The Cynefin Framework

The Cynefin Framework

1

2

3

A Leader’s Framework for Decision Making

11/29/2017

The framework sorts the issues facing leaders into five contexts defined by the nature of the relationship between cause and effect. Four of these—simple, complicated, complex, and chaotic—require leaders to diagnose situations and to act in contextually appropriate ways. The fifth—disorder—applies when it is unclear which of the other four contexts is predominant.

Using the Cynefin framework can help executives sense which context they are in so that they can not only make better decisions but also avoid the problems that arise when their preferred management style causes them to make mistakes. In this article, we focus on the first four contexts, offering examples and suggestions about how to lead and make appropriate decisions in each of them. Since the complex domain is much more prevalent in the business world than most leaders realize—and requires different, often counterintuitive, responses—we concentrate particularly on that context. Leaders who understand that the world is often irrational and unpredictable will find the Cynefin framework particularly useful.

https://hbr.org/2007/11/a-leaders-framework-for-decision-making

Typology or Taxonomy?

10/22/2011

There is no clear agreement on the definitions of typology and taxidermy, well there are in biology but as we move into other fields it gets harder. I’d also make it clear that I am pushing the boundaries of my knowledge here so if Patrick Lambe (or someone else who does understand this) wants to correct me on my use that is fine. To be honest I am less concerned about the labels than I am about the distinctions. Knowledge management is closer to policy formation than it is to biology so I am going to use the definitions in Kevin Smith’s September 2002 paper in the Policy Studies Journal Typologies, taxonomies, and the benefits of policy classification as my starting point.

• In a typology the dimensions represent concepts, they do not necessarily exist in physical reality (although they can). As such typologies generate heuristics which are more adaptive under changing circumstances. On the downside the concepts can be arbitrary may not be exhaustive and can easily be subject to clashes of interpretation.

• A taxonomy on the other hand classifies things based on clear empirical characteristics and will have rules that allow determination of location. They have clear boundaries mostly determined by cluster analysis allow rapid decision making . On the downside, once a taxonomy is established if something does not fit, it will be made to fit as the taxonomy itself creates a filtering mechanism through which we filter observable characteristics.

A Cynefin postscript

Now Cynefin is a bit of a hybrid. It is a conceptual framework so at that level its a typology, but the dimensions are based on natural science so there is an empirical aspect but its not from any form of cluster analysis.

I’ve always said that the approach I and colleagues have developed over the years is a form of naturalising sense-making; the naturalising is a philosophical reference to rooting theory in the natural sciences. Now this is useful but it can lead to some confusions if people seize on it and make it a two by two matrix.

It can, and is used as such but properly used it is a lot more. Some quick points here and I plan some more posts on aspects of this:

Cynefin has five domains; simple, complicated, complex, chaotic and disordered.

The disordered domain recognizes the essential inauthenticity to any typology of human experience so disorder is the state of not knowing which of the domains you are in (and you may be in several). Disorder is frequently left out which is a pity.

The chaos domain is always a transitionary state and Cynefin is as much about movements between domains (dynamics) as it is about the domains themselves. Now anyone with the right science background gets that quickly, order comes for free to quote John Holland (I think but it might be one of the other Santa Fe founders and I have limited internet access so can’t check that).

Its very important to use the constrain based definition of the domain as that allows you to understand how to move between them.

• If there are no constrains its chaotic. If the constraints are severe enough to make agent behaviour predictable then its ordered,
• if the nature of those constrains are self-evident then its Simple,
• if they require analysis then they are Complicated.
• If the constrains and agents co-evolve then its Complex.

That means that changing the constraints can change the nature of the system and consequently the nature of situational assessment and decision making.

Simple is next to Chaotic as complacency, or inappropriate application of constrains which can lead to catastrophic failure (again movement)
Ideally the framework is social constructed (the boundaries emerge from the data), ideally with the four points method I described in my paper on the history of Cynefin.

However it can also be used as a categorisation framework (boundaries precede the data) and the HBR article largely focuses on this aspect as it was an approach more familiar to the readers.

http://cognitive-edge.com/blog/typology-or-taxonomy/

Bounded diversity: A typology of foresight

10/21/2011

The dimensions of the typology (which is very different from a taxonomy) were:

• Distributed cognition (the term goes back to Hutchins 1995 book Cognition in the Wild) is a key aspect of any complex adaptive system and is to my mind a much more accurate term that Wisdom of the Crowds and less prone to practice error. Language remains the master of man to quote Heidegger and what language we use is very important to practice.

• For the second dimension I took the probable-possible-plausible dimension which I had used on day one and which is key to the idea of resilience. If you are not familiar then I posted the material as The mantra of resilience in March of this year and there is also a recorded presentation. There is a further elaboration in the form of a table in my post Meaning and the content heresy.

http://cognitive-edge.com/blog/a-typology-of-foresight/

Cynefin: a suggestive framework for problem solving

7/22/2014

####Obvious

• Obvious is the domain of best practices. (Note: This category was referred to as “Simple” in prior versions of the model.)
• Characteristics: Problems are well understood and solutions are evident to anyone with a reasonable amount of common sense. One example is server patching – this is a well-documented procedure that can be scripted and regulated. Minimal expertise is required.
• Approach: Problems here are well known. The correct approach is to sense the situation, categorize it into a known bucket, and apply a well-known, potentially templated, solution.

Complicated

• Complicated is the domain of good practices.
• Characteristics: It’s categorized by a list of known unknowns – in other words, you likely know the questions you need to answer and how to obtain the answers. Expert knowledge is required to assess the situation and determine the appropriate course of action. You could reasonably spend enough time in analysis to identify known risk and devise a relatively accurate plan.For example, the development of a new CMS system would be a complicated problem. Competitors exist, the market is well understood and there is significant precedent. Expertise is required, but the work is evolutionary, not revolutionary.
• Approach: Sense the problem and analyze. Apply expert knowledge to assess the situation and determine a course of action. Execute the plan.

Complex

• Complex is the domain of emergent solutions.
• Characteristics: It is categorized by unknown unknowns – you don’t even know the right questions to ask. Experimentation is required to even understand the problem, let alone begin to solve it. The final solution is only apparent once discovered. No matter how much time you spend in analysis, it’s not possible to identify the risks or accurately predict the solution or effort required to solve the problem.For instance, the emergence of Twitter® was a complex situation in 2007. When developed, Twitter defined a new market. And it wasn’t possible to predict what features would stick and which would miss the mark. Expertise was needed, but it required experimentation to discover the nature of the product.
• Approach: Develop and experiment to gather more knowledge. Execute and evaluate. As you gather more knowledge, determine your next steps. Repeat as necessary, with the goal of moving your problem into the complicated domain.

Chaotic

• Chaotic is the domain of novel solutions.
• Characteristics: As the name implies, this is where things get a bit crazy. The immediate priority is containment. Production defects could be an example of a chaotic situation. Your initial focus is to correct the problem and contain the issue. Your initial solution might not be the best, but as long as it works, it’s good enough. Once you’ve stopped the bleeding, you can take a breath and determine a real solution.
• Approach: Triage and stop the bleeding. When you’ve gotten a measure of control, assess the situation and determine next steps. Take action to remediate or move your problem to another domain.

Disorder

• Disorder is the space in the middle.
• Characteristics: If you don’t know where you are, then you are in disorder. Priority One is to move to a known domain in one of the four corners.
• Approach: Gather more info on what you know or identify what you don’t know. Get enough info to move to a more defined domain.

It’s worth noting that Cynefin emphasizes the cliff between Obvious and Chaotic. There is an ever-present danger that even well-controlled systems might slip into chaos due to apathy, negligence, or any number of other reasons. It’s important to remain vigilant to keep things in control.

So what use is the Cynefin model? What keeps it from being just another in a long line of “next big things?” I’ve found two general cases where it’s proven its worth:

https://www.godaddy.com/garage/cynefin-suggestive-framework-problem-solving/