Abstract
In a mixed strategy equilibrium players randomize between their actions according to a very specific probability distribution, even though with regard to the game payoff, they are indifferent between their actions. Currently, there is no compelling model explaining why and how agents may randomize their decisions is such a way, in real world scenarios.
We experiment with a model for two player games, where the goal of the players is to find robust strategies for which the uncertainty in the outcome of the opponent is reduced as much as possible. We show that in an evolutionary setting, the proposed model converges to mixed strategy profiles, if these exist. The results suggest that only local knowledge of the game is sufficient to attain the adaptive convergence.
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Acknowledgments
The first author acknowledges the financial support of the Sectoral Operational Program for Human Resources Development 2007-2013, co-financed by the European Social Fund, within the project POSDRU 89/1.5/S/60189 with the title “Postdoctoral Programs for Sustainable Development in a Knowledge Based Society”. The second author wishes to thank for the financial support of the national project code TE 252 financed by the Romanian Ministry of Education and Research CNCSIS-UEFISCSU and “Collegium Talentum".
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Iclănzan, D., Gaskó, N., Nagy, R., Dumitrescu, D. (2013). Multiobjective Evolution of Mixed Nash Equilibria. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_34
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DOI: https://doi.org/10.1007/978-3-642-44973-4_34
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