This is part of my 5 Minute Concepts series, which is designed to help you understand fundamental concepts about subjects like learning, memory and competition in the shortest time possible. Each episode is available in video format on my YouTube channel and audio via my podcast. If you prefer to read, the transcript is below.
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Let’s talk about double- and single-loop learning.
These are ideas from cybernetics and they have to do with the way that a system optimizes its behavior.
With single-loop learning, you get a system that is more or less following predetermined rules. So you have an algorithm and you follow that algorithm, single-loop learning just means that you’re optimizing that particular algorithm — trying to get maximum results.
A more realistic example would be something along the lines of you are told what to do in a day and you just do your very best to do those things every day.
So the analogy would be a job, say you’re a janitor and you’re told “go sweep the floor,” and single-loop learning would just mean you do your very best to sweep the floor and every single day you’re coming up with new improvements to get better at sweeping the floor.
What’s more important is double-loop learning, and double-loop learning involves questioning and optimizing the system itself.
If you are the janitor and you’re sweeping every day, and you’re getting really good at it, double-loop learning would say what’s a better way to clean the floor? Maybe it’s not sweeping. Maybe it’s vacuuming. Maybe it’s mopping.
Maybe it’s something along those lines that just doesn’t exist within that particular algorithm. It means you’re stepping outside of the system.
And sometimes you’re even breaking the system altogether. This is an important concept in and of itself.
But in general double-loop learning is the hardest learning to engage in because it’s very easy for somebody to tell you what to do.
It’s very easy to follow the rules and optimize around those rules. What’s hard is to come up with new rules all together to step outside the system and ask yourself “is this even a system I want to be part of? Is this even a game that I want to be playing?”
That’s not easy to do. The world is filled with people who are excellent at single-loop learning, and in fact, we come with a lot of hardware and software, so to speak, that allows us to be just out of the box very good at single-loop learning.
Whereas double-loop learning requires real thought and effort and, more importantly, risk-taking. That involves trade-offs that mean saying “I’m going to try something that may fail.”
So if you want to take your learning to a new place, if you feel like you’re plateauing for whatever reason, then chances are good that you’ve just been engaging in way too much single-loop learning.
You need to start stepping into the double-loop paradigm and do something different. Ask yourself if you’re even trying to optimize the correct thing because, the fact is, if you’re optimizing around the wrong system, it doesn’t matter how good you are at optimizing.
And that’s really the conundrum that we all face on a day-to-day basis, we’re trying to figure out should I continue optimizing this system I’m in right now, or should I come up with a new system altogether?
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