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Dynamic Generalized Berge-Zhukovskii Equilibrium

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Book cover Recent Advances in Soft Computing (MENDEL 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 837))

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Abstract

The Generalized Berge-Zhukovskii equilibrium extends the Berge-Zhukovskii equilibrium problem by introducing constraints over the set of strategy profiles. The new equilibrium is computed in a dynamic environment by using an evolutionary dynamic equilibrium tracking algorithm. Numerical experiments for the generalized Cournot duopoly illustrate the capability of the approach.

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References

  1. Arrow, K.J., Debreu, G.: Existence of an equilibrium for a competitive economy. Econometrica 22, 265–290 (1954)

    Article  MathSciNet  Google Scholar 

  2. Cardell, J.B., Hitt, C.C., Hogan, W.W.: Market power and strategic interaction in electricity networks. Resour. Energy Econ. 19(1–2), 109–137 (1997)

    Article  Google Scholar 

  3. Cournot, A.: Recherches sur les Principes Mathematique de la Theorie des Richesses. Hachette, Paris (1838)

    MATH  Google Scholar 

  4. Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)

    Article  Google Scholar 

  5. Debreu, G.: A social equilibrium existence theorem. Proc. Natl. Acad. Sci. USA 38(10), 886–893 (1952)

    Article  MathSciNet  Google Scholar 

  6. Gaskó, N., Dumitrescu, D., Lung, R.I.: Evolutionary detection of Berge and Nash equilibria. In: Pelta, D., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2011). Studies in Computational Intelligence, vol. 387, pp. 149–158. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Haurie, A., Krawczyk, J.: An introduction to dynamic games (2001)

    Google Scholar 

  8. Lasaulce, S., Tembine, H.: Game Theory and Learning for Wireless Networks: Fundamentals and Applications, 1st edn. Academic Press, Cambridge (2011)

    Google Scholar 

  9. Lung, R.I., Dumitrescu, D.: Computing Nash equilibria by means of evolutionary computation. Int. J. Comput. Commun. Control 3, 364–368 (2008)

    Google Scholar 

  10. Nash, J.: Non-cooperative games. Ann. Math. 54(2), 286–295 (1951)

    Article  MathSciNet  Google Scholar 

  11. Van Long, N.: A Survey of Dynamic Games in Economics: Volume 1 of World Scientific Books. World Scientific Publishing Co. Pte. Ltd. (2010)

    Google Scholar 

  12. Roos, C.F.: A mathematical theory of competition. Am. J. Math. 47(3), 163–175 (1925)

    Article  MathSciNet  Google Scholar 

  13. Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)

    Article  Google Scholar 

  14. Suciu, M., Gaskó, N., Lung, R.I., Dumitrescu, D.: Nash equilibria detection for discrete-time generalized cournot dynamic oligopolies. In: Terrazas, G., Otero, F., Masegosa, A. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol. 512, pp. 343–354. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  15. Suciu, M., Lung, R.I., Gaskó, N., Dumitrescu, D.: Differential evolution for discrete-time large dynamic games (2013)

    Google Scholar 

  16. Van Veldhuizen, D.A., Lamont, G.B.: On measuring multiobjective evolutionary algorithm performance. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 204–211 (2000)

    Google Scholar 

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Acknowledgments

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-2560.

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Authors and Affiliations

  1. Babeş-Bolyai University, Cluj-Napoca, Romania

    Noémi Gaskó, Mihai Suciu & Rodica Ioana Lung

Authors
  1. Noémi Gaskó
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  2. Mihai Suciu
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  3. Rodica Ioana Lung
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Corresponding author

Correspondence to Noémi Gaskó .

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Editors and Affiliations

  1. Department of Applied Computer Science, Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Gaskó, N., Suciu, M., Lung, R.I. (2019). Dynamic Generalized Berge-Zhukovskii Equilibrium. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_28

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