We discuss how computational data science and agent-based modeling, are shedding new light on the age-old issue of human conflict. While social science approaches focus on individual cases, the recent proliferation of empirical data and complex systems thinking has opened up a computational approach based on identifying common statistical patterns and building generative but minimal agent-based models. We discuss a reconciliation for various disparate claims and results in the literature that stand in the way of a unified description and understanding of human wars and conflicts. We also discuss the unified interpretation of the origin of these power-law deviations in terms of dynamical processes. These findings show that a unified computational science framework can be used to understand and quantitatively describe collective human conflict.

我们讨论了计算数据科学和基于代理的建模如何为人类冲突这个古老的问题提供新的思路。尽管社会科学方法侧重于个别案例，但最近经验数据和复杂系统思想的泛滥开辟了一种基于识别常见统计模式并建立生成式但基于主体的模型的计算方法。我们在文献中讨论了对各种不同主张和结果的调和，这妨碍了对人类战争和冲突的统一描述和理解。我们还将讨论关于动力过程偏差的起源的统一解释。这些发现表明，可以使用统一的计算科学框架来理解和定量描述集体人类冲突。