Biased Assimilation, Homophily and the Dynamics of Polarization

TL;DR

This paper models opinion formation to explore how biased assimilation and homophily influence societal polarization, revealing that biased assimilation can lead to polarization, disagreement, or consensus, unlike traditional averaging models.

Contribution

It introduces a generalized opinion formation model incorporating biased assimilation, demonstrating its role in societal polarization beyond homophily effects.

Findings

Biased assimilation can cause polarization, disagreement, or consensus.

Homophily alone does not lead to polarization in the model.

Recommender algorithms may contribute to polarization through biased assimilation.

Abstract

Are we as a society getting more polarized, and if so, why? We try to answer this question through a model of opinion formation. Empirical studies have shown that homophily results in polarization. However, we show that DeGroot's well-known model of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize DeGroot's model to account for a phenomenon well-known in social psychology as biased assimilation: when presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position thereby arriving at a more extreme opinion. We show that in a simple model of homophilous networks, our biased opinion formation process results in either polarization, persistent disagreement or consensus depending on how biased individuals are. In other words, homophily alone, without biased assimilation, is not sufficient to polarize society. Quite interestingly, biased assimilation also provides insight into the following related question: do internet based recommender algorithms that show us personalized content contribute to polarization? We make a connection between biased assimilation and the polarizing effects of some random-walk based recommender algorithms that are similar in spirit to some commonly used recommender algorithms.