In product development we often use iterations to increase the quality and robustness of our products. Why does this work?
To begin, let me clarify my terminology. By “iteration” I mean covering the same ground twice. I do not use the term iteration to mean breaking a larger task into several smaller pieces; I call that batch size reduction. I must mention this because many people in the agile software community to use the term iteration to refer to breaking a project into a number of smaller pieces. It is a superb technique, but I consider it confusing to label it iteration. Continue reading
Aristotle understood the value of thought experiments. They can be done quickly without get your hands dirty. Measurements are always precise and consistent. Results are perfectly repeatable. So, why bother with a real experiment when you can do a thought experiment?
Silly Aristotle. We are much smarter than that now. Perhaps. Management thinkers constantly quote the example of the boiled frog. Put a frog in boiling water and it will jump out. Slowly raise the temperature of the water and it will fail to notice the gradual change and boil to death. Since most of us lack both the frog and the insensitivity needed to do this experiment, like Aristotle, we accept the results of the mental experiment.
What would a real experiment reveal? The frog dies if placed in boiling water. The frog jumps out when the water is heated slowly. What happens if the frog is placed in freezing water? It will die. What happens if the temperature of the water drops slowly, such as from summer to winter? The frog will activate genes that produce metabolic enzymes that function at low temperatures.
As any dinosaur could tell you, slow change gives us time to react, rapid change can lead to extinction.
Fast Company: “Next Time, What Say We Boil a Consultant.” November 1995 (p. 20)
www.snopes.com: The “boiled frog” story.
Only a fool assumes that bad outcomes are always a sign of bad decisions. If you play Russian Roulette with 5 of 6 chambers full, and don’t blow your head off, you still made a bad choice.
When we get a bad outcome based on a good choice, we often incorrectly “learn” that we made a bad choice. It is even worse when we get good outcomes from bad choices — we “learn” that we made a good choice. Real learning requires that we clearly distinguish the decision from its outcome, and such learning is critical in a stochastic world.