Complex systems appear across domains that seem unrelated only at the level of surface phenomena. Economics, finance, energy and machine learning all confront the same structural question: how do constraints and local rules produce outcomes that cannot be inferred from any single component?
Models are valuable when they reconstruct mechanisms rather than merely fitting historical correlations. Mechanism-oriented thinking forces explicit assumptions, identifiable failure modes and testable implications.
For institutional decision-making, this perspective implies building systems where feedback, uncertainty and multi-scale effects remain visible rather than collapsed into a single score.