A general model for how attributes can reduce polarization in social groups

Abstract

Polarization makes it difficult to form positive relationships across existing groups. Decreasing polarization may improve political discourse around the world. Polarization can be modeled on a social network as structural balance, where the network is composed of groups with positive links between all individuals in the group and negative links with all others. Previous work shows that incorporating attributes of individuals usually makes structural balance, and hence polarization, harder to achieve. That work examines only a limited number and types of attributes. We present a generalized model and a simulation framework to analyze the effect of any type of attribute, including analytically as long as an expected value can be written for the type of attribute. As attributes, we consider people’s (approximately) immutable characteristics (e.g., race, wealth) and such opinions that change more slowly than relationships (e.g., political preferences). We detail and analyze five classes of attributes, recapitulating the results of previous work in this framework and extending it. While it is easier to prevent than to destabilize polarization, we find that usually the most effective at both are continuous attributes, followed by ordered attributes and, finally, binary attributes. The effectiveness of unordered attributes varies depending on the magnitude of negative impact of having differing attributes but is smaller than of continuous ones. Testing the framework on network structures containing communities revealed that destroying polarization may require introducing local tensions. This model could be used by policymakers, among others, to prevent and design effective interventions to counteract polarization.

Multidimensional attributes expose Heider balance dynamics to measurements

“…Here, we applied the Manhattan metric as the distance between agents, creating a measure similar to Gower similarity 58 . In other approaches 38 , 39 , 51 , 54 , 59 Euclidean distance, cosine similarity or other functions were also used. Let be an opinion of a student i on a topic (in our case ) and be the difference in t -th opinion between students i and j .…”

“…Therefore, both processes coevolve and influence each other 10 , 12 , 41 – 43 . Coevolution of HBT and agents’ attributes allows analysis of system polarization or consensus 28 , 43 – 51 . Usually, one-dimensional (scalar) variables define states of nodes and links.…”