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.

Neil Johnson is a professor of physics at GW and heads up a new initiative in Complexity and Data Science which combines cross-disciplinary fundamental research with data science to attack complex real-world problems. He is a core member of GW's new Knight Foundation-funded Institute for Data, Democracy and Politics. He is a Fellow of the American Physical Society (APS) and is the recipient of the 2018 Burton Award from the APS. He received his BA/MA from St. John's College, Cambridge, University of Cambridge and his Ph.D. as a Kennedy Scholar from Harvard University. He was a Research Fellow at the University of Cambridge, and later a Professor of Physics at the University of Oxford until 2007, having joined the faculty in 1992. Following a period as Professor of Physics at the University of Miami, he was appointed Professor of Physics at George Washington University in 2018. He presented the Royal Institution Christmas Lectures “Arrows of Time” on BBC TV in 1999. He co-founded and co-directed CABDyN (Complex Agent-Based Dynamical Systems) which is Oxford University's interdisciplinary research center in Complexity Science, and an Oxford University interdisciplinary research center in financial complexity (OCCF).