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How To Slow Down Time? A non-technical lesson from an intense software school.
“Very quickly Siffre’s memory deteriorated… Since there was nobody to talk to, and not much to do, there was nothing novel to impress upon his memory.”
Of all delectable islands the Neverland is the snuggest and most compact, not large and sprawly, you know, with tedious distances between one adventure and another, but nicely crammed. When you play at it by day with the chairs and table-cloth, it is not in the least alarming, but in the two minutes before you go to sleep it becomes very real. That is why there are night-lights.
I’ve been using the Cynefin framework for many years now. For me, I think I’ve internalized it through practice and it becomes second nature to not only talk about and teach from it but to use the way it was intended to be used: to help make decisions.
Today Dave Snowden posts a very useful set of guidelines for working with complexity that are captured in the framework. This list is useful for us to tuck away as it provides very clear guideposts for moving around the complexity domain:
The essence:
- In any situation, what can we change?
- Out of the things we can change, where can we monitor the impact of that change?
- Out of the things we can change, where we can monitor the impact, where could we amplify success and/or dampen or recover from failure.
What we should avoid:
- Retrospective coherence, we should learn from the past but not assume that what happened will repeat, or that it had linear causality
- Premature convergence, coming to quickly to a single solution (although coming quickly to parallel safe to fail experiments is a good thing) rather than keeping our options open
- Pattern entrainment, assume that the patterns of past success will entrain the inevitability of future failure unless you actively manage to prevent it.
Then the three basic heuristics of complexity management:
- Work with finely grained objects
- Distribute cognition/sense-making within networks
- Disintermediation, putting decision makers in contact with raw data without interpretative layers
Admittedly this is technical language, but I appreciate the clarity.