Novel computational framework analyzes conflict effects using social fabrics and socioeconomic indicators.
Understanding the societal impacts of armed conflict remains challenging due to limitations in current models, which often apply fixed-radius buffers or composite indices that obscure critical dynamics. These approaches struggle to account for indirect effects, cumulative damage, and context-specific vulnerabilities, especially the question of why similar events produce vastly different outcomes across regions. We introduce a novel computational framework that applies principles from engineering and material science to conflict analysis. Communities are modeled as elastic plates, “social fabrics”, whose physical properties (thickness, elasticity, coupling) are derived from spatial socioeconomic indicators. Conflict events are treated as external forces that deform this fabric, enabling the simulation of how repeated shocks propagate and accumulate. Using a custom Python-based finite element analysis implementation, we demonstrate how heterogeneous data sources can be integrated into a unified, interpretable model. Validation tests confirm theoretical behaviors, while a proof-of-concept application to Nigeria (2018) reveals emergent patterns of spillover, nonlinear accumulation, and context-sensitive impacts. This framework offers a rigorous method to distinguish structural vulnerability from external shocks and provides a tool for understanding how conflict interacts with local conditions, bridging physical modeling and social science to better capture the multifaceted nature of conflict impacts.
No takes yet. Share an insight, caveat, or question.
Schwebel et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: