Article Contents
Article Contents

# Optimal control and zero-sum games for Markov chains of mean-field type

• * Corresponding author: H. Tembine
• We establish existence of Markov chains of mean-field type with unbounded jump intensities by means of a fixed point argument using the total variation distance. We further show existence of nearly-optimal controls and, using a Markov chain backward SDE approach, we suggest conditions for existence of an optimal control and a saddle-point for respectively a control problem and a zero-sum differential game associated with payoff functionals of mean-field type, under dynamics driven by such Markov chains of mean-field type.

Mathematics Subject Classification: Primary: 60H10, 60H07; Secondary: 49N90.

 Citation:

•  [1] V. E. Beneš, Existence of optimal stochastic control laws, SIAM J. Control, 9 1971,446-472. doi: 10.1137/0309034. [2] P. Brèmaud, Point Processes and Queues: Martingale Dynamics, Springer-Verlag, New York-Berlin, 1981. [3] S. N. Cohen and R. J. Elliott, Existence, uniqueness and comparisons for BSDEs in general spaces, Annals of Probability, 40 (2012), 2264-2297.  doi: 10.1214/11-AOP679. [4] S. N. Cohen and R. J. Elliott, Stochastic Calculus and Applications, Second edition. Probability and its Applications. Springer, Cham, 2015. doi: 10.1007/978-1-4939-2867-5. [5] F. Confortola, M. Fuhrman and J. Jacod, Backward stochastic differential equations driven by a marked point process: an elementary approach, with an application to optimal control, Preprint, arXiv: 1407.0876, [math.PR], 2014. [6] S. E. Choutri and H. Tembine, A stochastic maximum principle for markov chains of mean-field type, Games, 9 (2018), Paper No. 84, 21 pp, https://doi.org/10.3390/g9040084. doi: 10.3390/g9040084. [7] D. Dawson and X. Zheng, Law of large numbers and central limit theorem for unbounded jump mean-field models, Advances in Applied Mathematics, 12 (1991), 293-326. doi: 10.1016/0196-8858(91)90015-B. [8] B. Djehiche and S. Hamadène, Optimal control and zero-sum stochastic differential game problems of mean-field type, Appl Math Optim., 2018, https://doi.org/10.1007/s00245-018-9525-6. [9] B. Djehiche and I. Kaj, The rate function for some measure-valued jump processes, The Annals of Probability, 23 (1995), 1414-1438.  doi: 10.1214/aop/1176988190. [10] B. Djehiche and A. Schied, Large deviations for hierarchical systems of interacting jump processes, Journal of Theoretical Probability, 11 (1998), 1-24.  doi: 10.1023/A:1021690707556. [11] I. Ekeland, On the variational principle, J. Math. Anal. Appl., 47 (1974), 324-353.  doi: 10.1016/0022-247X(74)90025-0. [12] N. El Karoui and S. Hamadène, BSDEs and risk-sensitive control, zero-sum and nonzero-sum game problems of stochastic functional differential equations, Stochastic Processes and their Application, 107 (2003), 145-169.  doi: 10.1016/S0304-4149(03)00059-0. [13] N. El Karoui, S. Peng and M.-C. Quenez, Backward stochastic differential equations in finance, Mathematical Finance, 7 (1997), 1-71.  doi: 10.1111/1467-9965.00022. [14] R. J. Elliott and M. Kohlmann, The variational principle and stochastic optimal control, Stochastics, 3 (1980), 229-241.  doi: 10.1080/17442508008833147. [15] S. N. Ethier and T. G. Kurtz, Markov Processes: Characterization and Convergence, John Wiley & Sons, Inc., New York, 1986. doi: 10.1002/9780470316658. [16] S. Feng, Large deviations for empirical process of mean-field interacting particle system with unbounded jumps, The Annals of Probability, 22 (1994), 2122-2151.  doi: 10.1214/aop/1176988496. [17] S. Feng and X. Zheng, Solutions of a class of nonlinear master equations, Stochastic processes and their applications, 43 (1992), 65-84.  doi: 10.1016/0304-4149(92)90076-3. [18] S. Hamadène and J. P. Lepeltier, Backward equations, stochastic control and zero-sum stochastic differential games, Stochastics Stochastics Rep., 54 (1995), 221-231. doi: 10.1080/17442509508834006. [19] B. Jourdain, S. Méléard and W. Woyczynski, Nonlinear SDEs driven by Lévy processes and related PDEs, ALEA Lat. Am. J. Probab. Math. Stat., 4 (2008), 1-29. [20] V. N. Kolokoltsov,  Nonlinear Markov Processes and Kinetic Equations, Cambridge University Press, Cambridge, 2010.  doi: 10.1017/CBO9780511760303. [21] V. N. Kolokoltsov, Nonlinear Markov games on a finite state space (mean-field and binary interactions), International Journal of Statistics and Probability, 1 (2012), 77. [22] C. Léonard, Some epidemic systems are long range interacting particle systems, Stochastic processes in epidemic systems (eds. J.P. Gabriel et al.), Lecture Notes in Biomathematics, volume 86, 1990, Springer. [23] C.Léonard, Large deviations for long range interacting particle systems with jumps, Annales de l'IHP Probabilités et Statistiques, 31 (1995), 289-323. [24] G. Nicolis and I. Prigogine, Self Organization in Non-Equilibrium Systems, New York-London-Sydney, 1977. [25] K. Oelschläger, A martingale approach to the law of large numbers for weakly interacting stochastic processes, The Annals of Probability, (1984), 458-479. [26] E. Pardoux and S. Peng, Adapted Solution of a Backward Stochastic Differential Equation, Systems and Control Letters, 14 (1990), 55-61. doi: 10.1016/0167-6911(90)90082-6. [27] L. C. G. Rogers and  D. Williams,  Diffusions, Markov Processes and Martingales-Volume 2: Itô Calculus, Cambridge University Press, Cambridge, 2000.  doi: 10.1017/CBO9781107590120. [28] F. Schlögl, Chemical reaction models for non-equilibrium phase transitions, Zeitschrift für Physik, 253 (1972), 147-161. [29] A. Sokol and N. R. Hansen, Exponential martingales and changes of measure for counting processes, Stochastic Analysis and Applications, 33 (2015), 823-843. doi: 10.1080/07362994.2015.1040890. [30] A.-S. Sznitman, Topics in propagation of chaos, Ecole d'Été de Probabilités de Saint-Flour XIX 1989, 1964 (1991), 165-251. doi: 10.1007/BFb0085169.