A fractal model for forecasting global crises: from local events to global consequences

Abstract

This article proposes a fractal model as an innovative tool for forecasting global crises in international relations. It is based on the concept of self-similarity the recurrence of patterns at different scales and allows for the analysis of local events (sanctions, cyberattacks, migration waves) as markers of broader global processes. Instead of complex mathematical calculations, the model utilizes qualitative analysis and historical analogies. The fractal approach is demonstrated through examples such as the US-China trade war, the Colonial Pipeline cyberattack, and the migration crisis in the EU. In each case, local triggers exhibited fractal characteristics, enabling analysts to identify potential threats at early stages. The model underscores the nonlinear nature of the international system, where minor events can have large-scale consequences. The model's uniqueness lies in its universality and accessibility: it can be applied in international relations, diplomacy, security, environmental studies, and social processes. This article is aimed at diplomats, researchers, and analysts in the field of international relations who seek to understand the dynamics of crises in a complex, interdependent world. Further research could integrate the model with social network analysis tools to enhance prediction accuracy.