The role of clustering on the emergence of efficient social conventions

Multiagent models of the emergence of social conventions have demonstrated that global conventions can arise from local coordination processes without a central authority. We further develop and extend previous work to address how and under what conditions emerging conventions are also socially efficient, i.e. better for all agents than potential alternative conventions. We show with computational experiments that the clustering coefficient of the networks within which agents interact is an important condition for efficiency. We also develop an analytical approximation of the simulation model that sheds some light to the original model behavior. Finally, we combine two decision mechanisms, local optimization and imitation, to study the competition between efficient and attractive actions. Our main result is that in clustered networks a society converges to an efficient convention and is stable against invasion of sub-optimal conventions under a much larger range of conditions than in a non-clustered network. On the contrary, in non-clustered networks the convention finally established heavily depends on its initial support