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Attitudinal polarization on social networks: A cognitive architecture perspective

Mark Orr
University of Virginia ~ Biocomplexity Institute
Prof. Andrea Stocco
University of Washington ~ University of Washington
Christian Lebiere
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213 USA
Don Morrison
Carnegie Mellon University, United States of America

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.



Cognitive Modeling; Long-Term Memory; Resting-state fMRI; Functional Network
Social Networks.

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Cite this as:

Orr, M., Stocco, A., Lebiere, C., & Morrison, D. (2021, July). Attitudinal polarization on social networks: A cognitive architecture perspective. Paper presented at Virtual MathPsych/ICCM 2021. Via