VUCA and the organizational contexts of the Cynefin model (1/2)
This series of two articles explores the fit between the organizational models attributed to the value systems of Spiral Dynamic and each of the five organizational contexts identified by the Cynefin model.
This first article entitled VICA and the organizational contexts of the Cynefin model makes the connection between the increasing complexity of organizational contexts, the need for evolution according to Spiral Dynamic and the different contexts proposed by the Cynefin model. In the second article Cynefin and the organizational models of Spiral Dynamic, we will explore the detail of the different organizational models that correspond to the value systems of Spiral Dynamic and look at which ones are compatible with each context of the Cynefin model. We will also connect the Cynefin model with the second tier value systems of Spiral Dynamic. The second article is intended for an audience that has a basic knowledge of Spiral Dynamics and an understanding that the value systems (purple, red, blue, orange, green and yellow) each correspond to a type of organizational structure.
The 21st century context of organizations
This century marks an acceleration in the domains of human, societal, environmental, technological and economic evolution. In this environment, often referred to as VUCA (volatility, uncertainty, complexity, ambiguity), a new paradigm is emerging for organizations for whom the capacity to adapt to a constantly changing environment is becoming the main criteria for both success and survival. The culture and organizational structure as well as the place given to the human being are evolving. In response to this VUCA context, organizations are beginning to evolve towards more agile, distributed and self-organized models.
The four components that constitute the acronym VUCA are:
- Volatility: technological and economic cycles are accelerating.
- Uncertainty: it is becoming increasingly difficult to predict or anticipate the future.
- Complexity: the sources of information are multiplying and the quantity of parameters to be considered is constantly changing.
- Ambiguity: despite abundant information, we lack clarity on how to interpret it, the information is contradictory, imprecise, or incomplete.
We are also seeing a shift in the needs of individuals; they are no longer satisfied from simply doing their jobs and having hobbies. With the advent of the Internet, individuals are increasingly interested in being active and involved both mentally and emotionally in their projects as well as in their organization. The need for autonomy and independence is also constantly increasing, everyone seeks to have free will and now makes demands before committing to anything. And finally, the need for purpose and to identify with values or an important cause is becoming more widespread in society. Initially attributed to the new generations Y or Z, this “quest for purpose” has very quickly spread to all active generations.
The different organizational contexts
There seems to be a link between the VUCA environment, particularly the increasing complexity, and the emergence of a new type of organization. All the models that exist point to the increasing complexity as one of the drivers behind the need to evolve. If this is the case, which model should be chosen to cope with this environment? No model is inherently better than another. In reality, only the fit between a model and the organizational context is a determinant of how well an organization functions.
However, be careful not to generalize the need for evolution and apply it to all organizations. Spiral Dynamics is a model – or a reading grid – that maps the different evolutionary paradigms of humans, organizations, and society. Although it would be tempting to want to progress along the different paradigms and evolve into so-called “higher” paradigms. Alas, this is not how evolution works. Spiral Dynamics explains it very clearly. Evolution only takes place when there is a lack of congruence between the life conditions (social, economic, political, technological, etc.) – which are evolving – and the practices we had developed to cope with the previous conditions of life. In other words, without tensions between our behavior and the environment we are in, no concrete evolution is necessary, nor desirable. This allows us to avoid a false vision of evolution that would be a smooth, natural and linear one towards increasingly complex contexts and models.
To begin with, it is interesting to observe that the organizational context of “complexity” is not systematically present, even in a VUCA environment. On the other hand, complexity is brought into the context from the moment an organization wishes to move away from a mechanistic vision of its functioning and chooses to put the human being at the center of its concerns (and stop considering humans as a “resource”). Indeed, the functioning of a single human being is already complex in nature, and the complexity increases when several human beings are asked to collaborate and form an organization.
The Cynefin model
The Cynefin Framework was developed by David Snowden in 1999. Cynefin is pronounced “kuh-NEV-in” as it is a Welsh word (meaning habitat). The principle is that any situation or any problem you are dealing with belong to one of these four categories: Obvious, Complicated, Complex, Chaotic. The dark space in the center represents Disorder.
The Cynefin model is not the usual “categorizing” 2×2 matrix. It is a model that emerges to make sense of existing dimensions, but whose boundaries are sometimes blurred. Therefore, the lines delimiting the different contexts are not straight. The model distinguishes a “predictable” nature on the right side of the model and an “unpredictable” one on the left.
Later, we will discuss different organizational contexts based on the Cynefin model, but first let’s illustrate how useful the model can be. This model has been designed as a problem-solving tool that helps to assess a situation by identifying which of the five “contexts” it fits in and how to respond appropriately.
In most professional situations that turned out to be worse than expected, it appears that decision makers assumed that they were dealing with an obvious or complicated context (right side of the diagram) when it became clear later that it really was a complex or maybe even somewhat chaotic context (left side of the diagram) instead. If a situation is clear (the right side of the diagram), then cause and effect are predictable, and with sufficient research, effective action can be developed. Expertise is usually quite useful when dealing with complicated problems, because the more you know about the variables and causal nodes, the better the decision you can make.
However, if you have misdiagnosed your context and are actually dealing with a complex situation, then pretty much any technique that helps you with simpler problems tends to be counterproductive when dealing with complexity. Complex situations are difficult to get a handle on because the cause-and-effect relationships are unclear, the best way to deal with them is often through trial and error. A particularly problematic feature of complex situations is that reliance on expertise often leads you off-course. The experts think they know how things work and implements solutions that only make things worse.
Applying the Cynefin framework to the problem of organizational change highlights a persistent pathology: most change initiatives are pursued as if implementation is complicated, when in fact organizational change is complex and often messy.
The relationship between cause and effect
Let’s start with a view of the relationship between “Cause (C) and Effect (E)” in the different contexts:
- Obvious: Problems are perceived as “obvious” and have a direct, clear and repeatable cause-and-effect relationship. The “predictability” of cause and effect is particularly strong. The game of “tic-tac-toe” in which two players take turns filling in the squares of a 3 by 3 grid is a good representation: a few simple rules are enough to play the best possible game, such as placing the first piece in a corner.
- Complicated: The cause-and-effect relationship is no longer obvious in nature and often requires a fine analysis, often conducted by experts, to determine the various relationships between multiple causes and the observed effects. Predictability still exists, but it is the domain of experts who use different predictive models. The game of chess is a good example of this context; it involves simultaneously considering several possible options and acting to reduce unpredictability.
- Complex: Complex problems are of a completely different nature than complicated problems. The main difference is that the cause-and-effect relationship is only revealed in retrospect. The game metaphor for complexity is poker. Unlike chess, which is a game of prediction, poker is a game that requires careful observation and experimentation. In a context of complexity, there is no way to learn to solve the problem, it is necessary to be in action.
- Chaotic: Chaos occurs when there is no observable relationship, even a posteriori, between cause and effect or when these relationships are constantly changing. In this context, there is no point in investigating or analyzing. No study can improve the chances of success of our actions. The game analogy is the children’s game in which the rules are constantly changing.
The practices
Let’s now observe the different types of practices that these different contexts give rise to for problem solving:
- Obvious: After looking at all possible options and calculating the best path, we respond to obvious problems by choosing the best possible practice. Combined with the predictability of the link between cause and effect, the best practice quickly becomes common knowledge. For example, washing your hands during a viral outbreak is the best practice to avoid contamination.
- Complicated: There is no longer a “best” practice in this context, but after a series of analyses it is possible to determine “good practices” based on the results observed following a series of experiments. An example of a “good practice” in chess would be to assign a value to the pieces and ensure that the total value of the pieces is greater than that of the opponent. The recommendations of the health authorities at the beginning of the COVID-19 crisis are perfect examples: banning gatherings, closing schools and businesses, confining people at risk, etc.
- Complex: As the path goes through an experimental part, action is part of the cycle. It is about investigating the results that our actions have produced, sensing how to adapt our practices and experimenting again. The resulting practices are emergent in nature. The progressive release of sanitary restrictions in the context of COVID-19 was a good example of this step-by-step approach since no model can predict the impact of releasing a lockdown or restrictions on the risks of triggering a new wave of contamination.
- Chaotic: Since the rules and context are constantly changing, a chaotic context invites action and the testing of new practices without any indication of their effect on the problem at hand. In the unprecedented situation of COVID-19, government executives took power and acted as quickly as possible. The main objective of the leaders who found themselves in a position to act was to stabilize the situation so that they can get out of the chaos as quickly as possible.
Strategy and organization type
Strategies for solving problems and organizational models are different in each context and they are connected with one-another.
- Obvious: To use the tic-tac-toe analogy, observing involves looking at the grid. Then we categorize ways to reduce the possible combinations into smaller, more manageable subsets. Categorization is the central element of the core problem-solving strategy in this context before a response occurs. This context is predestined for a centralized type of organization as it is possible for one person or a small group of persons to have a complete overview of everything happening (both inside and outside the organization).
- Complicated: Where it was enough to categorize in the obvious context, it is now a matter of analyzing in the complicated domain. It is in this phase that expert advice comes into play, developing models to interpret observations and then recommending actions to influence the course of events. For an organization that evolves in a “complicated” context, it is necessary to develop different centers of expertise. The centralized structure reaches its limits in this context and requires a form of decentralization with a formal delegation of competencies. Collective intelligence becomes necessary, and it requires different experts coming together in order to analyze a situation and determine collectively, the best practices to implement. In this context, a decentralized type of organization is what is most common.
- Complex: Since analysis is no longer sufficient to solve a complex problem, it is necessary to start a probe by investigating, which implies a form of action. In the poker example, investigation can take the form of a bet, which forces the opponent to respond, either by folding or outbidding. This allows us to sense their reaction. Other forms of investigation can be the observation of body language, but the interpretation does not involve an analysis of the observations, but rather a more subtle part that involves feeling. For an organization that evolves in a complex context, or more broadly qualified by VUCA, the distributed type of organization is the most appropriate. The principle of subsidiarity allows this cycle of investigate-sense-respond to be carried out as close to the field as possible and away from any centralized power that can only work by analysis and anticipation. When an organization faces a complex context, moving to a distributed authority and collective intelligence model is the only way to guarantee agility, evolutionary capacity and long-term sustainability.
- Chaotic: In a chaotic situation, to act is the first necessary step. To use the analogy of a children’s game, there is no point in trying to analyze the rules or investigate. The first thing to do is to participate in the game with others, to sense the impact on others and to adjust the way you act or respond according to the impact on others. Because action is the first step of any strategy in a chaotic environment, a centralized type of organization is best suited so that decisions about how to act can be made quickly by one or a few people. At this stage, it doesn’t matter whether the action taken is correct, but it matters most to act quickly to be able to sense the impact of that action and respond to that.
Observation about the top-down split of the Cynefin model
The Cynefin model has an intuitive left-right structure that distinguishes predictable contexts on the right from the unpredictable ones on the left. But the top-down distinction is less visible, and the author didn’t produce an explicit answer to it. On first sight, the obvious and the chaotic contexts have very little in common. By mapping the type of organizations that are suitable for each context, we can now observe a commonality between the obvious (sometimes also called “simple”) context and the chaotic context on the bottom of the model: they both do best with a centralized structure, while the other two contexts clearly don’t.
In a way, the need for a centralized type of organization in the chaotic context is because it’s a “simple” trial and error approach. While in an obvious context there might be a strategy and its execution might be fairly simple, in the chaotic context there is no possible strategy as nothing is predictable. In both contexts, it is most effective to not have too much discussion and debate before acting. In the obvious context it’s true because it would be useless to add complication to a situation that is obvious and in the chaotic context it’s true because it would be time consuming and hindering the necessary action that needs to be taken.
The continuation of this series can be found in the second article entitled Cynefin and the organizational models of Spiral Dynamic.
In video (ValueMatch conference)
Here is a recording of a conference presenting this material in November 2022.
Resources and suggested reading
- The Cynefin Framework by Snowden, D. and Boon, M. “A Leader’s Framework for Decision Making”, Harvard Business Review, November 2007
- Complicated or Complex? by Carmen Medina, January 2022
- Understanding Complexity by Erwin van der Koogh, July 2019
- Spiral Dynamics Integral (audiobook) written and read by Dr. Don Beck
- Spiral Dynamics in Action: Humanity’s Master Code by Dr. Don Beck, Teddy Hebo Larsen, Sergey Solonin, Dr. Rica Viljoen, Thomas Q. Johns
- La spirale dynamique – Comprendre comment les hommes s’organisent et pourquoi ils changent by Fabien Chabreuil and Patricia Chabreuil
- ValueMatch offers a free Spiral Dynamics knowledge test, as well as a values report and a personal change report (for a fee)
I founded Paradigm21 in 2018 to share 3 years of experience in creating and managing a "distributed" organization. I am committed to supporting the transformation of our society and its institutions.
Specialized in the diagnosis and supporting transformation, I combine Spiral Dynamics, Integral Vision with a human and holarchic approach to organizations.
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