Language Evolution and Computation Bibliography

The naming game (NG) describes the agreement dynamics of a population of N agents interacting locally in pairs leading to the emergence of a shared vocabulary. This model has its relevance in the novel fields of semiotic dynamics and specifically to opinion formation and language evolution. The application of this model ranges from wireless sensor networks as spreading algorithms, leader election algorithms to user-based social tagging systems. In this paper, we introduce the concept of overhearing (i.e., at every time step of the game, a random set of Nδ individuals are chosen from the population who overhear the transmitted word from the speaker and accordingly reshape their inventories). When δ = 0 one recovers the behavior of the original NG. As one increases δ, the population of agents reaches a faster agreement with a significantly low-memory requirement. The convergence time to reach global consensus scales as logN as δ approaches 1.

We study the dynamics of the Naming Game as an opinion formation model on social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of dominance of certain opinions over others in pursuit of faster agreement on social networks. We propose two models to incorporate dominance of the opinions. We observe that both these models lead to faster agreement among the agents on an opinion as compared to the base case reported in the literature. We perform extensive simulations on computer-generated networks as well as on a real online social network (Facebook) and in both cases the dominance based models converge significantly faster than the base case.