Game-Theoretical and Evolutionary Simulation: A Toolbox for Complex Enterprise Problems

  • Yves CaseauEmail author


Complex systems resist analysis and require experimenting or simulation. Many enterprise settings, for instance with cases of competition in an open market or “co-opetition” with partners, are complex and difficult to analyze, especially to accurately figure the behaviors of other companies. This paper describes an approach towards modeling a system of actors which is well suited to enterprise strategic simulation. This approach is based upon game theory and machine learning, applied to the behavior of a set of competing actors. Our intent is not to use simulation as forecasting - which is out of reach precisely because of the complexity of these problems - but rather as a tool to develop skills through what is commonly referred as “serious games”, in the tradition of military wargames. Our approach, dubbed GTES, is built upon the combination of three techniques: Monte-Carlo sampling, searching for equilibriums from game theory, and local search meta-heuristics for machine learning. We illustrate this approach with “Systemic Simulation of Smart Grids”, as well as a few examples drawn for the mobile telecommunication industry.


simulation machine learning game theory evolutionary game theory evolutionary algorithms Monte-Carlo dynamic games serious games 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Caseau, Y.: GTES: une méthode de simulation par jeux et apprentissage pour l’analyse des systèmes d’acteurs. RAIRO Operations Research 43(4) (2009)Google Scholar
  2. 2.
    Weibull, J.: Evolutionary Game Theory. The MIT Press (1995)Google Scholar
  3. 3.
    Gibbons, R.: Game Theory for Applied Economists. Princeton University Press (1992)Google Scholar
  4. 4.
    Korn, G.A.: Advanced Dynamic-system Simulation: Model-replication Techniques and Monte Carlo Simulation. Wiley Interscience (2007)Google Scholar
  5. 5.
    Slantchev, B.: Game Theory: Repeated Games. University of California – San Diego (2004),
  6. 6.
    Fudenberg, D., Levine, D.: The Theory of Learning in Games. The MIT Press (1998)Google Scholar
  7. 7.
    Aarts, E., Lenstra, J.K.: Local Search in Combinatorial Optimisation. Wiley (1993)Google Scholar
  8. 8.
    Jørgensen, S., Quincampoix, M., Vincent, T. (eds.): Advances in Dynamic Game Theory: Numerical Methods, Algorithms, and Applications to Ecology and Economics. Annals of the International Society of Dynamic Games. Birkhauser, Boston (2007)Google Scholar
  9. 9.
    Nissan, N., Roughgarden, T., et al.: Algorithmic Game Theory. Cambridge University Press (2007)Google Scholar
  10. 10.
    Duncan Luce, R., Raiffa, H.: Games and Decisions – Introduction and Critical Survey. Dover Publications, New York (1957)zbMATHGoogle Scholar
  11. 11.
    Alkemade, F., La Poutré, H., Amman, H.: Robust Evolutionary Algorithm Design for Socio-economic Simulation. Computational Economics (28), 355–380 (2006)Google Scholar
  12. 12.
    Siarry, P., Dréo, J., et al.: Métaheuristiques pour l’optimisation difficile. Eyrolles, Paris (2003)Google Scholar
  13. 13.
    Horst, R., Tuy, H.: Global Optimization: Deterministic Approaches. Springer, Berlin (2010)Google Scholar
  14. 14.
    Milano, M.: Constraint and Integer Programming. Kluwer Academic Publishers (2004)Google Scholar
  15. 15.
    Axelrod, R.: The Complexity of Cooperation- Agent-Based Models of Competitions and Cooperation. Princeton University Press (1997)Google Scholar
  16. 16.
    Nelson, R., Winter, S.: An Evolutionary Theory of Economic Change. Belknap Harvard (1982)Google Scholar
  17. 17.
    Ferber, J.: Multi-agent systems: An introduction do distributed artificial intelligence. Addison-Wesley (1999)Google Scholar
  18. 18.
    Kandori, M., Mailath, G., Rob, R.: Learning, Mutation and Long Run Equilibria in Games. Econometrica 61(1), 29–56 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Blum, A., Blum, M., Kearns, M., Sandholm, T., Hajiaghayi, M.T.: Machine Learning, Game Theory and Mechanism Design for a Networked World. NSF proposal (2006),
  20. 20.
    Forrester, J.: Principles of Systems. System Dynamics Series. Pegasus Communications, Waltham (1971)Google Scholar
  21. 21.
    Sterman, J.: Business Dynamics – System Thinking and Modeling for a Complex World. McGraw Hill (2001)Google Scholar
  22. 22.
    Lucas, S.M., Kendall, G.: Evolutionary Computation and Games. IEEE Computational Intelligence Magazine (February 2006)Google Scholar
  23. 23.
    Bowling, M., Veloso, M.: An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning. Carnegie Mellon University, CMU-CS-00-165 (2000)Google Scholar
  24. 24.
    Caseau, Y., Silverstein, G., Laburthe, F.: Learning Hybrid Algorithms for Vehicle Routing Problems. TPLP 1(6), 779–806 (2001)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Bouygues TelecomIssy-les-MoulineauxFrance

Personalised recommendations