Linear construction of Cynefin

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List of methods / Cynefin® specific methods

Linear construction of Cynefin is one of the 4 methods for constructure of the framework and although it appraoches it from first principles it is intended as the secondary method. The method supports the construction of the Cynefin® framework from first principles, demonstrating what we mean when we say that this is a contextualization, not a categorization framework.

Note: the primary method for construction of the framework is 3-points


Identify the focal issue or prompting question for the Linear Cynefin® activity Prepare either physical or virtual workspaces with blank canvasses, a sheet of paper and sufficient sticky notes. If you have more than one group of 5 to 7 undertaking this activity then ensure they have their own space. This is particularly important if doing this in a virtual environment.


Step 1: Frame the challenge / question

Do we need to be more specific about focus or can it be open-ended, working with activities/ issues/ challenges or decision points

  • Groups may struggle with the language here: is it the outcome that is predictable EFFECT of the activity or the OUTCOME that is predictable or unpredictable?
  • See prompting questions for a discussion of the types of questions that should be considered
Step 2: Generate material

Frame the question/challenge/prompt to the group (the focus of the Linear process), asking each team to list all the activities/ issues/ challenges or decision points related to the question at hand (one per sticky)

  • It is recommended to allow the individual to reflect and to create stickies before the group discussion
  • One idea or activity per hexie
  • Hexies should contain just enough information that others will understand
Step 3: Linear placement

The group then works to place items on a linear continuum from ‘Unpredictable’ to ‘Predictable’

  • First, choose exemplars to define the endpoints of the line i.e the most and least predictable items from the material they have created
  • Then place all other items in relation to the endpoints and each other, along the predictable/unpredictable continuum. With each item to the right is more predictable, to the left less predictable.
  • More is more and a large number of hexies is fine. De-duplicate only when items are truly the same.
  • Don’t put two different items in the exact same place on the line – try to differentiate them slightly, that is do not look to cluster or build columns
Step 4: Place boundary lines

Groups place boundary lines to divide their continuum up based on the items:

  • Line 1 – separates “should be able to predict” from “can't predict” where does it go from predictable to not predictable?
  • Line 2 - on the Unpredictable side: where does it go from unpredictable but with discernable patterns (i.e. some self-organisation or constraints are evident) to no pattern, random and unstable?
  • Line 3 - on the Predictable side: where does it go from “predictability that requires expertise to see” to “predictability that most people can understand”?
  • Avoid using the domain names (chaotic, complex, clear) to describe the line placements when giving instructions. Avoid using the word ‘volatile’ to describe the far left of the line.
  • The central boundary (dividing mostly predictable from mostly unpredictable) can widen to represent a zone of confusion that contains multiple items.
Step 5: Transform into Cynefin® framework

The ends of the line are then pulled together to form the Cynefin®™ framework and boundary conditions between the domains are defined

Do's and Don'ts

  • Consensus is not the priority here - rather, sense-making through conversation is. Encourage groups to unpack the items and build a shared understanding of them, without necessarily reaching agreement.
  • Note that the dividing lines really should not end up dividing the line into even areas – this is contextualistion not categorisation, and the world is not symmetrical.

In a virtual environment

The method has been conducted in virtual settings with success over a range of platforms.



See the Shared context and sense making chapter of Cynefin Mini-book for some comments on linear contextualisation

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