The relationship between predictive and explanatory power has long been debated in the philosophy of science, artificial intelligence, and empirical research. Traditionally viewed as distinct or even opposing goals, predictive power refers to a model's ability to accurately forecast outcomes, while explanatory power relates to how well a model provides an understanding of the underlying mechanisms of a phenomenon. This article explores the interplay between these two concepts and argues for a more integrated perspective, suggesting that the dichotomy between them is not only artificial but also counterproductive in advancing science and decision-making.

Qualitative approaches are crucial for expanding our understanding of sustainability by shifting focus from one-dimensional progress trajectories to dynamic, interconnected systems. It highlights the need for diverse expertise, as interdisciplinary collaboration is key to addressing the complex nature of sustainability challenges. However, the main limitation lies not in expertise, but in the qualitative frameworks that fail to capture system-level interactions. The integration of qualitative insights with quantitative methods is essential for developing informed, effective, and sustainable solutions.