Curiosity-Driven Partner Selection Accelerates Convention Emergence in Language Games

In language games a speaker and a listener attempt to coordinate on a shared mapping between words and concepts. The usual approach in the literature is to study convention emergence in well-mixed populations, where pairs of agents are randomly matched to play the role of speaker and listener, respectively. This way of pairing agents can be shown to promote the emergence of a unifying common language in the long run. Despite the theoretical guarantee, convention emergence can be very slow and practically unfeasible, especially in large populations with many words and concepts. Here, we propose an alternative approach, where we allow agents to selectively partner with other agents based on their past experience. To this aim, we study Boltzmann Q-learning agents that are curiosity-driven, i.e., more likely to choose partners they misunderstood in the past. We show that this selection method significantly accelerates convention emergence when compared against a random-matching baseline and is even more pronounced in graph generation models restricting agents' communication channels. By inspecting the evolution of the agents' interaction frequency we see that partner selection induces low treewidth and high degree variance at the early stages of learning, to then converge to a regular graph, which allows for settling misunderstandings in the population at a faster rate than the traditional approaches.