Global world (dis-)order? Analyzing the dynamic evolution of the micro-structure of multipolarism by means of an unsupervised neural network approach

We make use of an advanced artificial neural network (Auto-CM) to model the structure of the current world order as a data-driven reconstruction of the implicit relationships between countries and of their time evolution, as derived from a database of publicly observable socioeconomic and political variables. Building on previous research, we analyze 93 variables derived from dozens of key indicators for 128 countries and trace their evolution along a period of eight years, 2007–2014. We find evidence of an increasing structural instability that seems to signal a transition toward a new, as yet undetermined, multipolar world order.