Towards understanding the role of learning models in the dynamics of the minority game

We report experiments in a boundedly rational evolutionary game, namely the minority game, where agents apply a very simple learning algorithm to discard bad strategies and create new ones. The results show that even such simplified learning model presents qualitative differences from the behavior of the traditional game, where strategies are fixed and cannot be modified or discarded. We show that this result is qualitatively similar to other, more complex, learning approaches. Also, we study how the learning parameters of our model affect the dynamics of the game and we provide experimental evidence of a high dependence between the behavior of the system and the way fitness is attributed as new strategies enter the game.