Considering the dynamic character of repeated games and Markov process, this paper presented a novel dynamic decision model for symmetric repeated games. In this model, players' actions were mapped to a Markov decision process with payoffs, and the Boltzmann distribution was introduced. Our dynamic model is different from others', we used this dynamic model to study the iterated prisoner's dilemma, and the results show that this decision model can successfully be used in symmetric repeated games and has an ability of adaptive learning.
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