Clear and Complicated domains of the Cynefin Framework
Following the introduction to the Cynefin Framework in the previous article, the management landscape was divided into five domains, each governed by a simple philosophy: act according to the situation. This article embarks on a detailed exploration of the first two of these domains — the Clear and Complicated.
To unravel each domain, we’ll examine the knowledge available, the cause-and-effect relationships, and the most effective response strategies. Although this perspective simplifies the intricacies of the framework, it serves as a solid foundation for our discussion. So, let’s dive in.
Cynefin: Clear Domain (Simple; Obvious)
The Clear domain is characterized as Known-Knowns, featuring a direct Cause-and-Effect (C➡E) relationship. In simpler terms, if you fully understand a situation and the result of your action is predictable and immediate — this is the Clear domain.
Consider the game of Tic-Tac-Toe as an analogy. You know all the rules, you see the entire board, and you can easily predict the consequences of your move.
In management, a Clear situation is resolved by implementing Best Practice — an instruction detailing what to do to achieve the best result. The operating mode here could be described in these terms:
― Sense: I’m playing a game with circles and crosses on a 3x3 field.
― Categorize: This is the Tic-Tac-Toe game, and there is an optimal way to play it.
― Respond: I will make my moves according to the winning strategy.
It is crucial to emphasize that Cynefin is subjective. What appears clear to some individuals might be obscure to others. For instance, performing appendicitis surgery may be straightforward for a surgeon, but it is far from clear for the average person.
Examples of management methods applicable to the Clear domain include Lean, eXtreme-Programming (XP), Scientific Management, Process Management, and Automation.
Take XP as an example; it establishes a set of best practices such as Code Review, Unit Tests, and Continuous Integration. These practices have become a necessary discipline in software development. Their application is not a matter of choice but a clear mandate when executing software projects.
However, the complexity of certain situations surpasses this basic level of predictability, necessitating a shift from the Clear to the Complicated domain.
Cynefin: Complicated domain
The Complicated domain is characterized as Known-Unknowns, where Cause-and-Effect relationships exist but are not immediately apparent (C↬E). It’s a situation where you’re aware of the gaps in your knowledge and can fill them by learning or consulting an expert. It requires an analysis of potential options to devise a plan that will be sufficient. Contrary to the Clear domain, there are no Best Practices here, but there can be Good Practices — approaches that usually work well but not always, and therefore should be applied with caution.
Consider the game of Chess as an analogy. A step-by-step instruction manual won’t suffice to play the game due to the countless combinations of moves. Chess demands thinking several moves ahead. The Chess concept of “controlling the center” is an example of a Good Practice.
The operational mode in the Complicated domain is Sense-Analyze-Respond:
― Sense: You notice your kitchen, being 20 years old, is outdated and lacks modern amenities.
― Analyze: You research various kitchen designs, vendors, and appliances that suit your needs and budget. It would be wise to involve a kitchen designer (an Expert) for assistance.
― Respond: After finalizing your plan, you order from vendors and arrange for professional installation of your new kitchen.
Examples of management methods applicable to the Complicated domain include the Theory of Constraints (TOC), Total Quality Management (TQM), Classical Project Management (WHAT-IF scenarios), the Critical Path Method (CPM), and the Program Evaluation and Review Technique (PERT).
Take Classical Project Management as an example; it involves more than merely decomposing a project into manageable tasks. It’s about ensuring the project aligns with constraints of time, resources, and scope. Consequently, project managers often draft multiple versions of a project plan (WHAT-IF planning), each tailored to different combinations of these constraints (e.g., more resources with less time, or a larger scope with more time). This process allows for the selection of the plan that best suits the project’s needs and circumstances.
Predictable domain: Clear and Complicated together
Although Cynefin defines Clear and Complicated domains distinctly, they share a common trait: predictability. The spectrum between the Clear and Complicated domains is continuous, with situations ranging from completely clear, less clear, slightly complicated, and so on, mirroring games like Tic-Tac-Toe, Checkers, and Chess.
Similarly, management scenarios may require varying levels of analysis based on their sophistication — none, slight, moderate, etc. While it is worth distinguishing Clear and Complicated domains from each other, as they apply different management approaches. It is also important to consider them as a group together―Predictable domain, as the difference between them is not that drastic, as the difference to other domains which are Complex and Chaotic.
Summary
From a historical perspective, management in the Predictable domain significantly evolved within the Real Sector during the 20th century. This period is succinctly covered in the article titled “1900–1980s: Classical Management in 5 minutes”, which provides a starting point for a deeper exploration of the specific management methods applicable to the Clear and Complicated domains.
Classical Management methods hinge on the fundamental assumption of predictability, postulating that everything can be planned and designed upfront with sufficient expertise and analytical effort. While we now understand that this isn’t always possible and that Complex systems require a more Agile approach, it’s worthwhile to illustrate this point further with another example.
If we consider the increasing complexity of board games from Tic-Tac-Toe to Checkers to Chess, we can add another notable game to the list — Go. Although Go technically fits into the Predictable domain — with known rules, a fully visible board, and the potential to plan moves ahead — it stands apart due to the overwhelming magnitude of possible combinations.
The potential combinations in Go are so vast that it’s impossible to apply brute force analysis to predict the best move. This limitation exceeds the capabilities of human brains, and it exceeds the computational power of modern computers too; moreover, it exceeds the total computing power available in our universe. It is impossible to win in Go by brute force combinations.
This point was underscored when the chess computer Deep Blue defeated Garry Kasparov by applying computational power.
However, this approach proved fundamentally unsuitable against Lee Se-Dol in Go. AlphaGo’s triumph resulted from employing a novel method — Deep Neural Networks and Reinforcement Learning — to navigate the game.
This example underscores the importance of adopting vastly different strategies for Complex systems. Analytical methods have their limitations, and even entirely predictable systems can have so many variables that analysis alone cannot provide solutions.
With this understanding, we’re prepared to move forward to the next article, to explore the Complex and Chaotic domains of the Cynefin Framework.
