A Universal Model for War and Peace

Human flourishing is often severely limited by violent conflict, yet the root causes of war and peace are intricate and difficult to quantify. In recent years, the increasing availability of data has led to models which attempt to explain the complexities of warfare from various perspectives [1]–[5]. However, a universal spatial model that can predict areas of war and peace on a global scale remains absent. Here, we take up the oft-cited hypothesis that land trade routes play a key role in determining both the strategic importance and the vulnerability to conflict of cities and regions around the world [6], [7]. Using population data from cities, we infer a global network by means of the ‘gravity law’. We find that two standard network measures, degree and betweenness, together predict with high accuracy whether a given region will be at risk of suffering conflict. We use these results to propose a metric called strategic centrality, and find that it accounts for over 80% of the variance in number of attacks among the world’s main population zones. It is also a good predictor of the distance to a conflict zone, and can be used to provide an estimate of the conflict risk for a given region. Our results demonstrate that despite the complexities underlying human violence, a pattern exists at the global scale such that it is possible to make predictions based on simple network measures, significantly outperforming predictions based on established socioeconomic or geopolitical factors. These findings suggest a particular connection between conflict and trade routes which should be explored in more depth. We also anticipate that the model can be used to guide developing regions in the construction of new cities and major transport links. Human conflict in one guise or another has shaped our world, enthralled historians for millennia, and continues to represent an existential threat to humanity. The conditions which lead to war or peace are multi-dimensional, and seem so specific and interwoven that it is often considered simplistic or naive to search for general patterns which might provide real predictive power. The mathematical analysis of conflict goes back as far as the 1940s [8] (and more recently in [2]), where it was found that both the cumulative and non-cumulative intensity of conflict followed power-law distributions and exhibited fractal properties. However, the conflict data for earlier eras are sparse, inaccurate, and often aggregated across a whole war (i.e. the death-tolls were in the 10s to 100s of thousands). In recent years, the greater availability of data has