The simple rules for working with complexity
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:
- 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.