Új modell: Mitől válik a politika fekete-fehérré?

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.


The team used their working model of biased assimilation to also study the polarizing effects of three popular Internet-based recommender systems. Recommender systems are widely used on the Internet to deliver personalized search results, news articles and product suggestions based on the user’s likes and dislikes.
It has been claimed that these systems contribute to polarization by creating an echo chamber effect where, for example, a left-leaning user is recommended more liberal articles and a right-leaning user is recommended more conservative ones.
“The system that recommends the most relevant item to a user turns out to be always polarizing. The other two systems, which chose a random item liked by the user and recommends an item most similar to it, were polarizing only if the user was biased to begin with. It was surprising to find that biased assimilation provides a useful framework to analyze the polarizing effects of recommender systems.” Dandekar said.