Last week, I wrote about getting better at the wrong thing. This week I want to go a little further. I want to look at what rule keeps making the wrong thing seem right.
This comes from my continued study of Chris Argyris's work on single-loop and double-loop learning.
Single-loop learning asks: "How do I fix this problem?"
Double-loop learning asks: "What assumption or rule keeps creating this problem?"
When I looked at my overflowing notes inbox, the first reaction was: "how to improve my tags, filters, or review process?" This is single-loop learning.
But with double-loop learning, I start asking : Why did I keep capturing so much in the first place? Maybe the rule underneath my system is: "If something looks useful, save it"
If that rule stays untouched, the system keeps recreating the same problem.
With that in mind, here's a simple audit I did on my system:
What keeps happening?
What did I do in response?
What assumption made that action seem reasonable?
Is that assumption still useful?
What rule should I test instead?
This simple audit helps me identify the hidden problem behind the system.
The key lesson I learned this week:
Do not just review what went wrong. Review what made the wrong action seem right.
Until next time, keep learning.
Gav.
