Understanding complexity

A short primer on why complex systems require different ways of acting and making decisions

A few years ago, as I was managing a growing team, I set out to try and understand why traditional forms of management didn’t feel appropriate. I went deep on group dynamics, psychology, and human development theory, and started to piece together different parts of what felt like a very big puzzle.

Throughout my reading, the concept of complexity kept coming up. On the surface, it made sense: everything pertaining to the evolution of the human experience is tangled up in a web of cause-and-effect; resulting from — and leading to — increases in complexity.

Hunter-gatherer tribes became early civilizations, which became industrial nations, which then led to the network age. The internet, mobile networking, AI, automation, and increasingly distributed workforces, all contribute to the complexity of today’s workplaces.

So sure, the world is more complex; therefore it probably makes sense that we need to manage our organizations differently. But what does it actually mean for something to be more complex? And once we know we’re dealing with a complex system, what are the implications for how we should think and act? Is managing a team of 100 programmers in the 2010’s actually harder than running a 1,000 person factory in the mid-1900’s?

It was during a conversation with my friend Jordan Husney that the penny dropped. We had been discussing Spiral Dynamics and value-attracting memes (that’s another story) and contemplating how complexity is really a matter of perspective. Something that seems obvious to you, say how a combustion engine works, might seem complicated to me.

We discussed how much of management theory is based on 20th-century practices that were concerned primarily with efficiency and consistency; about making sense of systems, making the unknown known, and then optimizing the hell out of it. In these systems there are many moving parts and lots of variables, but with effort and analysis you can reason about them. You can create blueprints, schematics, or flow-charts.

In this networked era, where knowledge work is predominant and everything is interconnected, this way of viewing work is no longer possible. There are always going to be things that are unknowable. This is the root of what it means for a system to be complex.

“Complexity is a movement in time that is both knowable and unknowable. Uncertainty is a basic feature of all complex systems.” — Esko Kilpi

A framework for complexity

To better understand what this means in practice, I’ve found the Cynefin framework hugely helpful. The framework outlines contexts, or domains, that describe a “sense of place” from which a system can be perceived. By understanding your context and the nature of the system, you can better make sense of the available information and better understand how to make decisions.

The domains are obvious, complicated, complex, and chaotic.

Something that is obvious is deterministic and certain. 1 + 1 = 2. If I put a cup of water in the freezer, it will turn to ice. If I flick the light switch, the light goes on. In the obvious domain, solutions are well-known.

Complicated systems are harder to understand, but they are still knowable. A well-known set of inputs will produce consistent outputs. When you have to read your oven’s instruction manual to bake bread, that’s complicated. But so is a car or a nuclear power plant. In the complicated domain, experts rule and knowledge is power.

As we discussed above, complex systems are unknowable. You are dealing with probabilities and emergent characteristics. There are often an indefinite number of variables influencing what is happening and — if it is possible at all — cause-and-effect can only be determined in hindsight. Through experimentation and analysis, patterns may be observed which allow for predictions to become more accurate. The stock market, ecosystems, and human culture are all complex.

In chaotic systems, cause and effect are always unclear. Chaos is the realm of crisis and panic.

Cynefin Framework: a 2x2 showing obvious, complicated, complex, and chaotic, with disordered center

It’s all about your perspective

An important element of this framework is that it is concerned as much with your perspective of the system as the system itself. Something that seems obvious to you, might be complicated for me. Something complex to a novice becomes complicated to an expert. Therefore, movement between domains is possible, for better and for worse, based on the information and knowledge you have.

Imagine an old TV. If you hit the power button it turns on or off (obvious). As it gets older, something breaks and it starts to randomly turn off (chaotic). Through experimentation, you realize that propping it up on a book and hitting it on one side sometimes fixes the problem (complex). You find a schematic, take it apart, and find a loose wire (complicated).

To some extent, complexity can, therefore, be managed through the acquisition of knowledge, surfacing of new data, or by decomposing a system into constituent parts which are more easy to reason about.

Leading through complexity

Knowing where you and your team are is an important first step. Are you dealing with a complicated system, or — as is more likely — a complex or chaotic one?

Next, look to see where there is room for movement. Can you gather knowledge that reduces the complexity? Are there actions you can take which could surface variables that might make the unknown known? Can the problem space be decomposed, such that elements become more obvious?

But in the workplace, no matter what you do, you are dealing with people. And humans are inherently complex. Regardless of how much data you surface or how much knowledge you acquire there will always be unknowable elements that affect how people behave and perform: the interaction of personalities, goings-on in personal lives, macro-economic movements, political changes, etc. etc.

This is part of the reason industrial management tactics, which were concerned with prediction and control, are no longer as effective. As leaders, we need to accept there will always be uncertainty, and focus on building flexible teams that can experiment and learn, through increased transparency and cooperation.

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