Understanding How Learning Affects Agreement Process in Social Networks

In this article, we study how learning affects the dynamics of opinion formation in a population of agents modeled through the so-called naming game. This agent-based model captures the essential features of the agreement dynamics by means of a memory-driven negotiation process. We analyze the impact of learning in such social agreement model through a control parameter describing the resistance toward learning. We show that there exists a critical above which the consensus time diverges. In particular, we embed this model on various interaction topologies and real-world face-to-face interaction data and, thereby, point out the important differences in the agreement dynamics that take place in a real time-varying social settings vis-a-vis different static social settings. Remarkably, in all types of topology, we observe that beyond the critical value of, the number of unique words increase manifold in the system and hence the time to consensus diverges possibly pointing to an universal aspect of language learning. In order to support the simulation results, we further develop a web-based online game - the `tagging game´ - which is a close correlate of the naming game and observe the game dynamics when human subjects are exposed to play the game. Remarkably, similar characteristic properties as that of the modified naming game is observed here even while different people from different geographical location play the game. This shows that synthetic modeling has nowadays reached the maturity to answer certain long-standing questions in cognitive science reasonably well.