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Mean Field Games with Common Noises and Conditional Distribution Dependent FBSDEs

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Abstract

In this paper, the authors consider the mean field game with a common noise and allow the state coefficients to vary with the conditional distribution in a nonlinear way. They assume that the cost function satisfies a convexity and a weak monotonicity property. They use the sufficient Pontryagin principle for optimality to transform the mean field control problem into existence and uniqueness of solution of conditional distribution dependent forward-backward stochastic differential equation (FBSDE for short). They prove the existence and uniqueness of solution of the conditional distribution dependent FBSDE when the dependence of the state on the conditional distribution is sufficiently small, or when the convexity parameter of the running cost on the control is sufficiently large. Two different methods are developed. The first method is based on a continuation of the coefficients, which is developed for FBSDE by [Hu, Y. and Peng, S., Solution of forward-backward stochastic differential equations, Probab. Theory Rel., 103(2), 1995, 273–283]. They apply the method to conditional distribution dependent FBSDE. The second method is to show the existence result on a small time interval by Banach fixed point theorem and then extend the local solution to the whole time interval.

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References

  1. Ahuja, S., Wellposedness of mean field games with common noise under a weak monotonicity condition, SIAM J. Control Optim., 54(1), 2016, 30–48.

    Article  MathSciNet  Google Scholar 

  2. Ahuja, S., Ren, W. and Yang, T. W., Forward-backward stochastic differential equations with monotone functionals and mean field games with common noise, Stoch. Proc. Appl., 129(10), 2019, 3859–3892.

    Article  MathSciNet  Google Scholar 

  3. Cardaliaguet, P., Notes on mean field games, P.-L. Lions’ lectures, College de France, Paris, 2010.

    Google Scholar 

  4. Carmona, R. and Delarue, F., Probabilistic analysis of mean-field games, SIAM J. Control Optim., 51(4), 2013, 2705–2734.

    Article  MathSciNet  Google Scholar 

  5. Carmona, R. and Delarue, F., Forward-backward stochastic differential equations and controlled McKean-Vlasov dynamics, Ann. Probab., 43(5), 2015, 2647–2700.

    Article  MathSciNet  Google Scholar 

  6. Carmona, R., Delarue, F. and Lacker, D., Mean field games with common noise, Ann. Probab., 44(6), 2016, 3740–3803.

    Article  MathSciNet  Google Scholar 

  7. Carmona, R., Fouque, J. P. and Sun, L. H., Mean field games and systemic risk, Commun. Math. Sci., 13(4), 2015, 911–933.

    Article  MathSciNet  Google Scholar 

  8. Carmona, R. and Lacker, D., A probabilistic weak formulation of mean field games and applications, Ann. Appl. Probab., 25(3), 2015, 1189–1231.

    Article  MathSciNet  Google Scholar 

  9. Hu, Y. and Peng, S., Solution of forward-backward stochastic differential equations, Probab. Theory Rel., 103(2), 1995, 273–283.

    Article  MathSciNet  Google Scholar 

  10. Huang, M., Caines, P. E. and Malhamé, R. P., Large-population cost-coupled lqg problems with nonuniform agents: Individual-mass behavior and decentralized ε-nash equilibria, IEEE T. Automat. Contr., 52(9), 2017, 1560–1571.

    Article  MathSciNet  Google Scholar 

  11. Huang, M., Malhamé, R. P. and Caines, P. E., Large population stochastic dynamic games: Closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle, Commun. Inf. Sys., 6(3), 2006, 221–252.

    Article  MathSciNet  Google Scholar 

  12. Kolokoltsov, V. N., Li, J. and Yang, W., Mean field games and nonlinear Markov processes, 2011, arXiv: 1112.3744v2.

  13. Lacker, D., A general characterization of the mean field limit for stochastic differential games, Probab. Theory Rel., 165(3), 2016, 581–648.

    Article  MathSciNet  Google Scholar 

  14. Lasry, J.-M. and Lions, P.-L., Jeux à champ moyen, I-le cas stationnaire, C.R.A.S. Math., 343(9), 2006, 619–625.

    Article  MathSciNet  Google Scholar 

  15. Lasry, J.-M. and Lions, P.-L., Jeux à champ moyen, II-horizon fini et contrôle optimal, C.R.A.S. Math., 343(10), 2006, 679–684.

    Article  Google Scholar 

  16. Lasry, J.-M. and Lions, P.-L., Mean field games, Jpn. J. Math., 2(1), 2007, 229–260.

    Article  MathSciNet  Google Scholar 

  17. Nourian, M. and Caines, P. E., ε-Nash mean field game theory for nonlinear stochastic dynamical systems with major and minor agents, SIAM J. Control Optim., 51(4), 2013, 3302–3331.

    Article  MathSciNet  Google Scholar 

  18. Peng, S. and Wu, Z., Fully coupled forward-backward stochastic differential equations and applications to optimal control, SIAM J. Control Optim., 37(3), 1999, 825–843.

    Article  MathSciNet  Google Scholar 

  19. Pham, H., Continuous-time Stochastic Control and Optimization with Financial Applications, 61, Springer-Verlag, Berlin Heidelberg, 2009.

    Book  Google Scholar 

  20. Pham, H., Linear quadratic optimal control of conditional McKean-Vlasov equation with random coefficients and applications, Probability, Uncertainty and Quantitative Risk, 1(1), 2016, 252–277.

    Article  MathSciNet  Google Scholar 

  21. Pham, H. and Wei, X., Dynamic programming for optimal control of stochastic McKean-Vlasov dynamics, SIAM J. Control Optim., 55(2), 2017, 1069–1101.

    Article  MathSciNet  Google Scholar 

  22. Sznitman, A.-S., Topics in propagation of chaos, 1464, Springer-Verlag, Berlin Heidelberg, 1991.

    Book  Google Scholar 

  23. Yong, J. and Zhou, X. Y., Stochastic Controls: Hamiltonian Systems and HJB Equations, 43, Springer-Verlag, New York, 1999.

    Book  Google Scholar 

  24. Yu, J. and Tang, S., Mean-field game with degenerate state- and distribution-dependent noise, Chinese Ann. Math., 41A(3), 2020, 233–262 (in Chinese).

    MathSciNet  MATH  Google Scholar 

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Correspondence to Ziyu Huang or Shanjian Tang.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 12031009).

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Huang, Z., Tang, S. Mean Field Games with Common Noises and Conditional Distribution Dependent FBSDEs. Chin. Ann. Math. Ser. B 43, 523–548 (2022). https://doi.org/10.1007/s11401-022-0344-3

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  • DOI: https://doi.org/10.1007/s11401-022-0344-3

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