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Emergence of conventions through social learning
Intervenant(s) : Dr. Stéphane AIRIAU (Université Paris-Dauphine)Societal norms or conventions help identify one of many appropriate behaviors during an interaction between agents. We consider the problem of the emergence of conventions in a society through distributed adaptation by agents from their online experiences at run time. The agents are connected to each other within a fixed network topology and interact over time only with their neighbours in the network. We study the emergence of system-wide conventions via the process of social learning where an agent learns to choose one of several available behaviors by interacting repeatedly with randomly chosen neighbors without considering the identity of the interacting agent. We experimentally show that social learning always produces conventions for random, fully connected and ring networks and study the effect of various parameters on the speed of convention emergence. We also observe and explain the formation of stable, distinct subconventions and hence the lack of emergence of a global convention when agents are connected in a scale-free network.
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