Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Dispersion games: general definitions and some specific learning results
Eighteenth national conference on Artificial intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Towards Understanding the Role of Learning Models in the Dynamics of the Minority Game
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Learning to compete, compromise, and cooperate in repeated general-sum games
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning against opponents with bounded memory
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the use of memory and resources in minority games
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Memetic networks: analyzing the effects of network properties in multi-agent performance
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of available resources. When requirements and constraints are dynamic, learning agents may be needed to allow for adaptation. One way of accomplishing learning is to observe past outcomes, using such information to improve future decisions. When limits in agents' memory or observation capabilities are assumed, one must decide on how large should the observation window be. We investigate how this decision influences both agents' and system's performance in the context of a special class of distributed resource allocation problems, namely dispersion games. We show by numerical experiments over a specific dispersion game (the Minority Game) that in such scenario an agent's performance is non-monotonically correlated with her memory size when all other agents are kept unchanged. We then provide an information-theoretic explanation for the observed behaviors, showing that a downward causation effect takes place.