Structural Resilience and Collapse in Social Representations: A Vectorized Fatigue Model (AK-VFM)

This repository provides the formal computational implementation of the Abric-Kiesler Vectorized Fatigue Model (AK-VFM), a framework designed for the quantitative analysis of social representation dynamics. The AK-VFM operationalizes the dual-system architecture of Jean-Claude Abric by integrating Kiesler’s commitment strength with non-linear socio-cognitive fatigue parameters. The core engine utilizes vectorized SIMD operations via NumPy to perform high-throughput Monte Carlo sampling across N=1000+ agent populations. Mathematical features include the derivation of the Collapse Frontier through threshold-based dissonance perception, non-linear fatigue growth scaling (P^2.1), and homeostatic recovery inhibition under effective pressure. The repository includes source code for four primary simulation protocols: baseline stability, latent exhaustion under incubation, parametric phase mapping, and social hysteresis. Datasets are provided in standardized CSV formats accompanied by JSON metadata to ensure computational reproducibility and parameter traceability within the University of Toulouse Jean Jaurès methodological tradition. Keywords: Social Representation Theory, Structural Approach, Computational Social Science, Abric, AK-VFM, Social Dynamics, Hysteresis.