Constrained Bimatrix Games with Fuzzy Goals and its Application in Nuclear Negotiations

Document Type : Research Article

Authors

1 Researcher, Institute for the Study of War, Command and Staff University, Tehran, I.R. Iran army.

2 University of Birjand, Birjand,

3 Birjand University of Technology, Birjand,

Abstract

Solving constrained bimatrix games in the fuzzy environment is the aim of this research. This class of two-person nonzero-sum games is considered with finite strategies and fuzzy goals when some additional linear constraints are imposed on the strategies. We consider constrained two-person nonzero sum games with single and multiple payoffs. It is shown that an equilibrium solution of single-objective case can be characterized by solving a quadratic programming problem with linear constraints. Some mathematical program ming problems are also introduced to obtain the equilibrium points in multi objective case with crisp and fuzzy constraints. Finally, a political application of such games is presented which is about nuclear negotiations between two countries.

Keywords


1. Bector, C. R. and Chandra, S. Fuzzy mathematical programming and fuzzy matrix games, Springer-Verlag, Berlin Heidelberg, 2005.
2. Bellman, R. E. and Zadeh, L. A. Decision making in a fuzzy environment, Management Sci. 17 (1970/71), 141–164.
3. Bigdeli, H. and Hassanpour, H. A satisfactory strategy of multiobjective two person matrix games with fuzzy payoffs, Iranian Journal of Fuzzy Systems, 13 (2016), 17–33.
4. Bigdeli, H., Hassanpour, H., and Tayyebi, J. The optimistic and pessimistic solutions of single and ultiobjective matrix games with fuzzy payoffs and analysis of some of military problems. Passive Defence Sci. & Tech. 2 (2017), 133–145.
5. Borm, P. E. M., Tijs, S. H., and van den Aarssen, J. C. M. Pareto equilibria in multiobjective games, XIII Symposium on Operations Research (Paderborn, 1988), 303–312, Methods Oper. Res., 60, Hain, Frankfurt am Main, 1990.
6. Charnes, A. Constrained games and linear programming, Proc. Nat. Acad. Sci. USA 39 (1953), 639–641.
7. Corley, H. W. Games with vector payoffs, J. Optim. Theory Appl. 47(1985), no. 4, 491–498.
8. Dresher, M. Games of strategy theory and applications, Prentice-Hall Applied Mathematics Series Prentice-Hall, Inc., Englewood Cliffs, N.J. 1961.
9. Fahem, K. and Radjef, M. S. Properly efficient nash equilibrium in multicriteria non-cooperative games, Math. Methods Oper. Res. 82(2015), no. 2, 175–193.
10. Kawaguchi, T. and Maruyama, Y. A note on minmax (maxmin) programming, Management Science, 22 (1976), 670–676.
11. Li, D. F. and Cheng, C. Fuzzy multiobjective programming methods for fuzzy constrained matrix games with fuzzy numbers, Internat. J. Uncertain. Fuzziness Knowledge-Based Systems 10 (2002), no. 4, 385–400.
12. Li, D. F. and Hong, F. X. Alfa-cut based linear programming methodology for constrained matrix games with payoffs of trapezoidal fuzzy numbers, Fuzzy Optim. Decis. Mak. 12 (2013), no. 2, 191–213.
13. Li, D. F. and Hong, F. X. Solving constrained matrix games with payoffs of triangular fuzzy numbers, Comput. Math. Appl. 64 (2012), no. 4, 432–446.
14. Li, D. and Sun, X. Nonlinear integer programming, Springer Science, New York, 2006.
15. Mangasarian, O. L. and Stone, H. Two-person nonzero-sum games and quadratic programming, J. Math. Anal. Appl. 9 (1964), 348–355.
16. Neumann, J. V. and Morgenstern, O. Theory of games and economic behavior, Wiley, New York, 1944,
17. Nishizaki, I. and Sakawa, M. Fuzzy and multiobjective games for conflict resolution, Springer-Verlag, Berlin Heidelberg, 2001.
18. Nishizaki, I. and Sakawa, M. Equilibrium solutions for multiobjective bimatrix games incorporating fuzzy goals, J. Optim. Theory Appl. 86 (1995), no. 2, 433–458.
19. Owen, G. Game theory, Academic Press, San Diego, Second Edition 1982, Third Edition, 1995.
20. Parthasarathy, T. and Raghavan, T. E. S. Some topics in two-Person games, No.22 American Elsevier Publishing Company, New York, 1971.
21. Sakawa, M. Fuzzy sets and interactive multiobjective optimization, Plenum press, New York, 1993.
22. Steuer, R. Multiple criteria optimization:theory, computation, and application, John Wiley & Sons, New York, 1986.
23. Wierzbicki, A. P. Multiple criteria solutions in noncooperative gametheory part III: theoretical foundations. Kyoto Institute of Economic Research Discussion paper, No. 288, 1990.
24. Zadeh, L. A. The concept of a linguistic variable and its application to approximate reasoning, Inform. Sci. 8 (1975), 199–249.
25. http:// www.lindo.com
26. http:// www.gams.com
CAPTCHA Image