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Exponential Families and Game Dynamics

Published online by Cambridge University Press:  20 November 2018

Ethan Akin*
Affiliation:
The City College of the City University of New York, New York, New York
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A symmetric game consists of a set of pure strategies indexed by {0, …, n} and a real payoff matrix (aij). When two players choose strategies i and j the payoffs are aij and aji to the i-player and j-player respectively. In classical game theory of Von Neumann and Morgenstern [16] the payoffs are measured in units of utility, i.e., desirability, or in units of some desirable good, e.g. money. The problem of game theory is that of a rational player who seeks to choose a strategy or mixture of strategies which will maximize his return. In evolutionary game theory of Maynard Smith and Price [13] we look at large populations of game players. Each player's opponents are selected randomly from the population, and no information about the opponent is available to the player. For each one the choice of strategy is a fixed inherited characteristic.

Type
Research Article
Copyright
Copyright © Canadian Mathematical Society 1982

References

1. Akin, E., The geometry of population genetics, LNBM 31 (Springer-Verlag, 1979).Google Scholar
2. Akin, E., Domination or equilibrium, Math. Biosciences 50 (1980), 239250.Google Scholar
3. Dawid, A. P., discussion in Efron, op. cit.Google Scholar
4. Efron, B., Defining the curvature of a statistical problem ﹛with applications to second order efficiency), Ann. Stat. 3 (1975), 11891242.Google Scholar
5. Halmos, P., Measure theory (D. Van Nostrand Company, Inc., 1950).Google Scholar
6. W. G. S., Hines, Strategy stability in complex populations, J. Appl. Prob. 17 (1980), 600610.Google Scholar
7. W. G. S., Hines, Three characterizations of population strategy stability, J. Appl. Prob. 17 (1980), 333340.Google Scholar
8. Hofbauer, J., Schuster, P. and Sigmund, K., A note on evolutionary stable strategies and game dynamics, J. Theo. Biol. 81 (1979), 609612.Google Scholar
9. Kullback, S., Information theory and statistics (John Wiley and Sons, Inc., 1959).Google Scholar
10. Lang, S., Differential manifolds (Addison-Wesley Publishing Company, Inc., 1972).Google Scholar
11. Lehmann, E. L., Testing statistical hypotheses (John Wiley and Sons, Inc., 1959).Google Scholar
12. Marsden, J. E. and McCracken, M., The Hopf bifurcation and its applications (Springer-Verlag, 1976).Google Scholar
13. J., Maynard Smith and Price, G. R., The logic of animal conflicts, Nature 246 (1973), 1518.Google Scholar
14. Shahshahani, S., A new mathematical framework for the study of linkage and selection, Memoirs MAS 211 (1979).Google Scholar
15. Taylor, P. D. and Jonker, L. B., Evolutionarily stable strategies and game dynamics, Math. Biosciences 40 (1978), 145156.Google Scholar
16. J., Von Neumann and Morgenstern, O., Theory of games and economic behavior (John Wiley and Sons, Inc., 1944).Google Scholar
17. Zeeman, E. C., Population dynamics from game theory, Proc. Int. Conf. Global Theory of Dynamical Systems, Northwestern, Evanston, LNM 819 (Springer-Verlag, 1979).Google Scholar
18. Zeeman, E. C., Dynamics of the evolution of animal conflicts (to appear).Google Scholar