  Social network structure is one of the key determinants of human language
evolution. Previous work has shown that the network of social interactions
shapes decentralized learning in human groups, leading to the emergence of
different kinds of communicative conventions. We examined the effects of social
network organization on the properties of communication systems emerging in
decentralized, multi-agent reinforcement learning communities. We found that
the global connectivity of a social network drives the convergence of
populations on shared and symmetric communication systems, preventing the
agents from forming many local "dialects". Moreover, the agent's degree is
inversely related to the consistency of its use of communicative conventions.
These results show the importance of the basic properties of social network
structure on reinforcement communication learning and suggest a new
interpretation of findings on human convergence on word conventions.
