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Potential Differential Games

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Abstract

This paper introduces the notion of a potential differential game (PDG), which roughly put is a noncooperative differential game to which we can associate an optimal control problem (OCP) whose solutions are Nash equilibria for the original game. If this is the case, there are two immediate consequences. Firstly, finding Nash equilibria for the game is greatly simplified, because it is a lot easier to deal with an OCP than with the original game itself. Secondly, the Nash equilibria obtained from the associated OCP are automatically “pure” (or deterministic) rather than “mixed” (or randomized). We restrict ourselves to open-loop differential games. We propose two different approaches to identify a PDG and to construct a corresponding OCP. As an application, we consider a PDG with a certain turnpike property that is obtained from results for the associated OCP. We illustrate our results with a variety of examples.

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References

  1. Amir R, Nannerup N (2006) Information structure and the tragedy of the commons in resource extraction. J Bioecon 8:147–165

    Article  Google Scholar 

  2. Charalambous CD (2016) Decentralized optimality conditions of stochastic differential decision problems via Girsanov’s measure transformation. Math Control Signals Syst 28:19. doi:10.1007/s00498-016-0168-3

  3. Clarke F (2013) Functional analysis, calculus of variations and optimal control. Springer, Berlin

    Book  MATH  Google Scholar 

  4. Dockner E, Feischtinger G, Jørgensen S (1985) Tractable classes of nonzero-sum open-loop Nash differential games: theory and examples. J Optim Theory Appl 45:179–197

    Article  MathSciNet  MATH  Google Scholar 

  5. Dockner EJ, Jørgensen S, Long NV, Sorger G (2000) Differential games in economics and management science. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  6. Dragone D, Lambertini L, Leitmann G, Palestini A (2009) Hamiltonian potential functions for differential games. IFAC Proc 42:1–8

    Article  MATH  Google Scholar 

  7. Dragone D, Lambertini L, Palestini A (2012) Static and dynamic best-response potential functions for the non-linear Cournot game. Optimization 61:1283–1293

    Article  MathSciNet  MATH  Google Scholar 

  8. Dragone D, Lambertini L, Leitmann G, Palestini A (2015) Hamiltonian potential functions for differential games. Automatica 62:134–138

    Article  MathSciNet  MATH  Google Scholar 

  9. Fonseca-Morales A, Hernández-Lerma O. A note on differential games with Pareto-optimal Nash equilibria: deterministic and stochastic models. J Dyn Games (to appear)

  10. Friedman A (2013) Differential games. Dover Publications, Inc., Mineola, New York

    Google Scholar 

  11. González-Sánchez D, Hernández-Lerma O (2013) Discrete-time stochastic control and dynamic potential games: the Euler-equation approach. Springer, Berlin

    Book  MATH  Google Scholar 

  12. González-Sánchez D, Hernández-Lerma O (2013) An inverse optimal problem in discrete-time stochastic control. J Differ Equ Appl 19:39–53

    Article  MathSciNet  MATH  Google Scholar 

  13. González-Sánchez D, Hernández-Lerma O (2014) Dynamic potential games: the discrete-time stochastic case. Dyn Games Appl 4:309–328

    Article  MathSciNet  MATH  Google Scholar 

  14. González-Sánchez D, Hernández-Lerma O (2016) A survey of static and dynamic potential games. Sci China Math 59:2075–2102

    Article  MathSciNet  MATH  Google Scholar 

  15. Gopalakrishnan R, Marden JR, Wierman A (2014) Potential games are necessary to ensure pure Nash equilibria in cost sharing games. Math Oper Res 39:1252–1296

    Article  MathSciNet  MATH  Google Scholar 

  16. Jørgensen S, Zaccour G (2012) Differential games in marketing, vol 15. Springer, Berlin

    Google Scholar 

  17. La QD, Chew YH, Soong BH (2016) Potential game theory: applications in radio resource allocation. Springer, Berlin

    MATH  Google Scholar 

  18. Long NV (2011) Dynamic games in the economics of natural resources: a survey. Dyn Games Appl 1:115–148

    Article  MathSciNet  MATH  Google Scholar 

  19. Mangasarian OL (1969) Nonlinear programming. McGraw-Hill, New York

    MATH  Google Scholar 

  20. Monderer D, Shapley LS (1996) Potential games. Game Econ Behav 14:124–143

    Article  MathSciNet  MATH  Google Scholar 

  21. Mou L, Yong J (2007) A variational formula for stochastic controls and some applications. Pure Appl Math Q 3:539–567

    Article  MathSciNet  MATH  Google Scholar 

  22. Potters JAM, Raghavan TES, Tijs SH (2009) Pure equilibrium strategies for stochastic games via potential functions. In: Advances in dynamic games and their applications. Birkhauser, Boston, pp 433–444

  23. Rosenthal RW (1973) A class of games possessing pure-strategy Nash equilibria. Int J Game Theory 2:65–67

    Article  MathSciNet  MATH  Google Scholar 

  24. Slade EM (1994) What does an oligopoly maximize? J Ind Econ 42:45–61

    Article  Google Scholar 

  25. Sundaram RK (1996) A first course in optimization theory. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  26. Tauchnitz N (2015) The Pontryagin maximum principle for nonlinear optimal control problems with infinite horizon. J Optim Theory Appl 167:27–48

    Article  MathSciNet  MATH  Google Scholar 

  27. Trélat E, Zuazua E (2015) The turnpike property in finite-dimensional nonlinear optimal control. J Differ Equ 258:81–114

    Article  MathSciNet  MATH  Google Scholar 

  28. Yong J, Zhou XY (1999) Stochastic controls: Hamiltonian systems and HJB equations, vol 43. Springer, Berlin

    Book  MATH  Google Scholar 

  29. Zazo S, Zazo J, Sánchez-Fernández M (2014) A control theoretic approach to solve a constrained uplink power dynamic game. In: 22nd European Signal processing conference on IEEE (EUSIPCO), pp 401–405

  30. Zazo S, Valcarcel S, Sánchez-Fernández M, Zazo J (2015) A new framework for solving dynamic scheduling games. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2071–2075

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Acknowledgements

Funding was provided by CONACyT (Grant No. 221291).

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Correspondence to Alejandra Fonseca-Morales.

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Fonseca-Morales, A., Hernández-Lerma, O. Potential Differential Games. Dyn Games Appl 8, 254–279 (2018). https://doi.org/10.1007/s13235-017-0218-6

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