The Rule Behind the Problem

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 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:

1. What keeps happening?
2. What did I do in response?
3. What assumption made that action seem reasonable?
4. Is that assumption still useful?
5. 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.