Towards a Sustainable Disruptive Growth Model: Integrating Foresight, Wild Cards and Weak Signals Analysis

Abstract

This paper introduces epistemological and methodological innovations for analyzing non-linear dynamics in sustainability systems, such as deforestation tipping points, exponential renewable adoption, and protests driving global reform. It focuses on adaptive resilience (e.g., decentralized grids stabilizing renewables) and topological models (e.g., network analysis of deforestation or policy diffusion). The study develops metrics to assess four dimensions of evolutionary change – context, people, process, and impact – supporting adaptive resilience and stability. In environmental systems, this may involve tracking early deforestation signals before tipping points, while in economics, it could mean analyzing how small policy shifts trigger market changes. It highlights Wild Cards and Weak Signals Analysis within the Sustainable Disruptive Growth Model (SD-Growth Model), enabling the early detection of disruptions – such as AI breakthroughs or geopolitical shifts – so systems can anticipate, reorganize, and adapt effectively to shocks. The research emphasizes constraints as the key to resilience and stability amid disruptions. It integrates advanced analytical approaches to monitoring and managing simultaneous information flows, ensuring efficient responses to shocks. This model also explores AI, machine learning, and explainable AI (XAI) in labor market dynamics, where predictive algorithms can identify trends and mitigate systemic risks. By combining quantitative metrics with strategic foresight, this framework enables decision-makers to preserve stability, sustain functionality, and adapt dynamically to change.