An adaptive strategy for minority games
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Maximising Personal Utility Using Intelligent Strategy in Minority Game
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
On the use of memory and resources in minority games
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Individual agent's wealth in minority games
International Journal of Autonomous and Adaptive Communications Systems
An information-theoretic analysis of memory bounds in a distributed resource allocation mechanism
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the evolution of memory size in the minority game
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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This paper reports 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.