3-Cs of Thinking: Complexity Thinking

What is Complexity Thinking?
How to Deal with It?
In a previous article I introduced you to the concept of the VUCA world, the Volatile, Uncertain, Complex and Ambiguous environment we find ourselves in today and the 3-Cs of Thinking. In this article I’d like to delve deeper into the field of complexity thinking and help you to understand what it is, why it matters and how to deal with it.
To begin, let’s look at a simple example of how the world we live in has become more complex. You may remember this place, the Central Perk coffee shop from the sitcom Friends. When the show launched in 1994, a simpler time, a coffee shop was a place where you’d meet your friends and your choice of coffee in most cases was either a black coffee or a coffee with milk and sugar, maybe an espresso or cappuccino in fancier places.
But nowadays, the coffee shop has become a place for work where people mostly interact with their devices and you can order anything from an Americano to a Frappuccino or an Eiskaffee, but a simple cup of coffee is nowhere to be found. Now, of course this isn’t really an example of complexity but I think it does give a clear image of how, on a daily basis, we have to deal with far more information than before. And processing all this information complex. Because of this, we tend to fall back on predictable systems and certainty. But by doing so, we also tend to make:
4 Potentially Catastrophic Mistakes
(1) The first and most common mistake is the tendency to prioritize numbers over people. Now of course numbers are important for business. Not knowing your profit margins, cash flow or return on investments would spell disaster. However, not everything can be expressed in numbers. For example, only focussing on the appraisal rankings of employees to gauge their performance is a mistake. The complexity of human behaviour cannot be expressed in numbers alone. And in the end it’s people who bring the money in, not numbers.
(2) Another mistake is the human tendency to design systems instead of growing them. Most of us have a firm belief that once a plan has been made, that’s it, we should stick to it. That’s a mistake. When I was in the army, we were always told that, yes, we should plan all our actions but only with the realization that those plans become obsolete the moment you execute them. The only reason to make those plans in the first place was to be ready to adapt them, you need a starting point. Another example is product design. Designers tend to fall in love with their designs and forget about the end users. However, if there’s one thing we can be sure of, it is that the user decides. Look at this example, the city planner crested an aesthetically pleasing design, but pedestrians like to take shortcuts. Over time the little elephant path created will become the way to go.
(3) The next one should be obvious, people today really aren’t great at straightforward face-to-face communication anymore. So the third mistake is that we rely too much on policies and procedures instead of communication. Of course certain written company policies and a steady flow of progress reports are necessary, but relying on these alone is not enough. Just so you know, in a later module I’ll come back in detail to this topic.
(4) The fourth and possibly most disastrous mistake is that the sum of the previous mistakes will lead to everybody pointing at each other as the cause of problems. When there’s no communication, an inflexible work environment and an overreliance on numbers, it becomes easy for people to hide behind this and avoid taking responsibility.
To sum it up, in and of itself numbers, systems and paper communication aren’t the problem. But when approached with the wrong mindset, they might lead to the four mistakes we’ve just covered. This is because organizations today have evolved into Complex Adaptive Systems (CAS), always adapting to everchanging environments. Some other examples of these complex adaptive systems are gardens, cities, traffic and beehives.
In other words, a CAS is a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, which in turn then influence behaviour of the agents. Huh, I can hear you think… Let’s visualize this definition.
The Magic Roundabout
Have a look at the Magic Roundabout in Swindon, UK as a prime example of a complex adaptive system. The semi-autonomous agents in this system are the vehicles on the roundabout. These agents all interact and are free to make their own choices about when and how they do so.
On this roundabout for example, drivers can decide to stay ‘safe’ and remain in the more traditional outside ring or they may make use of the more adventurous small inner roundabouts and at some point even find themselves going against traffic. Whatever choices they make, over time some interactions will happen more frequently than others and so these will generate system-wide patterns of behaviour that come to characterize that system as a whole. Subsequently those patterns will then reinforce the behaviours of the individual agents, which brings us full circle.
CAS Behaviour & Structure
Another way to look at complex adaptive systems is to study their behaviour and structure. Simply put we can argue that there are three basic behaviours of a complex adaptive system. The first behavioural domain is the ordered or the known. These are the elements of a complex adaptive system that we know and understand. The ordered domain represents stable situations in which the relationship between causes and effects is clear.
Next we have the complex or unknown domain. In this domain what causes what effect is unclear and can only be deduced in retrospect. It’s unknown but through deductive analysis and experimentation it can become knowable. Some examples of Complex domains are battlefields, markets and corporate cultures, each of which requires a take-it-apart-and-see-how-it-works approach to understand them.
Finally there’s the chaotic or unknowable domain. In this domain, events are too confusing for knowledge-based responses. Instead the chaotic needs instinctive action in an effort to turn the chaos into a complex situation. Examples of the chaotic are terrorist attacks or financial market crashes.
Those are the three different domains of a complex adaptive system and each of these can be structured in one of two ways: they’re going to be either simple or complicated. For example rolling dice. Rolling a die, for example, is in the chaotic domain as you can’t scientifically predict the outcome, but it’s also simple because there are only six possible outcomes. You either roll a 1, 2, 3, 4, 5 or 6, there are no other options. A stock market crash, on the other hand, is also in the chaotic domain and infinitely complex as there will be uncountable interrelated causes of the crash.
CAS Decision-Making Landscape
That brings us to the question, how to deal with complexity? Let’s map it out in a CAS Decision-Making Landscape.
There are three areas in this CAS decision-making landscape. In the green safe-zone on the left we find the ordered domain, the things we know and are organized in ways we understand. On the other side of the spectrum we find the chaotic danger-zone, just unknowable, unorganized chaos. And in between the two there’s the domain of the complex. The issues in this domain may be unknown and unorganized, but they are knowable and as such can be organized, often from the bottom-up through self-organization.
So how does this help me deal with complexity I can hear you think. Well, let’s look at them one by one.
(1) The ordered domain is the most straightforward one. When you are confronted with simple issues in the ordered domain, all you need to do, after you recognize you’re dealing with an ordered issue, is to categorize it and take action. A complicated issue in this domain requires a bit more action. Before categorizing it, the issue might need some expert analysis before rational action is taken. But overall, the ordered domain is the one where your training and experience pay their dues.
(2) In the complex domain traditional approaches aren’t very effective. To deal with complex issues, you need to continuously probe and experiment to correctly identify them before taking appropriate action; it’s a great opportunity to unleash creativity and innovation from the ground up and create new models of operation. It is also the domain where you learn from your mistakes.
(3) Then the chaotic, the unknowable domain which any rational being dreads. Issues that come up in this domain display high levels of uncertainty and disagreement, often disintegrating in total anarchy. To deal with issues in this domain you need to rely on your intuition. There’s no time to analyze or rationalize, action needs to be taken! Once the situation is somewhat under control, you need to probe and experiment until you can make some rational sense of the issue. Once you have a better grip, you’ll need to adjust the actions earlier taken.
To summarize, the ordered domain requires rational decision making, knowledge and skills. In the complex domain you’d focus on creative decision making and doing experiments. And finally, in the chaotic domain you’ll have to rely on instinct and make gut-feeling decisions. Rational, creative and intuitive, sounds simple enough. But, of course, this is easier said than done.
The 8 Complexity Thinking Guidelines
To help you out, there are eight guidelines to help you deal with complex adaptive systems. Unfortunately there aren’t any natural laws to deal with complexity. The best you can do is to guide your thinking by following these eight guidelines.
(1) The first one, to address complexity as complexity may sound a bit confusing, but it isn’t really. The main mistake often made when dealing with a complex issue is trying to categorize it as you would a simple one. Instead of doing this, it will be more effective if you try to break the complex issue down into smaller parts and then try to visualize them. I’m aware this may sound a bit abstract right now, but I think the following example might clarify it for you. Sudoku puzzles can be complex, but if approached systematically they aren’t that hard to solve. In this case, at first glance, it seems quite complicated. But if I focus on just one number, let’s say four, I can quickly see which boxes still need a four. Next I cross out all the squares blocked by the existing fours and voila I can quickly spot where the next one goes. As you can see, by simplifying the problem it became much easier to solve.
(2) The second guideline is to use a diversity of perspectives. Different people view things in different ways. So by acknowledging that there are other ideas than your own whether they’re right or wrong, a diversity of perspectives will give you a clearer picture of the complex problem. Take a look at this comic for example, both are right depending on what point of view you take. It’s the same with complex issues, they’re not just black or white.
(3) Number three is that you should assume subjectivity and coevolution. This again may sound a bit abstract but, like with addressing complexity as complexity, if you break down a problem into smaller parts and then work out the smallest part, then you can expand from there and work out the issue as a whole.
(4) Steal and tweak. Very straightforward I think. There’s no need to keep inventing the wheel. Instead search for already existing solutions to similar issues and adjust them to your own situation.
(5) This next guideline is another straightforward piece of advice, expect dependence on context. Everything is dependent on context. So what worked once might not work again and what worked for others might not work for you, or at least not in the same manner. So never just copy-paste, always, copy-analyze-paste a solution.
(6) Then number six, anticipate, explore and adapt. This is about being proactive and willing to experiment continuously. This is a topic which we’ll come back to extensively in module four of this course. As a quick example, look at this platform sign of the Paris underground.
People hate waiting, that didn’t need much research. But it’s not so much the waiting itself but not knowing how long that’s the issue. So it was decided to show how long it would take for the next train to arrive. Problem solved you’d think. But not really because now people were wondering if they would have time to get a coffee or go to the toilet or something but then they might miss that train. So a second arrival time was added so now people felt in control. A simple but effective solution.
(7) We’re almost there, number seven is to shorten the feedback cycle. Although it might be fun trying to tackle something as a whole, with complex systems it’s much more effective to run lots of experiments to learn step-by-step how the complex issue really works. Systems with slower feedback cycles have higher extinction rates in everchanging environments. So, iterate faster!
(8) And finally, keep your options open. Actually some advice for anything you do in general. Plan but accept that plans stop working once you execute them. So, just go with the flow and allow yourself to be surprized.
[T]here you are.