Experimentation and learning
The habit of running small, fast tests to validate assumptions and adapt direction.
🎯 What it enables​
It reduces the cost of uncertainty and accelerates insight.
🧠How to build it​
- Design experiments with clear hypotheses and success criteria.
- Share results openly, including failed experiments.
- Turn learnings into updated maps and decisions quickly.
📰 Related blog posts​
- Double-Loop Learning Keeps Wardley Maps Honest — Reinforces feedback loops that refresh assumptions.
- NK Model: Rugged Wardley Maps — Explains why experimentation is needed in rugged search landscapes.
- Anti-Fragile Chaos Engineering — Treats disruption as a structured learning exercise.