As you can derive from the name Creative Complexity, I have a very, very soft spot for complexity. And assuming reading those lines, you might be interested as well.
I like to say complexity again and again to normalize it. Complexity. Complexity. Complexity. Complexity. Complexity. Complexity. Complexity. And yes, it's much easier when you can copy-paste it. But on a serious note, more than often, it seems that complexity is the one thing that's likely being ignored and shoved under the rug when it comes to problem-solving. It's uncomfortable and intertwined with uncertainty. But most of all, complexity is not simplicity.
Everyone loves simple things. But let me ask you this: Can you name one truly simple thing? I remember that a few years ago, it was a reoccurring trend with some men in design to show a female nipple and introduce breastfeeding as naturally simple, even intuitive interface. But instead, listen to anyone who gave birth, and you will learn that it can – and more than often is a real struggle. Nothing is truly simple, even if we perceive it like that.
But at least in the context of complex problem solving, simplicity often is an outcome of complexity. That's what we're aiming for: Acknowledging, clarifying and understanding complexity to create simple solutions. You can't do one thing without the other.
We are experiencing increasing complexity through fragmentation of interests, social movements to respect all social groups (bummer! /s), questioning lifestyle decisions to prevent a drastic climate heating, and the growth of digital infrastructures driving a change of behaviors and environments. All of them leading to openness and progress as well as backlashes and polarizations.
In this current social climate, I understand embracing complexity as simply trying to acknowledge the world as it is, from multiple perspectives, not afraid of ambiguities and contradictions.
On this note, let's have a look at complexity from a scientific rather than personal perspective. What does this word even mean?
Sidenote: I am referring to complexity as defined by Joachim Funke, Professor of General and Theoretical Psychology at the University of Heidelberg, in his paper "Complex Problem Solving", 2012, and general descriptions of complexity in the context of Complexity Sciences.
First things first: Complexity is almost always used to describe a system or a problem. It's never just a singular entity.
Complexity is characterized by
1. a high number of actors/variables (in scientific literature sometimes called "agent") that 2. interact with each other in a non-linear way, 3. that are changing dynamically and are prone to disruption. Due to this nature, 4. there's uncertainty to know if all dependencies were included at any point. And to add, 5. there might also be a conflict of objectives between actors/variables.
That's why complex systems and complex problems are known to be larger than the sum of their parts.
In contrast, complicated problems are characterized by
1. a high number of actors and variables (also), but 2. they are connected linearly. 3. It is possible to gather a complete overview of dependencies 4. and find a viable solution.
To make these definitions more tangible, think of Spotify's and Netflix's recommendation systems.
On the interface level, there is a whole system to build the perfect and individualized cover designs (for movies and music) to keep you interested in the platforms and their contents. Building this asset creation ecosystem is highly complicated. It involves many departments and much knowledge. But with time, experience and by developing (automated) frameworks, this becomes much simpler.
In contrast, developing and balancing recommendation algorithms that make the "right" recommendation is a different beast. There might be a superficially shared objective of users and Netflix to want to stay on the platform, but while it seems like a good idea to show you things you might like, as a user, you might also want to be surprised.
This has implications on developing Original Shows and needs a ton of research – on a human level – to understand how people's taste (which can also change and evolve) works. Just thinking of myself, I love to sober-watch highly-regarded-über-detailed-intellectual-snob drama series like "Westworld" but drunk-watch trash movies like "Spice World". The same applies to music. Though we can see patterns through machine learning, we won't ever know all the factors involved. Like banner blindness, we might also develop a resistance to customization approaches over time. Understanding and anticipating these behaviors, translating them into (algorithm and experience) design decisions, that's complex.
As a rule of thumb: Complicated tasks can be managed and will be automated. Complex tasks – for the foreseeable future – can't.
I found it interesting that the same researchers started studying complexity and creativity in the 1960s. Leading them to the conclusion that it takes creative thinking to understand complex systems and problems.
This conclusion is also reflected in the Future of Skills Reports by the WEF. Though it's a little fluctuating, creativity and understanding complexity are some of the most important skills we have to acquire, even diverging into the breakouts skill of Analytical Thinking and Innovation as well as Reasoning and Ideation.
I know it's easier to shy away from it. But joining me on this journey, we might find some methods & approaches to make complexity a less daunting thing.