Optim. Calc. Var., 23, 1145-1177 (2017) · Zbl 1371.35110
[94] F.H. Clarke, Optimization and Nonsmooth Analysis, 2nd edn. Classics in Applied Mathematics, vol. 5 (Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1990) · Zbl 0696.49002
[95] Delarue, F.; Tchuendom, RF, Selection of equilibria in a linear quadratic mean-field game, Stoch. Process. Appl., 130, 1000-1040 (2020) · Zbl 1471.91029 · doi:10.1016/j.spa.2019.04.005
[96] F. Delarue, D. Lacker, K. Ramanan, From the master equation to mean field game limit theory: a central limit theorem. Electron. J. Probab. 24, 54 pp. (2019) · Zbl 1508.60032
[97] Delarue, F.; Lacker, D.; Ramanan, K., From the master equation to mean field game limit theory: large deviations and concentration of measure, Ann. Probab., 48, 211-263 (2020) · Zbl 1445.60025 · doi:10.1214/19-AOP1359
[98] DiPerna, RJ; Lions, P-L, Ordinary differential equations, transport theory and sobolev spaces, Invent. Math., 98, 511-547 (1989) · Zbl 0696.34049 · doi:10.1007/BF01393835
[99] Degond, P.; Liu, JG; Ringhofer, C., Large-scale dynamics of mean-field games driven by local Nash equilibria, J. Nonlinear Sci., 24, 1, 93-115 (2014) · Zbl 1295.91015 · doi:10.1007/s00332-013-9185-2
[100] I. Ekeland, R. Témam, R. Convex Analysis and Variational Problems, English ed., vol. 28. Classics in Applied Mathematics (Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1999). Translated from the French · Zbl 0939.49002
[101] Elie, R.; Mastrolia, T.; Possamaï, D., A tale of a principal and many, many agents, Math. Oper. Res., 44, 40-467 (2019) · Zbl 1443.91198 · doi:10.1287/moor.2018.0931
[102] R. Elie, J. Pérolat, M. Laurière, M. Geist, O. Pietquin, Approximate fictitious play for mean field games (2019). Preprint. arXiv:1907.02633
[103] O. Ersland, E.R. Jakobsen, On classical solutions of time-dependent fractional mean field game systems (2020). Preprint. arXiv:2003.12302
[104] Ferreira, R.; Gomes, D., Existence of weak solutions to stationary mean-field games through variational inequalities, SIAM J. Math. Anal., 50, 5969-6006 (2018) · Zbl 1418.91087 · doi:10.1137/16M1106705
[105] Fischer, M., On the connection between symmetric n-player games and mean field games, Ann. Appl. Probab., 27, 757-810 (2017) · Zbl 1375.91009 · doi:10.1214/16-AAP1215
[106] Fleming, WH; Rishel, RW, Deterministic and Stochastic Optimal Control (2012), Berlin: Springer Science & Business Media, Berlin · Zbl 0323.49001
[107] Fleming, WH; Soner, HM, Controlled Markov Processes and Viscosity Solutions (2006), Berlin: Springer Science & Business Media, Berlin · Zbl 1105.60005
[108] Gangbo, W.; Swiech, A., Existence of a solution to an equation arising from the theory of mean field games, J. Differ. Equ., 259, 6573-6643 (2015) · Zbl 1359.35221 · doi:10.1016/j.jde.2015.08.001
[109] Gangbo, W.; Tudorascu, A., On differentiability in the Wasserstein space and well-posedness for Hamilton-Jacobi equations, J. Math. Pures Appl., 125, 119-174 (2019) · Zbl 1419.35234 · doi:10.1016/j.matpur.2018.09.003
[110] Gilbarg, D.; Trudinger, NS, Elliptic Partial Differential Equations of Second Order (2015), Berlin: Springer, Berlin · Zbl 0361.35003
[111] Golse, F., On the dynamics of large particle systems in the mean field limit, in Macroscopic and Large Scale Phenomena: Coarse Graining, Mean Field Limits and Ergodicity, 1-144 (2016), Cham: Springer, Cham
[112] Gomes, D.; Patrizi, S., Obstacle mean-field game problem, Interfaces Free Bound., 17, 55-68 (2015) · Zbl 1327.35119 · doi:10.4171/IFB/333
[113] Gomes, DA; Mitake, H., Existence for stationary mean-field games with congestion and quadratic Hamiltonians, Nonlinear Differ. Equ. Appl., 22, 1897-1910 (2015) · Zbl 1343.91003 · doi:10.1007/s00030-015-0349-7
[114] Gomes, DA; Pimentel, EA, Time dependent mean-field games with logarithmic nonlinearities, SIAM J. Math. Anal., 47, 3798-3812 (2015) · Zbl 1331.35009 · doi:10.1137/140984622
[115] Gomes, D.; Saùde, J., Mean field games models - a brief survey, Dyn. Games Appl., 4, 110-154 (2014) · Zbl 1314.91048 · doi:10.1007/s13235-013-0099-2
[116] Gomes, DA; Voskanyan, VK, Short-time existence of solutions for mean-field games with congestion, J. Lond. Math. Soc., 92, 778-799 (2015) · Zbl 1338.35219 · doi:10.1112/jlms/jdv052
[117] Gomes, DA; Voskanyan, VK, Extended deterministic mean-field games, SIAM J. Control Optim., 54, 1030-1055 (2016) · Zbl 1343.49060 · doi:10.1137/130944503
[118] Gomes, DA; Mohr, J.; Souza, RR, Discrete time, finite state space mean field games, J. Math. Pures Appl., 93, 308-328 (2010) · Zbl 1192.91028 · doi:10.1016/j.matpur.2009.10.010
[119] Gomes, DA; Patrizi, S.; Voskanyan, V., On the existence of classical solutions for stationary extended mean field games, Nonlinear Anal. Theory Methods Appl., 99, 49-79 (2014) · Zbl 1284.49044 · doi:10.1016/j.na.2013.12.016
[120] Gomes, DA; Pimentel, EA; Sànchez-Morgado, H., Time-dependent mean-field games in the subquadratic case, Commun. Part. Differ. Eq., 40, 40-76 (2015) · Zbl 1322.35053 · doi:10.1080/03605302.2014.903574
[121] Gomes, DA; Pimentel, EA; Sànchez-Morgado, H., Time-dependent mean-field games in the superquadratic case, ESAIM Control. Optim. Calc. Var., 22, 562-580 (2016) · Zbl 1339.35090 · doi:10.1051/cocv/2015029
[122] Gomes, DA; Pimentel, EA; Voskanyan, V., Regularity Theory for Mean-Field Game Systems (2016), Berlin: Springer, Berlin · Zbl 1391.91003 · doi:10.1007/978-3-319-38934-9
[123] P.J. Graber, Weak solutions for mean field games with congestion (2015). ArXiv e-print 1503.04733
[124] Graber, PJ; Mészáros, AR, Sobolev regularity for first order mean field games, Annales de l’Institut Henri Poincaré C, Analyse non linéaire, 35, 1557-1576 (2018) · Zbl 1397.49054 · doi:10.1016/j.anihpc.2018.01.002
[125] Graber, PJ; Mouzouni, C., On mean field games models for exhaustible commodities trade, ESAIM: Control Optim. Calc. Var., 26, 11 (2020) · Zbl 1437.35664
[126] Graber, PJ; Mészáros, AR; Silva, FJ; Tonon, D., The planning problem in mean field games as regularized mass transport, Calc. Var. Part. Differ. Equ., 58, 115 (2019) · Zbl 1416.49045 · doi:10.1007/s00526-019-1561-9
[127] O. Gueant, A reference case for mean field games models. J. Math. Pures Appl. (9) 92, 76-294 (2009) · Zbl 1173.91020
[128] Gueant, O.; Lasry, J-M; Lions, P-L, Mean field games and applications, in Paris-Princeton Lectures on Mathematical Finance 2010, 205-266 (2011), Berlin, Heidelberg: Springer, Berlin, Heidelberg · Zbl 1205.91027 · doi:10.1007/978-3-642-14660-2_3
[129] Hadikhanloo, S.; Silva, FJ, Finite mean field games: fictitious play and convergence to a first order continuous mean field game, J. Math. Pures Appl., 132, 369-397 (2019) · Zbl 1427.35288 · doi:10.1016/j.matpur.2019.02.006
[130] Huang, M., Large-population LQG games involving a major player: the Nash certainty equivalence principle, SIAM J. Control Optim., 48, 3318-3353 (2010) · Zbl 1200.91020 · doi:10.1137/080735370
[131] M. Huang, P.E. Caines, R.P. Malhamé, Individual and mass behaviour in large population stochastic wireless power control problems: centralized and Nash equilibrium solutions, in 42nd IEEE Conference on Decision and Control, 2003. Proceedings, vol. 1 (IEEE, New York, 2003), pp. 98-103
[132] Huang, M.; Malhamé, RP; Caines, PE, Large population stochastic dynamic games: closed-loop Mckean-Vlasov systems and the nash certainty equivalence principle, Commun. Inf. Syst., 6, 221-252 (2006) · Zbl 1136.91349
[133] Huang, M.; Malhamé, RP; Caines, PE, An invariance principle in large population stochastic dynamic games, J. Syst. Sci. Complex., 20, 162-172 (2007) · Zbl 1280.91020 · doi:10.1007/s11424-007-9015-4
[134] Huang, M.; Malhamé, RP; Caines, PE, Large-population cost-coupled LQG problems with nonuniform agents: individual-mass behavior and decentralized ε-Nash equilibria, IEEE Trans. Autom. Control, 52, 1560-1571 (2007) · Zbl 1366.91016 · doi:10.1109/TAC.2007.904450
[135] M. Huang, R.P. Malhamé, P.E. Caines, The Nash certainty equivalence principle and Mckean-Vlasov systems: an invariance principle and entry adaptation, in 2007 46th IEEE Conference on Decision and Control ( IEEE, New York, 2007), pp. 121-126
[136] Kizilkale, AC; Caines, PE, Mean field stochastic adaptive control, IEEE Trans. Autom. Control, 58, 4, 905-920 (2012) · Zbl 1369.93731 · doi:10.1109/TAC.2012.2228032
[137] V.N. Kolokoltsov, J. Li, W. Yang, Mean field games and nonlinear Markov processes (2011). Preprint. arXiv:1112.3744
[138] Krusell, P.; Smith, AA Jr, Income and wealth heterogeneity in the macroeconomy, J. Polit. Econ., 106, 867-896 (1998) · doi:10.1086/250034
[139] Lacker, D., A general characterization of the mean field limit for stochastic differential games, Probab. Theory Relat. Fields, 165, 581-648 (2016) · Zbl 1344.60065 · doi:10.1007/s00440-015-0641-9
[140] Lacker, D., On the convergence of closed-loop Nash equilibria to the mean field game limit, Ann. Appl. Probab., 30, 1693-1761 (2020) · Zbl 1470.91036 · doi:10.1214/19-AAP1541
[141] Lacker, D.; Webster, K., Translation invariant mean field games with common noise, Electron. Commun. Probab., 20, 1-13 (2015) · Zbl 1332.60102 · doi:10.1214/ECP.v20-3822
[142] Ladyzhenskaia, OA; Solonnikov, VA; Uraltseva, N., Linear and Quasi-Linear Equations of Parabolic Type (1998), Providence, RI: American Mathematical Society, Providence, RI
[143] Lasry, J-M; Lions, P-L, Jeux à champ moyen, I -le cas stationnaire. Comptes Rendus Mathématique, 343, 619-625 (2006) · Zbl 1153.91009 · doi:10.1016/j.crma.2006.09.019
[144] Lasry, J-M; Lions, P-L, Jeux à champ moyen, II -horizon fini et contrôle optimal. Comptes Rendus Mathématique, 343, 679-684 (2006) · Zbl 1153.91010 · doi:10.1016/j.crma.2006.09.018
[145] Lasry, J-M; Lions, P-L, Mean field games, Jpn. J. Math., 2, 229-260 (2007) · Zbl 1156.91321 · doi:10.1007/s11537-007-0657-8
[146] Lasry, J-M; Lions, P-L, Mean-field games with a major player, Comptes Rendus Mathematique, 356, 886-890 (2018) · Zbl 1410.91048 · doi:10.1016/j.crma.2018.06.001
[147] Lavenant, H.; Santambrogio, F., Optimal density evolution with congestion: l^∞ bounds via flow interchange techniques and applications to variational mean field games, Commun. Part. Differ. Equ., 43, 1761-1802 (2018) · Zbl 1414.49026 · doi:10.1080/03605302.2018.1499116
[148] Lavenant, H.; Santambrogio, F., New estimates on the regularity of the pressure in density-constrained mean field games, J. Lond. Math. Soc., 100, 644-667 (2019) · Zbl 1431.35031 · doi:10.1112/jlms.12245
[149] P.-L. Lions, Cours au college de france, 2007-2012
[150] Lions, P-L; Lasry, J-M, Large investor trading impacts on volatility, in Paris-Princeton Lectures on Mathematical Finance 2004, 173-190 (2007), Berlin: Springer, Berlin · Zbl 1153.91428 · doi:10.1007/978-3-540-73327-0_4
[151] P.-L. Lions, P.E. Souganidis, Homogenization of the backward-forward mean-field games systems in periodic environments (2019). Preprint arXiv:1909.01250
[152] Lucas, RE Jr; Moll, B., Knowledge growth and the allocation of time, J. Polit. Econ., 122, 1, 1-51 (2014) · doi:10.1086/674363
[153] Masoero, M., On the long time convergence of potential MFG, Nonlinear Differ. Equ. Appl., 26, 15 (2019) · Zbl 1418.49004 · doi:10.1007/s00030-019-0560-z
[154] Mazanti, G.; Santambrogio, F., Minimal-time mean field games, Math. Models Methods Appl. Sci., 29, 1413-1464 (2019) · Zbl 1428.91006 · doi:10.1142/S0218202519500258
[155] Mészáros, AR; Silva, FJ, On the variational formulation of some stationary second-order mean field games systems, SIAM J. Math. Anal., 50, 1255-1277 (2018) · Zbl 1384.49031 · doi:10.1137/17M1125960
[156] Nutz, M., A mean field game of optimal stopping, SIAM J. Control Optim., 56, 1206-1221 (2018) · Zbl 1407.91040 · doi:10.1137/16M1078331
[157] Nutz, M.; San Martin, J.; Tan, X., Convergence to the mean field game limit: a case study, Ann. Appl. Probab., 30, 259-286 (2020) · Zbl 1437.91058 · doi:10.1214/19-AAP1501
[158] Orrieri, C.; Porretta, A.; Savaré, G., A variational approach to the mean field planning problem, J. Funct. Anal., 277, 1868-1957 (2019) · Zbl 1423.49044 · doi:10.1016/j.jfa.2019.04.011
[159] Overbeck, L.; Rockner, M.; Schmuland, B., An analytic approach to Fleming-Viot processes with interactive selection, Ann. Probab., 23, 1-36 (1995) · Zbl 0833.60053 · doi:10.1214/aop/1176988374
[160] G. Papanicolaou, L. Ryzhik, K. Velcheva, Travelling waves in a mean field learning model (2020). Preprint. arXiv:2002.06287 · Zbl 1469.91033
[161] Pedregal, P., Optimization, relaxation and Young measures, Bull. Am. Math. Soc., 36, 27-58 (1999) · Zbl 0916.49011 · doi:10.1090/S0273-0979-99-00774-0
[162] Peng, S., Stochastic Hamilton-Jacobi-Bellman equations, SIAM J. Control Optim., 30, 284-304 (1992) · Zbl 0747.93081 · doi:10.1137/0330018
[163] A. Porretta, On the planning problem for a class of Mean Field Games. C. R. Acad. Sci. Paris, Ser. I 351, 457-462 (2013) · Zbl 1273.91101
[164] Porretta, A., On the planning problem for the Mean Field Games system, Dyn. Games Appl., 4, 231-256 (2014) · Zbl 1314.91021 · doi:10.1007/s13235-013-0080-0
[165] Porretta, A., Weak solutions to Fokker-Planck equations and mean field games, Arch. Ration. Mech. Anal., 216, 1-62 (2015) · Zbl 1312.35168 · doi:10.1007/s00205-014-0799-9
[166] Porretta, A., On the weak theory for mean field games systems, Boll. Unione Mat. Ital., 10, 411-439 (2017) · Zbl 1411.91107 · doi:10.1007/s40574-016-0105-x
[167] Porretta, A., On the turnpike property in mean field games, Minimax Theory Appl., 3, 285-312 (2018) · Zbl 1406.49025
[168] Porretta, A.; Ricciardi, M., Mean field games under invariance conditions for the state space, Commun. Part. Differ. Equ., 45, 146-190 (2020) · Zbl 1430.35111 · doi:10.1080/03605302.2019.1666281
[169] A. Porretta, L. Rossi, Traveling waves for a nonlocal KPP equation and mean-field game models of knowledge diffusion. preprint arxiv:2010.10828v1
[170] Porretta, A.; Zuazua, E., Long time versus steady state optimal control, Siam J. Control Optim., 51, 4242-4273 (2013) · Zbl 1287.49006 · doi:10.1137/130907239
[171] Rachev, ST; Rüschendorf, L., Mass Transportation Problems: Volume I: Theory (1998), Berlin: Springer Science & Business Media, Berlin · Zbl 0990.60500
[172] Revuz, D.; Yor, M., Continuous Martingales and Brownian Motion (2013), Berlin: Springer Science & Business Media, Berlin · Zbl 0804.60001
[173] Santambrogio, F., A modest proposal for MFG with density constraints, Netw. Heterog. Media, 7, 337-347 (2012) · Zbl 1268.91023 · doi:10.3934/nhm.2012.7.337
[174] F. Santambrogio, Optimal Transport for Applied Mathematicians (Birkäuser, New York, NY, 2015), pp. 99-102 · Zbl 1401.49002
[175] Santambrogio, F., Regularity via duality in calculus of variations and degenerate elliptic PDEs, J. Math. Anal. Appl., 457, 1649-1674 (2018) · Zbl 1377.35079 · doi:10.1016/j.jmaa.2017.01.030
[176] Spohn, H., Large Scale Dynamics of Interacting Particles (2012), Berlin: Springer Science & Business Media, Berlin · Zbl 0742.76002
[177] Sznitman, A-S, Topics in propagation of chaos, in Ecole d’été de probabilités de Saint-Flour XIX—1989, 165-251 (1991), Berlin: Springer, Berlin · Zbl 0732.60114
[178] Villani, C., Topics in Optimal Transportation (2003), Providence: American Mathematical Society, Providence · Zbl 1106.90001
[179] Villani, C., Optimal Transport: Old and New (2008), Berlin: Springer Science & Business Media, Berlin · Zbl 1156.53003
[180] Yong, J.; Zhou, XY, Stochastic controls: Hamiltonian systems and HJB equations (1999), Berlin: Springer Science & Business Media, Berlin · Zbl 0943.93002 · doi:10.1007/978-1-4612-1466-3
[181] L.C. Young, Lectures on Calculus of Variations and Optimal Control Theory (W. B. Saunders, Philadelphia, 1969). Reprinted by Chelsea, 1980 · Zbl 0177.37801
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