Abstract
Evolutionary Game Theory is the study of strategic interactions among large populations of agents who base their decisions on simple, myopic rules. A major goal of the theory is to determine broad classes of decision procedures which both provide plausible descriptions of selfish behaviour and include appealing forms of aggregate behaviour. For example, properties such as the correlation between strategies’ growth rates and payoffs, the connection between stationary states and the well-known game theoretic notion of Nash equilibria, as well as global guarantees of convergence to equilibrium, are widely studied in the literature.
Our paper can be seen as a quick introduction to Evolutionary Game Theory, together with a new research result and a discussion of many algorithmic and complexity open problems in the area. In particular, we discuss some algorithmic and complexity aspects of the theory, which we prefer to view more as Game Theoretic Aspects of Evolution rather than as Evolutionary Game Theory, since the term “evolution” actually refers to strategic adaptation of individuals’ behaviour through a dynamic process and not the traditional evolution of populations. We consider this dynamic process as a self-organization procedure which, under certain conditions, leads to some kind of stability and assures robustness against invasion. In particular, we concentrate on the notion of the Evolutionary Stable Strategies (ESS). We demonstrate their qualitative difference from Nash Equilibria by showing that symmetric 2-person games with random payoffs have on average exponentially less ESS than Nash Equilibria. We conclude this article with some interesting areas of future research concerning the synergy of Evolutionary Game Theory and Algorithms.
This work was partially supported by the EU within the Future and Emerging Technologies Programme under contract IST-2001-331116 (FLAGS) and within the 6th Framework Programme under contract 001907 (DELIS).
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References
Cressman, R.: Evolutionary dynamics and extensive form games. MIT Press, Cambridge (2003)
Etessami, K., Lochbihler, A.: The computational complexity of evolutionary stable strategies. Technical Report 55, Electronic Colloquium on Computational Complexity (ECCC) (2004), ISSN 1433-8092
Fischer, S., Vöcking, B.: On the evolution of selfish routing. In: Albers, S., Radzik, T. (eds.) ESA 2004. LNCS, vol. 3221, pp. 323–334. Springer, Heidelberg (2004)
Haigh, J.: Game theory and evolution. Advances in Applied Probability 7, 8–11 (1975)
Hofbauer, J., Sigmund, K.: Evolutionary game dynamics. Bulletin of the American Mathematical Society 40(4), 479–519 (2003)
Koutsoupias, E., Papadimitriou, C.: Worst-case equilibria. In: Meinel, C., Tison, S. (eds.) STACS 1999. LNCS, vol. 1563, pp. 404–413. Springer, Heidelberg (1999)
Larry, S.: Evolutionary Games and Equilibrium Selection (Economic Learning and Social Evolution). The MIT Press, Cambridge (1997)
Matsui, A., Gilboa, I.: Social stability and equilibrium. Econometrica 59, 859–867 (1991)
McLennan, A., Berg, J.: The asymptotic expected number of nash equilibria of two player normal form games. In: Working document, Department of Economics. Univ. of Minnesota (2004)
Nash, J.F.: Noncooperative games. Annals of Mathematics 54, 289–295 (1951)
Rosenthal, R.W.: A class of games possessing pure-strategy nash equilibria. International Journal of Game Theory 2, 65–67 (1973)
Taylor, P.D., Jonker, L.: Evolutionary stable strategies and game dynamics. Mathematical Biosciences 40, 145–156 (1978)
Weibull, J.W.: Evolutionary Game Theory. The MIT Press, Cambridge (1995)
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Kontogiannis, S., Spirakis, P. (2005). Evolutionary Games: An Algorithmic View . In: Babaoglu, O., et al. Self-star Properties in Complex Information Systems. SELF-STAR 2004. Lecture Notes in Computer Science, vol 3460. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428589_7
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DOI: https://doi.org/10.1007/11428589_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26009-7
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