There are lots of resources that claim to help us learn from our mistakes and become more efficient. A video from the Farnam Street YouTube channel may help in this area:
The video is about 13 minutes long and has chapters, so it's not too long, and worth checking out.
The basic idea behind this technique is that you want to escape the limitations of 'single loop learning' where we're essentially stuck in a loop and making the same mistakes over and over. When we expand into double loop learning, we're more apt to collect data, get feedback, gauge our performance, and reflect, all of which lead us to reach our learning goals.
The video includes a really good way to illustrate the process: A simple thermostat is stuck in a single-loop because it has one goal that it tries to reach (the set temperature) without consideration for any factors. On the other hand, an ideal thermostat would be intelligent enough to become more efficient over time, taking into consideration things like current humidity, optimum temperature, and so forth. Thus, a double loop.
The same applies to our own efficiency when learning, as they discuss in the video, which goes on to provide some real-world examples where this worked. I'll leave you with a quote that I liked from the video:
"Many of us are so focused on solving problems as they arise, that we don't take the time to reflect on them after we've dealt with them — and this omission dramatically limits our ability to learn from the experiences."
Good food for thought.
Now on to this week's hand-picked productivity links!
