Complexity Thinking
Traversing the Magic Roundabout…
I’d like to explore with you why we think the time has come for organizations to shift their management styles towards more creative ones. The main reason for this is simply that the world we live and work in has become far more complex. But what do we mean by complex and what is complexity thinking?
Let’s look at an example of how the world we live in has become more complex. You probably remember the Central Perk coffee shop from the sitcom Friends. When that 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.
Nowadays, however, the coffee shop has become a place for work where people mostly interact with their devices and the regular cup of Joe has turned into a full range from Americano to Eiskaffee. 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, managers and scientists with a non-creative mindset tend to fall back on predictable systems and certainty. But by doing so they also tend to make four potentially catastrophic mistakes.
The Mistakes We Make (with a non-creative mindset)
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 focusing on the appraisal rankings of your employees to gauge their performance is a mistake. The complexity of human behaviour cannot be expressed in numbers alone.
Another mistake of the old-fashioned mindset is the habit to design systems instead of growing them. One example of this is creating an office layout and not wanting to adjust it when needed or forming teams and not reorganizing them if necessary. Another example is that is that once a plan has been made, that’s it, we should all stick to the plan. 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.
The third mistake managers make is that they rely too much on instruction instead of communication. Of course written company policies and a steady flow of progress reports are necessary, but only relying on these alone is not enough.
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 over-reliance on numbers, it becomes easy for people to hide behind this and avoid taking responsibility.
Basically, in and of itself numbers, systems and paper communication aren’t the problem. But when approached with a non-creative mindset, they might lead to the four mistakes we just covered. This is because organizations today have evolved into complex adaptive systems, always adapting to everchanging environments. Other examples of complex adaptive systems are, countries, gardens, cities and beehives.
In other words, a complex adaptive system 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.
Visualizing Complexity
Let’s look at the Magic Roundabout in Swindon, UK as a complex adaptive system. On the bottom of the graph you see the agents, which would be 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 behaviour of the individual agents, which brings us full circle.
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 cause causes what effect is unclear and can only be deduced in retrospect. It’s the 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 event 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 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 is in the chaotic domain, 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 market crash, on the other hand, is also in the chaotic domain and infinitely complex, there will be innumerable interrelated causes of the crash.
That brings us to the question, how to deal with complexity? Well, first, as Confucius once remarked, we need to be aware of what we know and don’t know. I would argue that there are four distinct kinds of knowledge:
There are things you know you know;
Things you know you don’t know;
Things you don’t know you don’t know, and;
Things you didn’t know you knew.
The first two are straight forward. From the moment we are born, we embark on a life-long learning journey and all the things we actively learn along this path become the things we know we know. By its nature, this means that we continuously encounter unknowns, the things we then realise we know we don’t know. The challenge, of course, is to tackle what we don’t know and transform it into something we know.
The third form of knowledge, the things we don’t know we don’t know, harks back to former US Secretary of Defence Donald Rumsfeld who said:
”There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know.”
Finally the fourth form of knowledge, which I find the most interesting one, the idea that there are things we don’t know we know. It was introduced by the Slovenian psychoanalytic philosopher Slavoj Žižek in reaction to Rumsfeld’s comments. Žižek’s rather dark interpretation of the unknown knowns is that they are those things we do know, but intentionally refuse to acknowledge.
Once we’ve aligned our knowledge, a good way to deal with complexity would be to visualize where we find ourselves. Let’s map things out in a CAS decision-making landscape.
Mapping Out Complexity
There are three areas in the 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.
The ordered domain is the most straightforward one. When managers are confronted with simple issues in the ordered domain, all they need to do, after they recognize they’re dealing with an ordered issue is categorize it and take action. A complicated issue in this domain requires a bit more action. Before categorizing it, the issue might need some analysis by experts before rational action is taken. But overall, the ordered domain is the one where a manager’s training and experience pay their dues.
In the complex domain traditional management approaches aren’t very effective. To deal with complex issues, managers 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.
Then the chaotic, the unknowable domain which any rational manager 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 managers need to rely on their intuition. There’s no time to analyze or rationalize, action needs to be taken! Once the situation is somewhat under control, managers needs to probe and experiment until they can make some rational sense of the issue. Once they have a better grip, they’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, it is easier said than done.
To help you out, there are eight guidelines to deal with complex adaptive systems. Unfortunately there aren’t any natural laws to deal with complexity. The best we can do is guide our thinking with the following eight guidelines.
The 8 Complexity Thinking Guidelines
1. Address complexity as complexity.
This may sound a bit confusing, but it isn’t really. A mistake often made when dealing with complex issue is trying to categorize it as you would and ordered 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.
2. 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.
3. Assume subjectivity and coevolution.
This also may sound a bit abstract but, like with addressing complexity with complexity, if you break down the problem into smaller parts and then start working with the parts you understand, then they in turn will clarify the ones you don’t understand yet.
4. Steal and tweak.
Very straightforward I think. There’s no need to keep inventing the wheel. Instead search for already existing solution to similar issues and adjust them to your own situation.
5. Expect dependence on context.
Another straightforward piece of advice. 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.
6. 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 tomorrow.
7. Shorten the feedback cycle.
Although it might be more interesting trying to workout 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.
8. 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 surprised.
[T]here you are.
