“It’s the network, stupid: Study offers fresh insight into why we’re so divided”
Social perception bias might simply be an emergent property of our social networks.
JENNIFER OUELLETTE – 1/4/2020, 5:07 PM
Social perception bias is best defined as the all-too-human tendency to assume that everyone else holds the same opinions and values as we do. That bias might, for instance, lead us to over- or under-estimate the size and influence of an opposing group. It tends to be especially pronounced when it comes to contentious polarizing issues like race, gun control, abortion, or national elections.
Researchers have long attributed this and other well-known cognitive biases to innate flaws in individual human thought processes. But according to a paper published last year in Nature Human Behaviour [https://www.nature.com/articles/s41562-019-0677-4], social perception bias might best be viewed as an emergent property of our social networks. This research, in turn, could lead to effective strategies to counter that bias by diversifying social networks.
The team was surprised to find that the survey results closely matched the model’s predictions. Specifically, “People who were surrounded by people similar to them think that their group is larger than it really is, and people who have more diverse social circles think their group is smaller than it really is,” Galesic told Ars. “These biases are exaggerated with the relative size of the majority and minority groups.””
“Homophily and minority-group size explain perception biases in social networks
Eun Lee, Fariba Karimi, Claudia Wagner, Hang-Hyun Jo, Markus Strohmaier & Mirta Galesic
Nature Human Behaviour volume 3, pagesc1078–1087(2019)
People’s perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.”