Cynefin exercise about Agile software development - 3 Categorization

This post is the third of the series co-authored with Michael Podvinec where we write about the exercise and some of the insights gained:
1 - Intro
2 - Sense-Making
3 - Categorization
4 - Our exercise
5 - Key learnings

Michael is a molecular biologist by training, and is convinced that agile methods have a place in all domains where we're commonly dealing with complexity and uncertainty, such as biomedical research. 
He really promises he will soon publish more regularly on topics like these on his blog. Until then, he suggests you to follow @mpodvinec on twitter.








Cynefin as categorisation framework
In the previous post we have described an application of the four-points method, where the categorization emerges from the input data. It is, however, perfectly legitimate to turn the process around, and have the framework precede the data. After the four domains (simple, complicated, complex, chaotic) are explained, the available data is placed into the four distinct domains. Applied in this fashion, the method allows participants to focus on the differences between the characteristics of each domain and the appropriate decision models and approaches for each domain.
 
When using the framework this way, participants need to be aware of the risk that data becomes reinterpreted to better fit into the given framework boundaries. So if you have the opportunity to use the Cynefin framework with the help of and experienced and trained facilitator, prefer the the "four-points"
Sense-Making method to the categorisation approach.
 
Here is a presentation of the Cynefin framework:




And if you already know the Cynefin framework you probably would like to know the latest model of the complex sub-domain: http://cognitive-edge.com/blog/entry/5792/complexity-sub-domain-framework/

These are a lot of new concepts, so let's try to look at some of them again, from a different angle.

There are some additional characteristics with which we can describe the four domains that a problem can fall into. They are helpful when we're unsure where to fit a particular problem.

  1. Is the problem ordered or unordered?

    If the problem is ordered, causality can be observed. Such a problem is confined to either the simple or the complicated domain. A problem in the simple domain is characterized by obvious cause-effect relationships ("If I press this button, that light will go on"), whereas a complicated problem is still governed by causal relationships, but they may only be discernible by subject matter experts ("If I flick that switch, it looks like sigmoidal curve-fitting, rather than least-squares is applied").

    If the problem is unordered, there are no stable, long-term causal relationships. Still, the observed behavior may be understood using agent-based modeling.


  2. What types of rules govern the problem's system?

    Similar to the thought process above, we can ask ourselves what rules are governing the system?
    If the system is governed by fixed, immutable rules (i.e. the problem could investigated using mathematical simulations), then people don't influence and are not part of the problem. In this case, the domain can be simple or complicated.

    We could say for the mathematical model that the experimentor is "outside of the system", in contrast to the case below, where the experimentator explores and experiments "inside the system", influencing it, a bit like in quantum mechanics.

    If the system is governed by heuristics, then the problem needs to be investigated using exploration and experimentation. Typically the actions of people influence the system in this case.


When the system is unordered and governed by heuristic it's possible that it is in the Cynefin chaotic or complex domain.



When the problem involves people and belongs to an unordered system, it is also possible to look at two further characteristics of the problem: The level of certainty and level of agreement/consensus to double-check if the problem is really in the chaotic or complex domain. In fact, the Stacey matrix (Strategic management and organisational dynamics: the challenge of complexity. 3rd ed., Stacey, 2002, Harlow: Prentice Hall) describes what amounts to the Cynefin domains using 2 dimensions, certainties and agreement/consensus:

  • when the team members are certain about how to solve a problem and they are in agreement about it, that is the simple domain
  • when there is some doubt about the approach, or some disagreement, that is the complicated domain
  • where there are no certainties and no agreement, that's the chaotic domain
  • between the complicated and the chaotic, the complex domain is found.



For the purpose of looking at complexity concepts from a different angle and check our understanding, this initial version of the Stacy Matrix is fit for purpose. For a more modern and complete view about the matrix  read also here.

Print | posted @ Saturday, November 17, 2012 3:57 PM

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