What is Double-Loop Learning?

Double-loop learning describes what happens when a person or organization questions not just an action that went wrong, but the underlying assumptions, goals, and mental models that led to that action in the first place. Single-loop learning fixes the error and moves on; double-loop learning asks whether the governing variable or the goal itself needs to change.

The concept was developed by Chris Argyris and Donald Schön and is central to systems thinking because it turns recurring mistakes into a chance to rewire the thinking behind a decision rather than patch the symptom again. Teams that only ever single-loop learn tend to solve the same problem repeatedly in slightly different clothes.

Related reading: Double-loop learning: how to fix the thinking behind the problem.