Applied Learning and Short-Vector Direction

"You are not as dumb as you look," he rejoined. "You need to work with short vectors so that the whole team learns as it goes and makes course corrections as a consequence. When you get to calibrate your machinery as you learn, you make smaller 'aiming' errors. You get to execute better on each succeeding iteration, given what you have learned, so you deviate less and less from your intended path. And each time you combine what you've learned with the discovery that the target has moved, you are better able to anticipate future movements. You might even get smart enough to 'lead the target' a bit.

"So a short vector approach means a shorter total path for two reasons. One, you spend less time on bad paths early on (short vector principle). And, two, your later vectors are closer to optimum (learning principle). Although there might be lucky exceptions, statistically these are the two features responsible for shorter total path length when averaged over many projects."

"Was there a systematic technique for getting maximum learning out of the early iterations?" I wondered aloud.