This is part two of a two-part blog series. Read part one here.
In his book, Managing for Happiness, the foundation of Management 3.0, author Jurgen Appelo uses an approach that, at first glance, resembles the Cynefin Framework, however, when observed in more detail, it presents a different factor:
The observer’s perspective: The Y axis presents the level of difficulty to understand a given situation such as:
- Simple: Represents something extremely easy to understand
- Complicated: Something very difficult to understand
- The X-axis shows the level of ability to predict the behavior of the situation: Orderly: it is totally predictable, Complex: partially predictable & Chaotic: Totally unpredictable.
In this model, something can be considered simple or complicated, yet still be ordered, complex or chaotic. Everything will depend on two variables:
1: Level of simplification
2: Level of linearization
The level of simplification increases as something becomes easier to understand and the level of linearization increases as something becomes more predictable.
The Structure-Behavior Model of systems
The Structure-Behavior Model (SBM) presents some similarities and differences in relation to the Cynefin Framework. The first difference is how to use them. SBM suggests an approach by categorization, in which the data is distributed in a pre-defined structure, that is, from an existing structure, the data is positioned in the quadrants. In Cynefin Framework, the domains have significant differences, as if they were on different planes. The border that separates the Clear from the Chaotic domain, for example, called a “zone of complacency”, reveals a region in which there is a “fall”. Cynefin is a sense-making structure, which emerges from existing data. It is the data that determine the structure and its limits.
The Cynefin Framework is used mainly to understand the dynamics of situations, in order to find the best decision-making process, helping to understand what is happening around us and, thus, enabling us to find the best answer to be adopted.
In addition to the visible similarities between the models, which bring with them the perspective of systems present in nature, another great similarity that makes them closer than distant is the “observer” view. Although the SBM makes use of categorization, when considering the level of difficulty in understanding about a given situation, the influence of the level of knowledge of the agents in the system is evident.
The example in the figure, the clock, in the complicated x ordered quadrant, is dependent and totally relative under this perspective. The operation of a watch, which is complicated for me, is absolutely obvious and simple for an experienced watchmaker. On the other hand, assembling a lego (simple-> ordered quadrant) can be an extremely complicated challenge for those who have never had contact with plastic blocks. In both cases, the level of knowledge present in the agent determines the quadrant in which the decision to be taken will be positioned.
An important point of difference between the two models is the concept applied to the types of systems
While SBM considers the level of predictability to be the determining factor for linearization, the Cynefin Framework, as detailed at the beginning of this article, separates systems using other criteria, such as attractors, boundaries and types of constraints. Chaos, for example, which contains an image of the stock exchange in the complicated-> chaotic quadrant of SBM, would conceptually be classified as Complex for the Cynefin Framework, as chaotic systems have no restrictions. The stock exchange, although unpredictable in the medium to long term, has some known patterns and clear restrictions, such as the hours of operation, currencies accepted, rules for investments, etc…
When the behavior of the stock exchange becomes totally unpredictable, someone “pulls the plug” and ends the operation, until the known restrictions (even small ones) are restored.
Something that caught my attention in relation to decision making and, especially, about the models and practices that I have been deepening in my studies over the last few years, is precisely the influence of the agent’s perception regarding his level of knowledge of the situation analyzed.
This reminds me a little of the Socratic paradox (I know that I know nothing) and the Dunning-Kruger effect — a phenomenon in which individuals who have little knowledge on a given subject believe they know more than others who have greater knowledge on the same topic. In other words, how can I be sure that something is really simple, if I am analyzing the phenomenon under the strong influence of my own knowledge (or lack thereof)?
I confess that this question has puzzled me for a long time, and it still puzzles me today.
About a year ago, researching the systems (ordered, complex and chaotic) and Johari’s Window, in order to see if anyone else suffered from the same dilemma, I came across an interesting article called: Cynefin framework and Johari window synergy with Risk Management. Although it is essentially an article on risk management, what caught my attention most was the comparison between Johari’s Window and the Cynefin Framework, which was exactly what I was looking for.
First a conceptual explanation: Johari’s window is a conceptual tool, created by Joseph Luft and Harrington Ingham in 1955, which aims to assist in understanding interpersonal communication and relationships with a group. Johari’s window application allows people to better understand their relationship with themselves and with other people.
Now, about the model. The window is divided into two axes: Me and the others. On each axis there is a quadrant related to the known and another to the unknown, so that a combination of 4 possible results is possible:
Johari’s Window Arena
This is an area where all the behaviors about which both I and others are aware are present. They are reciprocal and mutually known perceptions, that is, other people see the individual in the same way as the individual sees him/herself.
The Facade: Where are the behaviors I see about myself, but I do not allow others to see them. In this area, others see an image that does not reflect exactly who I am, and, for this reason, I must constantly take care to ensure that they do not perceive the “real me”.
Blind Spot: Where are the characteristics and behaviors that others perceive about me, but that I myself do not know and cannot perceive, that is, blind spots for me and visible to others.
Unknown: Where there are characteristics and behaviors that I do not know about my own personality and that others cannot perceive. Some of these unknown components may become aware of self-exposure (in a safe environment) and with a constant search for feedback. The change in one of the quadrants causes a change in the entire system.
Three Key Things to Highlight
#1: Asking for feedback: A movement established by accepting and encouraging the perception of others about ourselves, to identify how our behaviors are affecting them. This dynamic is as if we are seeing ourselves through the eyes of others. This makes the area that people know about me and I don’t know need shorter feedback loops, so that I can learn more about myself and, with that, migrate to another region.
#2: Disclosing and giving feedback: The movement through individual action, the “I”, offering feedback to others, identifying through their perceptions and feelings, how the behavior of others can affect me. Here I am the expert, and I need to give feedback so that people know more about me.
#3: Insights: The greater the self-exposure in a safe environment and the greater the amount of positive feedbacks, the stronger the vector towards Discovery, reducing the size of the unknown area. When we analyze the matrix formed by the Johari Window, there is a sudden desire to relate it to the domains of the Cynefin Framework, or to the quadrants of the Structure-Behavior Model. But I bravely resisted the temptation! I believe that each domain or quadrant can receive all the states of the matrix at a given moment and, precisely through blind spots, facades or unknown areas, both models retain in themselves an emerging property, a “perpetual innovation”, which allows us to constantly investigate and challenge them so that, little by little, we reduce the gap between a question and a good answer.