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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.4 México Oct./Dec. 2014

http://dx.doi.org/10.13053/CyS-18-4-1987 

Simulation of Baseball Gaming by Cooperation and Non-Cooperation Strategies

 

Matías Alvarado1, Arturo Yee Rendón1, and Germinal Cocho2

 

1 Computer Sciences Department, Center for Research and Advance Studies, Mexico City, Mexico. matias@cs.cinvestav.mx, ayee@computacion.cs.cinvestav.mx

2 Complex Sciences Department, Physics Institute, UNAM, Mexico City, Mexico. cocho@fisica.unam.mx

 

Article received on 24/06/2014.
Accepted on 07/11/2014.

 

Abstract

Baseball is a top strategic collective game that challenges the team manager's decision-making. A classic Nash equilibrium applies for non-cooperative games, while a Kantian equilibrium applies for cooperative ones. We use both Nash equilibrium (NE) and Kantian equilibrium (KE), separate or in combination, for the team selection of strategies during a baseball match: as soon as the selection of strategies by NE or KE carries a team to stay match loosing, a change to KE or NE is introduced. From this variation of selection of strategies the team that is losing tends to close or overcome the score with respect to the team with advantage according to the results from computer simulations. Hence, combining Nash selfish-gaming strategies with Kantian collaboration-gaming strategies, a baseball team performance is strengthened.

Keywords: Baseball strategies, cooperation and non-cooperation, Nash equilibrium, Kantian equilibrium, computer simulations.

 

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References

1. Bjarkman, P.C. (2004). Diamonds around the Globe: The Encyclopedia of International Baseball. Greenwood Press, USA.         [ Links ]

2. Bradbury, J.C. (2008). The Baseball Economist: The Real Game Exposed. The Penguin Group, New York, NY.         [ Links ]

3. Williams, T. (2005). Winning Strategies for Offense and Defense. Baseball's Best.         [ Links ]

4. Alaways, L.W. & Hubbard, M. (2001). Experimental determination of baseball spin and lift. Journal of Sport Science, Vol. 19, No. 5, pp. 349-358.         [ Links ]

5. Jinji, T., Sakurai, S., & Hirano, Y. (2011). Factors determining the spin axis of a pitched fastball in baseball. Journal of Sport Science, Vol. 29, No. 7, pp. 761 -767.         [ Links ]

6. MacMahon, C. & Starkes, J.L. (2008). Contextual influences on baseball ball-strike decisions in umpires, players, and controls. Journal of Sport Science, Vol. 26, No. 7, pp. 751-760.         [ Links ]

7. Alvarado, M. & Rendón, A.Y. (2012). Nash equilibrium for collective strategic reasoning. Expert System with Application, Vol. 39, No.15, pp. 12014-12025.         [ Links ]

8. Bell, D., Raiffa, H., & Tversky, A. (1999). Decision Making. Cambridge University Press.         [ Links ]

9. Dutta, P.K. (1999). Strategies and games: theory and practice. Massachusetts Institute of Tecnology, USA.         [ Links ]

10. McMillan, J. (1996). Games strategies and managers. Oxford University Press.         [ Links ]

11. Redondo, F.V. (2000). Economy and Games. Antoni Bosh, Spain.         [ Links ]

12. Nisan, N., Roughgarden, T., Tardos, E., & Vazirani, V.V. (2007). Algorithmic Game Theory. Cambridge University Press.         [ Links ]

13. Chalkiadakis, G., Elkind, E., & Wooldridge, M. (2011). Computational Aspects of Cooperative Game Theory. Morgan and Claypool.         [ Links ]

14. AL-Mutairi, M.S. (2010). Two-decision-maker cooperative games with fuzzy preferences. Proc. of the Industrial Engineering and Engineering Management (IEEM), pp. 6-12.         [ Links ]

15. Mintzberg, H., Quinn, J.B. (1991). The Strategy Process: Concepts, Context, Cases. Pretice Hall, Englewood Cliffs, NJ.         [ Links ]

16. McGrew, A.G. & Wilson, M.J. (1982). Decision making: approaches and analysis. Manchester University Press        [ Links ]

17. Nash, J. (1951). Non-Cooperative Games. The annals of Mathematics, Vol. 54, pp. 286-295.         [ Links ]

18. Roemer, J.E. (2010). Kantian Equilibrium. The Scandinavia Journal of Economics, Vol. 112, No. 1, p. 24.         [ Links ]

19. Roemer, J.E. (2012). Kantian Optimization, Social Ethos, and Pareto Efficiency. Cowles Foundation Discussion Paper, No. 1854.         [ Links ]

20. Coello, C.A.C., Lamount, G.B., & Veldhuizen, D.A.V. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Springer.         [ Links ]

21. Lindsey, G.R. (1963). An Investigation of Strategies in Baseball. Journal of Operation. Research. Vol. 11, pp. 477-501.         [ Links ]

22. Neumann, J. & Morgenstern, O. (1944). Theory of Game and Economic Behavior. New Jersey: Princenton University Press.         [ Links ]

23. Aumann, R.J. & Hart, S. (1992). Handbook of game theory with economic applications. Elsevier.         [ Links ]

24. Osborne, M.J. (2004). An introduction to game theory. Oxford University Press.         [ Links ]

25. Ross, D. (2008). Game Theory. The Stanford University.         [ Links ]

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