Johnson Neil F, Xu Chen, Zhao Zhenyuan, Ducheneaut Nicolas, Yee Nicholas, Tita George, Hui Pak Ming
Department of Physics, University of Miami, Miami, Florida 33126, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jun;79(6 Pt 2):066117. doi: 10.1103/PhysRevE.79.066117. Epub 2009 Jun 26.
Quantifying human group dynamics represents a unique challenge. Unlike animals and other biological systems, humans form groups in both real (offline) and virtual (online) spaces-from potentially dangerous street gangs populated mostly by disaffected male youths to the massive global guilds in online role-playing games for which membership currently exceeds tens of millions of people from all possible backgrounds, age groups, and genders. We have compiled and analyzed data for these two seemingly unrelated offline and online human activities and have uncovered an unexpected quantitative link between them. Although their overall dynamics differ visibly, we find that a common team-based model can accurately reproduce the quantitative features of each simply by adjusting the average tolerance level and attribute range for each population. By contrast, we find no evidence to support a version of the model based on like-seeking-like (i.e., kinship or "homophily").
量化人类群体动态是一项独特的挑战。与动物和其他生物系统不同,人类在现实(线下)和虚拟(线上)空间中形成群体——从主要由心怀不满的年轻男性组成的潜在危险街头帮派,到在线角色扮演游戏中的庞大全球公会,目前公会成员来自所有可能的背景、年龄组和性别,人数超过数千万。我们收集并分析了这两种看似不相关的线下和线上人类活动的数据,并发现了它们之间意想不到的定量联系。尽管它们的整体动态明显不同,但我们发现,一个基于团队的通用模型只需调整每个群体的平均容忍水平和属性范围,就能准确再现每个群体的定量特征。相比之下,我们没有发现证据支持基于同类相吸(即亲属关系或“同质性”)的模型版本。