David Catteeuw

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When facing dishonest behavior of any form, individuals may choose to punish in order to enhance future honesty from others, even if it is costly for the punishers. Such behavior can be found ubiquitously in human and animal communications, suggesting that it may play an important role in the evolution of honest signaling or reliable communication. By(More)
We study how communication systems can emerge through repeated interaction between two individuals. We apply three reinforcement learning algorithms (Roth-Erev learning , Learning Automata, and Q-Learning) to the repeated Lewis signaling game, a game theoretic model of a communication problem. Our experiments show that each of these algorithms always reach(More)
—We study honest signaling in the Philip Sidney game. Until now, researchers concentrated on verifying under what circumstances honest signaling is an evolutionarily stable strategy (ESS). Whereas the concept of ESS assumes infinite populations, we analyze here, for the first time, the more realistic scenario where populations are finite—which allows us to(More)
Lewis signaling games are a standard model to study the emergence of language. We introduce win-stay/lose-inaction, a random process that only updates behavior on success and never deviates from what was once successful, prove that it always ends up in a state of optimal communication in all Lewis signaling games, and predict the number of interactions it(More)
We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among learning agents can improve when agents use different learning parameters or even evolve their learning parameters. Better coordination leads to less(More)
We study the combination of co-evolution and individual learning in minority games (MGs). Minority games are simple models of distributed resource allocation. They are repeated conflicting interest games involving a large number of agents. In most of the literature, learning algorithms and parameters are evaluated under self-play. In this article, we want(More)
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