Attitudinal polarization on social networks: A cognitive architecture perspective

Polarization of attitudes is an important, and often troubling or disruptive, effect of interest in many fields. We seek to shed some light on how such polarization arises by applying cognitive architectures to the problem. We created a novel embedding of individual cognitive agents, using ACT-R’s declarative memory model, into social networks, simulated them communicating over time, and observed the evolution of the agents’ attitudes, both collectively and individually. The primary measures we use are both Shannon entropies, of the distribution of attitudes in the final configuration of the whole social network, and of the distributions of memory traces in the individual agents as the simulation progresses. Simulations were run over ten different network topologies, using three different distributions of initial attitudes, and five different values of the agents’ memory decay parameter. These simulations demonstrated that polarization can be understood from a social and cognitive perspective simultaneously, each providing insights into the system’s behavior.