Complex vs. Complicated

Considering the differences between simple, complicated, complex, and chaotic challenges is foundational to practicing systems thinking.

Describing a challenge as simple, complicated, complex, or chaotic is a useful way to describe how predictable they are, how easy they are to address, and how reliably we can use our solutions over and over again.

Often you don’t need to know all the details about a problem to make a good guess at whether it is complicated or complex. Think about how predictable outcomes are and whether top-down control is possible (Complex vs. Complicated).

Glouberman and Zimmerman suggested three helpful analogies for assessing the level of complexity: baking a cake is simple, sending a rocket to the moon is complicated, and raising a child is complex (Cake Rocket Child). These analogies allow for quick and dirty comparisons based on things like how the problem is defined, the use of rules and expertise, and the probability of predicting the outcome.

Snowden zeros in on the relationship between causality and our ability to understand it (Cynefin). With simple problems, this relationship is clear or obvious, which allows for things like a “best practice” or a “recipe” for solving it. In complicated problems, the relationship between cause and effect is less obvious and requires some investigation or analysis to understand it better. This allows you to develop an approach to the problem that is mostly reproducible after some trial and error. But when the problem is complex, the relationship between cause and effect is very hard to understand except in hindsight. Any approaches you develop must never stop adjusting and evolving. This is sometimes called emergent practice.

Stacey developed a framework for organizational management that considers the level of agreement about what the problem is versus the level of certainty about how to address it (Stacey Matrix).  When certainty and agreement are low, problems are complex, and decisions are more influenced by subjective or political factors.

Several of these frameworks also include chaos, the extreme end of the spectrum of complexity where agreement and certainty are very low (Stacey Matrix) and the relationship between cause and effect is unclear and impossible to determine (Cynefin).

It’s important to remember that these types of systems (simple, complicated, complex, and chaotic) are not fully distinct—these are just ways of generally classifying problems and systems. Depending on the exact nature of a challenge (social, physical, political, ecological, etc.), it might have attributes of more than one type. It might even seem to flow from one type to another (and back). The Order to Chaos framework uses the metaphor of water to illustrate what flowing between order, complexity, and chaos might look like (Order to Chaos).

Frameworks in this collection