Isakov Alexander, Holcomb Amelia, Glowacki Luke, Christakis Nicholas A
Department of Physics, Harvard University, Cambridge, Massachusetts, United States of America.
Department of Mathematics, Yale University, New Haven, Connecticut, United States of America.
PLoS One. 2016 Feb 1;11(2):e0148314. doi: 10.1371/journal.pone.0148314. eCollection 2016.
There is significant heterogeneity within and between populations in their propensity to engage in conflict. Most research has neglected the role of within-group effects in social networks in contributing to between-group violence and focused instead on the precursors and consequences of violence, or on the role of between-group ties. Here, we explore the role of individual variation and of network structure within a population in promoting and inhibiting group violence towards other populations. Motivated by ethnographic observations of collective behavior in a small-scale society, we describe a model with differentiated roles for individuals embedded within friendship networks. Using a simple model based on voting-like dynamics, we explore several strategies for influencing group-level behavior. When we consider changing population level attitude changes and introducing control nodes separately, we find that a particularly effective control strategy relies on exploiting network degree. We also suggest refinements to our model such as tracking fine-grained information spread dynamics that can lead to further enrichment in using evolutionary game theory models for sociological phenomena.
不同人群在参与冲突的倾向方面存在显著的异质性。大多数研究忽视了社会网络中群体内部效应在导致群体间暴力方面所起的作用，而是将重点放在了暴力的先兆和后果上，或者群体间联系的作用上。在这里，我们探讨了群体内部个体差异和网络结构在促进和抑制针对其他群体的群体暴力方面所起的作用。基于对一个小规模社会中集体行为的人种学观察，我们描述了一个在友谊网络中个体角色不同的模型。使用一个基于类似投票动态的简单模型，我们探索了几种影响群体层面行为的策略。当我们分别考虑改变群体层面的态度变化和引入控制节点时，我们发现一种特别有效的控制策略依赖于利用网络度。我们还建议对我们的模型进行改进，比如追踪能够导致在使用进化博弈论模型研究社会学现象时进一步丰富内容的细粒度信息传播动态。
