Our world is AVUCA.
It is Accelerating. Volatile. Uncertain. Complex. Ambiguous.
AVUCA causes a lot of dynamics in our daily life, the work we do, the industries we work in. In other articles we wrote, we learned about the other parts of the AVUCA acronym. This time, we will do a deep dive into the “Complexity” aspect.
We often hear that society is going through increasingly complex problems. Climate change, humanitarian disasters, the energy transition, global financial crisis and the COVID-19 pandemic. All of them are challenges that society is confronting at this very moment. And the problems are difficult to solve, for one solution triggers other problems, or every benefit to one party in the system might negatively affect others. It causes the problems to be, as we call it: complex.
But what does ‘complex’ mean exactly?
Before applying any kind of strategy, it is important to have an understanding of the differences between the types of problems we face in our daily life. A good way to get an overview of the types of problems out there is by using the Cynefin framework.
The Cynefin framework was introduced by management consultant and researcher David Snowden (2007) . His developed framework sorts the issues that leaders face into four separate contexts. Each separate context is different in the relationship between cause and effect. As a consequence, each separate context requires leaders to diagnose situations differently and to act in ways that are specific to the type of situation.
The Cynefin framework can help you categorize what kind of problem you are facing. Once you do that, you can then understand what is the most sensible way to approach it. So let’s break down each of the quadrants:
- Simple — Cause and effect exist and are predictable. Best practices have been established. An example of simple problems are the ones faced at a tech support call center — there is a clear issue and protocol to follow per specific issue.
- Complicated — The complicated challenges are mostly where subject matter experts are thriving. The problems in this category are not identifiable for everyone, and there are multiple answers that can solve the problem. Experienced individuals will be able to analyze the situation and identify possible solutions or practices. An example of a complicated challenge is improving the accuracy of sensors for autonomous driving — we may not be sure which exact technology is more efficient but in a couple of trials we can find the best solution.
- Chaotic — Usually crisis situations, in which acting is more urgent than analyzing and finding patterns. An example of a chaotic situation or problem is when doing disaster management — there is no time to collect data on the situation, we need to act first and analyze later.
- Complex — Rapidly evolving and systemic situations with a large number of variables. Complex situations are also often unpredictable. From the start, there is no clear direction to find a solution, experts might not even be able to provide a right guess. An example of a complex problem is when designing smart cities — there is no established best practice and the possible paths to develop a solution depend on a multitude of variables, such as location, traffic participants, and urban context.
Of these four categories, complex problems often cause the most difficulty to solve and are increasingly emerging in our present world. To dive even deeper into complex problems, let’s look at the following question:
So what makes a complex problem… complex?
Professor of Cognitive Psychology, Joachim Funke (2003)  has introduced five different features that characterize complex problems. To make these features more tangible and easier to grasp, we will apply them to the context of a current complex problem: the COVID-19 pandemic.
- Complexity of the situation — defined based on the number of variables in the given system.
In the case of the covid-19 pandemic, we observe numerous variables. Examples of variables in this situation are: countries, cases, regulations, or healthcare capacity. All of these variables interact and are dependent on each other. The larger the number of variables in a situation, the more complex the problem becomes.
2. Connectivity between involved variables — it is not only the number of variables that is decisive for the complexity of a problem, but also the connectivity between these variables.
This means that, if we have a situation in which all variables are dependent on each other, this will be, of course, harder to deal with rather than just having two variables dependent on each other. An example is the prognosis for COVID-19 patients, many factors are at play when trying to diagnose and predict how the infection is going to evolve in each patient.
3. Dynamics of the situation — This feature explains the fact that interventions into a complex network of variables might activate processes whose impact was possibly not intended and the situation changes itself over time.
How are the parameters related and how does the situation change once you shift one of the variables? An example of this connectivity is how governments were flabbergasted in the beginning of the pandemic, when the situation was quite unpredictable or poorly forecasted. Here we witnessed how one simple regulation can influence the number of interactions between people and therefore the number of COVID-19 cases, making the situation escalate.
4. Intransparency — concerning the variables involved and concerning the definition of the goal.
A clear example of intransparency was the fuzziness around COVID-19 symptoms, especially when the pandemic emerged. It was not clear what was the clinical presentation that showed healthcare workers whether someone was infected with this particular virus. This was also caused by the wide range of symptoms that each individual could display, which were not consistent.
5. Polytely — “Polytely means that in a situation, there are many goals. Because there are many goals, conflicts can arise on what is the most important goal. Conflicting and different goals require the forming of compromises and the definition of priorities, bringing complexity to a situation.”
An example of polytely can be seen in the never-ending discussion around lockdowns: should we put the economy on hold in detriment for lower covid-19 infections? The big challenge here is to establish a balanced trade-off between goals in the situation.
What does this mean for us as individuals and organizations?
Now you know what type of situations there are, you know that problems are the most difficult to solve, and you know what makes a problem a complex problem. To solve these increasingly emerging and increasingly difficult problems, we need to adapt our ways of thinking, planning and working.
We are dealing with unprecedented complexity and fluidity of problems and systems. Drawing conclusions from experience is no longer sufficient when we are constantly faced with novel and ambiguous issues. To quote a few thinkers of our generation:
- “There are no fixed rules, it is up to us to derive them and even then they change without notice.” Epstein (2019, p. 31) 
- „…what a rapidly changing, wicked world demands — conceptual reasoning skills that can connect new ideas and work across contexts.“ Epstein (2019, p. 53. 107) 
- “Relying on experience from a single field can be disastrous.” Epstein (2019, p. 53. 107) 
- “We are now dealing with the ‘Survival of the Most Fluid’.” Mlodinow, L. (2018) 
In fact, not being able to establish complex problem-solving practices inside organizations can cause a lot of harm to a firm’s growth and innovation. In a recent report from Harvard Business Review , they analyzed the “cost of complexity” and found out how much it harms organizations not being prepared to deal with complex problems. Extracting the results from the report mentioned above, we saw:
“Six out of ten say complexity increased operational costs by at least 11%. Almost half say their IT systems cannot respond quickly enough to deliver innovative business models or processes and 43% say complexity slows growth, impedes their ability to respond quickly to competitive threats, and interferes with effective decision making.”
How can we get better at dealing with complex problems?
On an organizational level, this means that we need to be prepared to look beyond our own markets, establish connections between different fields, and challenge our fixed thinking patterns. We need to work on overcoming our inherent fear of failure and we need to learn how to reframe complex problems. The good news is that we’re still way better at that than machines!
Tell us, what is the most important complex problem you are trying to solve within your organization?
 Snowden, D., Boone, M. (2007), A Leader’s Framework for Decision Making, retrieved online from https://hbr.org/2007/11/a-leaders-framework-for-decision-making#
 Funke, J. (2012) Complex problem solving, pp. 682–683
[3,4,5] Epstein. D (2019). Range: Why generalists triumph in a specialized world. New York: Riverhead books.
 Mlodinow, L. (2018). Your elastic mind. Psychology Today. https://www.psychologytoday.com/us/articles/201803/your-elastic-mind
 The Business Case for Managing Complexity, 2015, Harvard Business School Publishing. https://hbr.org/resources/pdfs/comm/sap/19277_HBR_SAP_Report_5.pdf