2025/10/29 by ACHIEPO, Odilon Yapo Mélaine, MENSAH, Edoeté Patrice
#Applied Mathematics #Artificial Intelligence #FOS: Mathematics #Modeling #PLS Path Modeling #Resilience Process #Resilometrics
paper · doi:10.71655/uvcibv.jpgm8-rp054
The notion of resilience reflects the ability of a system to maintain vital functions in changing environments. In social sciences, resilience is defined as the ability of a system to bounce back from a traumatic shock that has produced a breakdown in social habits. The use of the notion of resilience in social sciences poses many challenges which include the modeling of resilience process. To do so, a new modeling area, called resilometrics, is proposed to fill the gap. Hence, resilometrics will be the discipline that model resilience processes in social science. Resilometrics will essentially use information's about suffering in the modeled social systems and favors graphical interfaces and very easy interpretation of the model's results. Resilometrics is developed using computational models in Applied Mathematics and Artificial Intelligence essentially. The goal of this paper is to briefly present the discipline and describe the basic models to measure the resilience and its dimensions. These models are based on Partial Least Square Path Modeling method (PLSPM) which is a computational approach in the statistical modeling fields known as Structural Equation Modeling with Latent Variables (SEMLV).