Worldwide bilateral geopolitical interactions network inferred from national disciplinary profiles

TL;DR

This paper models worldwide bilateral geopolitical interactions using a network inferred from countries' disciplinary profiles, applying statistical physics, clustering, and Bayesian inference to reveal genuine correlations and characterize international relations.

Contribution

It introduces a novel approach combining disciplinary profiles, random matrix theory, and a Heisenberg model to infer and analyze global geopolitical interactions.

Findings

Empirical cross-correlation matrices contain genuine information beyond noise.

The inferred network interactions reveal meaningful geopolitical relationships.

The approach enables deeper historical and geopolitical analysis.

Abstract

A disciplinary profile of a country is defined as the versor whose components are the numbers of articles produced in a given discipline divided the overall production of the country. Starting from the Essential Science Indicators (ESI) schema of classification of subject area, we obtained the yearly disciplinary profiles of a worldwide graph, where on each node sits a country, in the two time intervals [1980-1988] and [1992-2017], the fall of the Berlin Wall being the watershed. We analyse the empirical pairwise cross-correlation matrices of the time series of disciplinary profiles. The contrast with random matrix theory proves that, beyond measurement noise, the empirical cross-correlation matrices bring genuine information. Arising from the Shannon theorem as the least-structured model consistent with the measured pairwise correlations, the stationary probability distribution of disciplinary profiles can be described by a Boltzmann distribution related to a generalized $n_d$-dimensional Heisenberg model. The set of network interactions of the Heisenberg model have been inferred and to it they have been applied two clusterization methods, hierarchical clustering and principal component analysis. On a geopolitical plane this allow to obtain a characterization of the worldwide bilateral interactions based on physical modeling. A simple geopolitical analysis reveals the consistency of the results obtained and gives a boost to deeper historical analysis. In order to obtain the optimal set of pairwise interactions we used a Pseudo-Likelihood approach. We analytically computed the Pseudo-Likelihood and its gradient. The analytical computations deserve interest in whatever inference Bayesian problem involving a $n_d$-dimensional Heisenberg model.