Group mixing drives inequality in face-to-face gatherings

被引:8
|
作者
Oliveira, Marcos [1 ,2 ]
Karimi, Fariba [3 ]
Zens, Maria [1 ]
Schaible, Johann [1 ,4 ]
Genois, Mathieu [5 ]
Strohmaier, Markus [1 ,3 ,6 ]
机构
[1] Inst Social Sci, Computat Social Sci, GESIS Leibniz, Cologne, Germany
[2] Univ Exeter, Comp Sci, Exeter, Devon, England
[3] Complex Sci Hub Vienna, Vienna, Austria
[4] TH Koln Univ Appl Sci, Gummersbach, Germany
[5] Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France
[6] Univ Mannheim, Business Sch, Mannheim, Germany
关键词
SOCIAL NETWORKS;
D O I
10.1038/s42005-022-00896-1
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The emergence of inequality in social interactions can depend on a number of factors, among which the intrinsic attractiveness of individuals, but also group size the presence of pre-formed social ties. Here, the authors propose "social attractiveness" as a mechanism to account for the emergence of inequality in face-to-face social dynamics and show this reproduces real-world gathering data, predicting the existence of a critical group size for the minority group below which higher cohesion among its members leads to higher inequality. Uncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-to-face situations can enable the emergence of disparities in the visibility of social groups. These disparities translate into members of specific social groups having fewer social ties than the average (i.e., degree inequality). We characterize group degree inequality in sensor-based data sets and present a mechanism that explains these disparities as the result of group mixing and group-size imbalance. We investigate how group sizes affect this inequality, thereby uncovering the critical size and mixing conditions in which a critical minority group emerges. If a minority group is larger than this critical size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens inequality. Finally, we expose group under-representation in degree rankings due to mixing dynamics and propose a way to reduce such biases.
引用
收藏
页数:9
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