期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：6
DOI：10.1073/pnas.2121103119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Social stability is often associated with triangular interactions between people. Various possible social triangles appear in peculiar ratios. The triangles “The friend of my friend is my friend” and “The enemy of my friend is my enemy” are strongly overrepresented, which plays an important role for social balance. A standard explanation for these characteristic triangle fractions is that people consider triadic information before forming social relations. This assumption often contradicts everyday experience. We propose an explanation of the observed overrepresentations without individuals having to consider triangles. A society where individuals minimize their social stress self-organizes toward the empirically observed triangular structures. We demonstrate this with data from a society of computer game players, where triangle formation can be directly observed.
The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific (“balanced”) triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.
