Cynefin: A suggestive framework for problem solving

All how you frame it

As noted in last week’s post, Cynefin takes the sense-making idea and creates a more defined framework for approaching problems. It doesn’t tell you how to solve a problem, but rather helps you pick an approach.

If this sounds a bit ambiguous, it’s by design. Cynefin is a framework, not a discrete tool, and requires you to have some understanding of your environment. Contrast that with a more discrete problem solving approach – say, the Lean Startup method. The Lean Startup gives you a defined process, inspired by the scientific method, to solve certain classes of problems. It is more prescriptive. Cynefin, on the other hand, guides you towards a solution without dictating how best to resolve the matter. It’s suggestive.

Cynefin categorizes problems into five domains:

Cynefin for Problem Solving


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 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 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 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 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:

Project planning

This is perhaps the most obvious application. Cynefin gives a clear framework for evaluating problems and how to proceed. Determine where you are, identify a compatible solution. I’ve met some people who had a love affair with the Lean Startup. It seemed everyone was talking about MVPs and validated learning. This is not necessarily a bad thing, but sometimes the approach doesn’t fit a particular class of problem.

The Lean Startup is tailored to problems in the complex domain. You have many unknown unknowns and need to iterate through a series of experiments just to define the problem space. In contrast, Web hosting, for example, is more of a complicated problem. While we are solving new problems, they are often extensions of existing problems and are revolutionary vs. evolutionary. Remember that step one is to figure out where you are today. Cynefin can help you do that.

Assigning resources

The right people will find a way to succeed.

While less obvious, this use of Cynefin has proven impactful in my current role. Everyone is wired differently and prefers different approaches to problem solving. I’m inclined towards complex problems. I get a kick out of being assigned to an impossible problem and being left to determine how to proceed. I actually struggle with simpler, more straightforward issues. I have to rein in my more creative impulses to go with the more straightforward and well-known solution.

Others might be the opposite. Give them a complicated problem and they knock it out of the park. They can do a deep-dive analysis, identify all the risks, and execute flawlessly. However, give them a few unknown unknowns and they get lost. In my experience, knowing your domain is more critical to choosing the team than it is to choosing the approach. The right people will find a way to succeed.

I’ve only scratched the surface of Cynefin and sense-making. If you’re interested in learning more, check out the Cognitive Edge blog. They are the geniuses behind Cynefin, actively driving its evolution and development.