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doi: 10.3934/mcrf.2022029
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Control theory approach to continuous-time finite state mean field games

Higher School of Economics, Moscow, Russia

Received  March 2021 Revised  January 2022 Early access July 2022

Fund Project: The article was prepared in the framework of a research grant funded by the Ministry of Science and Higher Education of the Russian Federation (grant ID: 075-15-2020-928)

In the paper, we study the dependence of solutions of the continuous-time finite state mean field game on initial distribution of players. Our approach relies on the concept of value multifunction of the mean field game that is a mapping assigning to an initial time and an initial distribution a set of expected outcomes of the representative player corresponding to solutions of the mean field game. Using the reformulation of the finite state mean field game as a control problem with mixed constraints, we give the sufficient condition on a given multifunction to be a value multifunction in the terms of the viability theory. The maximal multifunction (i.e., the mapping assigning to an initial time and an initial distribution the whole set of values corresponding to solutions of the mean field game) is characterized via the backward attainability set for the certain control system.

Citation: Yurii Averboukh. Control theory approach to continuous-time finite state mean field games. Mathematical Control and Related Fields, doi: 10.3934/mcrf.2022029
References:
[1]

C. D. Aliprantis and K. C. Border, Infinite Dimensional Analysis: A Hitchhiker's Guide, Springer, Berlin, Heidelberg, 2006.

[2]

L. Ambrosio, N. Gigli and G. Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures, Lectures in Mathematics ETH Zurich, Birkhäuser, Basel, 2005.

[3]

J.-P. Aubin, Viability Theory, Systems & Control: Foundations & Applications. Birkhäuser Boston, Inc., Boston, MA, 1991.

[4]

R. BasnaA. Hilbert and V. N. Kolokoltsov, An approximate Nash equilibrium for pure jump Markov games of mean-field-type on continuous state space, Stochastics, 89 (2017), 967-993.  doi: 10.1080/17442508.2017.1297812.

[5]

E. BayraktarA. CecchinA. Cohen and F. Delarue, Finite state mean field games with Wright-Fisher common noise, J. Math. Pures Appl., 147 (2021), 98-162.  doi: 10.1016/j.matpur.2021.01.003.

[6]

E. BayraktarA. CecchinA. Cohen and F. Delarue, Finite state mean field games with Wright-Fisher common noise as limits of ${N}$-player weighted games, Math. Oper. Res., 147 (2021), 98-162.  doi: 10.1016/j.matpur.2021.01.003.

[7]

E. Bayraktar and A. Cohen, Analysis of a finite state many player game using its master equation, SIAM J. Control. Optim., 56 (2018), 3538-3568.  doi: 10.1137/17M113887X.

[8]

E. Bayraktar and X. Zhang, On non-uniqueness in mean field games, Proc. Amer. Math. Soc., 148 (2020), 4091-4106.  doi: 10.1090/proc/15046.

[9]

C. BelakD. Hoffmann and F. T. Seifried, Continuous-time mean field games with finite state space and common noise, Appl. Math. Optim., 84 (2021), 3173-3216.  doi: 10.1007/s00245-020-09743-7.

[10]

P. Cardaliaguet, F. Delarue, J.-M. Lasry and P.-L. Lions, The Master Equation and the Convergence Problem in Mean Field Games, Annals of Mathematics Studies, 201. Princeton University Press, Princeton, 2019. doi: 10.2307/j.ctvckq7qf.

[11]

A. Cecchin and M. Fischer, Probabilistic approach to finite state mean field games, Appl. Math. Opt., 81 (2020), 253-300.  doi: 10.1007/s00245-018-9488-7.

[12]

A. Cecchin and G. Pelino, Convergence, fluctuations and large deviations for finite state mean field games via the master equation, Stochastic Process. Appl., 129 (2019), 4510-4555.  doi: 10.1016/j.spa.2018.12.002.

[13]

C. Dellacherie and P.-A. Meyer, Probabilities and Potential, North-Holland Mathematics Studies, 29. North-Holland Publishing Co., Amsterdam-New York, 1978.

[14]

L. C. Evans and R. F. Gariepy, Measure Theory and Fine Properties of Functions, Textbooks in Mathematics, CRC Press, Boca Raton, FL, 2015.

[15]

W. H. Fleming and H. M. Soner, Controlled Markov Processes and Viscosity Solutions, Second edition, Stochastic Modelling and Applied Probability, 25. Springer, New York, 2006.

[16]

D. A. GomesJ. Mohr and R. R. Souza, Continuous time finite state mean field games, Appl. Math. Opt., 68 (2013), 99-143.  doi: 10.1007/s00245-013-9202-8.

[17]

X. Guo and O. Hernández-Lerma, Continuous-Time Markov Decision Processes, Stochastic Modelling and Applied Probability, 62. Springer-Verlag, Berlin, 2009. doi: 10.1007/978-3-642-02547-1.

[18]

M. HuangP. E. Caines and R. P. Malhamé, Large-population cost-coupled LQG problems with nonuniform agents: Individual-mass behavior and decentralized Nash equilibria, IEEE Trans. Automat. Control, 52 (2007), 1560-1571.  doi: 10.1109/TAC.2007.904450.

[19]

M. HuangR. P. Malhamé and P. E. Caines, Large population stochastic dynamic games: Closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle, Commun. Inf. Syst., 6 (2006), 221-251.  doi: 10.4310/CIS.2006.v6.n3.a5.

[20]

S. Katsikas and V. Kolokoltsov, Evolutionary, mean-field and pressure-resistance game modelling of networks security, J. Dyn. Games, 6 (2019), 315-335.  doi: 10.3934/jdg.2019021.

[21]

V. Kolokoltsov and W. Yang, Inspection games in a mean field setting, (2015), arXiv: 1507.08339.

[22]

V. N. Kolokoltsov, Nonlinear Markov Process and Kinetic Equations, Cambridge Tracts in Mathematics, 182. Cambridge University Press, Cambridge, 2010. doi: 10.1017/CBO9780511760303.

[23]

V. N. Kolokoltsov and A. Bensoussan, Mean-field-game model for botnet defense in cyber-security, Appl. Math. Opt., 74 (2016), 669-692.  doi: 10.1007/s00245-016-9389-6.

[24]

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

[25]

V. N. Kolokoltsov and O. A. Malafeyev, Many Agent Games in Socio-Economic Systems: Corruption, Inspection, Coalition Building, Network Growth, Security, Springer Series in Operations Research and Financial Engineering, Springer, Cham, 2019. doi: 10.1007/978-3-030-12371-0.

[26]

V. N. Kolokoltsov and O. A. Malafeyev, Corruption and botnet defense: A mean field game approach, Int. J. Game Theory, 47 (2018), 977-999.  doi: 10.1007/s00182-018-0614-1.

[27]

J.-M. Lasry and P.-L. Lions, Jeux à champ moyen. Ⅰ. Le cas stationnaire, C. R. Math. Acad. Sci. Paris, 343 (2006), 619-625.  doi: 10.1016/j.crma.2006.09.019.

[28]

J.-M. Lasry and P.-L. Lions, Jeux à champ moyen. Ⅱ. Horizon fini et contrôle optimal, C. R. Math. Acad. Sci. Paris, 343 (2006), 679-684.  doi: 10.1016/j.crma.2006.09.018.

[29]

P.-L. Lions, College de France Course on Mean-Field Games, College de France, 2007–2011.

show all references

References:
[1]

C. D. Aliprantis and K. C. Border, Infinite Dimensional Analysis: A Hitchhiker's Guide, Springer, Berlin, Heidelberg, 2006.

[2]

L. Ambrosio, N. Gigli and G. Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures, Lectures in Mathematics ETH Zurich, Birkhäuser, Basel, 2005.

[3]

J.-P. Aubin, Viability Theory, Systems & Control: Foundations & Applications. Birkhäuser Boston, Inc., Boston, MA, 1991.

[4]

R. BasnaA. Hilbert and V. N. Kolokoltsov, An approximate Nash equilibrium for pure jump Markov games of mean-field-type on continuous state space, Stochastics, 89 (2017), 967-993.  doi: 10.1080/17442508.2017.1297812.

[5]

E. BayraktarA. CecchinA. Cohen and F. Delarue, Finite state mean field games with Wright-Fisher common noise, J. Math. Pures Appl., 147 (2021), 98-162.  doi: 10.1016/j.matpur.2021.01.003.

[6]

E. BayraktarA. CecchinA. Cohen and F. Delarue, Finite state mean field games with Wright-Fisher common noise as limits of ${N}$-player weighted games, Math. Oper. Res., 147 (2021), 98-162.  doi: 10.1016/j.matpur.2021.01.003.

[7]

E. Bayraktar and A. Cohen, Analysis of a finite state many player game using its master equation, SIAM J. Control. Optim., 56 (2018), 3538-3568.  doi: 10.1137/17M113887X.

[8]

E. Bayraktar and X. Zhang, On non-uniqueness in mean field games, Proc. Amer. Math. Soc., 148 (2020), 4091-4106.  doi: 10.1090/proc/15046.

[9]

C. BelakD. Hoffmann and F. T. Seifried, Continuous-time mean field games with finite state space and common noise, Appl. Math. Optim., 84 (2021), 3173-3216.  doi: 10.1007/s00245-020-09743-7.

[10]

P. Cardaliaguet, F. Delarue, J.-M. Lasry and P.-L. Lions, The Master Equation and the Convergence Problem in Mean Field Games, Annals of Mathematics Studies, 201. Princeton University Press, Princeton, 2019. doi: 10.2307/j.ctvckq7qf.

[11]

A. Cecchin and M. Fischer, Probabilistic approach to finite state mean field games, Appl. Math. Opt., 81 (2020), 253-300.  doi: 10.1007/s00245-018-9488-7.

[12]

A. Cecchin and G. Pelino, Convergence, fluctuations and large deviations for finite state mean field games via the master equation, Stochastic Process. Appl., 129 (2019), 4510-4555.  doi: 10.1016/j.spa.2018.12.002.

[13]

C. Dellacherie and P.-A. Meyer, Probabilities and Potential, North-Holland Mathematics Studies, 29. North-Holland Publishing Co., Amsterdam-New York, 1978.

[14]

L. C. Evans and R. F. Gariepy, Measure Theory and Fine Properties of Functions, Textbooks in Mathematics, CRC Press, Boca Raton, FL, 2015.

[15]

W. H. Fleming and H. M. Soner, Controlled Markov Processes and Viscosity Solutions, Second edition, Stochastic Modelling and Applied Probability, 25. Springer, New York, 2006.

[16]

D. A. GomesJ. Mohr and R. R. Souza, Continuous time finite state mean field games, Appl. Math. Opt., 68 (2013), 99-143.  doi: 10.1007/s00245-013-9202-8.

[17]

X. Guo and O. Hernández-Lerma, Continuous-Time Markov Decision Processes, Stochastic Modelling and Applied Probability, 62. Springer-Verlag, Berlin, 2009. doi: 10.1007/978-3-642-02547-1.

[18]

M. HuangP. E. Caines and R. P. Malhamé, Large-population cost-coupled LQG problems with nonuniform agents: Individual-mass behavior and decentralized Nash equilibria, IEEE Trans. Automat. Control, 52 (2007), 1560-1571.  doi: 10.1109/TAC.2007.904450.

[19]

M. HuangR. P. Malhamé and P. E. Caines, Large population stochastic dynamic games: Closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle, Commun. Inf. Syst., 6 (2006), 221-251.  doi: 10.4310/CIS.2006.v6.n3.a5.

[20]

S. Katsikas and V. Kolokoltsov, Evolutionary, mean-field and pressure-resistance game modelling of networks security, J. Dyn. Games, 6 (2019), 315-335.  doi: 10.3934/jdg.2019021.

[21]

V. Kolokoltsov and W. Yang, Inspection games in a mean field setting, (2015), arXiv: 1507.08339.

[22]

V. N. Kolokoltsov, Nonlinear Markov Process and Kinetic Equations, Cambridge Tracts in Mathematics, 182. Cambridge University Press, Cambridge, 2010. doi: 10.1017/CBO9780511760303.

[23]

V. N. Kolokoltsov and A. Bensoussan, Mean-field-game model for botnet defense in cyber-security, Appl. Math. Opt., 74 (2016), 669-692.  doi: 10.1007/s00245-016-9389-6.

[24]

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

[25]

V. N. Kolokoltsov and O. A. Malafeyev, Many Agent Games in Socio-Economic Systems: Corruption, Inspection, Coalition Building, Network Growth, Security, Springer Series in Operations Research and Financial Engineering, Springer, Cham, 2019. doi: 10.1007/978-3-030-12371-0.

[26]

V. N. Kolokoltsov and O. A. Malafeyev, Corruption and botnet defense: A mean field game approach, Int. J. Game Theory, 47 (2018), 977-999.  doi: 10.1007/s00182-018-0614-1.

[27]

J.-M. Lasry and P.-L. Lions, Jeux à champ moyen. Ⅰ. Le cas stationnaire, C. R. Math. Acad. Sci. Paris, 343 (2006), 619-625.  doi: 10.1016/j.crma.2006.09.019.

[28]

J.-M. Lasry and P.-L. Lions, Jeux à champ moyen. Ⅱ. Horizon fini et contrôle optimal, C. R. Math. Acad. Sci. Paris, 343 (2006), 679-684.  doi: 10.1016/j.crma.2006.09.018.

[29]

P.-L. Lions, College de France Course on Mean-Field Games, College de France, 2007–2011.

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