The difference between complex and complicated

We often think that complicated and complex are on a continuum, that complex is just a magnitude above complicated; or that they are synonyms. These are actually different, and one cannot address complex systems in the same way as complicated. Many improvement efforts fail by not seeing the difference and they throw resources at projects that are bound for failure because they are looking at the system the wrong way.

Complicated problems originate from causes that can be individually distinguished; they can be address­ed piece by­ piece; for each input to the system there is a proportionate output; the relevant systems can be controlled and the problems they present admit permanent solutions.

Complex problems result from networks of multiple interacting causes that cannot be individually distinguished and must be addressed as entire systems. In complex systems the same starting conditions can produce different outcomes, depending on interactions of the elements in the system. They cannot be addressed in a piecemeal way; they are such that small inputs may result in disproportionate effects; the problems they present cannot be solved once and for ever, but require to be systematically managed and typically any intervention merges into new problems as a result of the interventions dealing with them;  and the relevant systems cannot be controlled – the best one can do is to influence them, or learn to “dance with them” as Donella Meadows said.

Lets break down some ways these look and act different by looking at some of the key terminology.

Causality, the relationship between the thing that happens and the thing that causes it

Complicated	Linear cause-and-effect pathways allow us to identify individual causes for observed effects.
Complex	Because we are dealing with patterns arising from networks of multiple interacting (and interconnected) causes, there are no clearly distinguishable cause-and-effect pathways.

This challenges the usefulness of root cause analysis. Most common root cause analysis methodologies are based on cause-and-effect.

Linearity,  the relationships between elements of a process and the output

Complicated	Every input has a proportionate output
Complex	Outputs are not proportional or linearly related to inputs; small changes in one part of the system can cause sudden and unexpected outputs in other parts of the system or even system-wide reorganization.

Think on how many major changes, breakthroughs and transformations, fail.

Reducibility, breaking down the problem

Complicated	We can decompose the system into its structural parts and fully understand the functional relationships between these parts in a piecemeal way.
Complex	The structural parts of the system are multi-functional — the same function can be performed by different structural parts.  These parts are also richly inter-related i.e. they change one another in unexpected ways as they interact.  We can therefore never fully understand these inter-relationships

This is the challenge for our problem solving methodologies, which mostly assume that a problem can be broken down into its constituent parts. Complex problems present as emergent patterns resulting from dynamic interactions between multiple non-linearly connected parts.  In these systems, we’re rarely able to distinguish the real problem, and even small and well-intentioned interventions may result in disproportionate and unintended consequences.

Constraint

Complicated

One structure-one function due to their environments being delimited i.e. governing constraints are in place that allows the system to interact only with selected or approved types of systems.  Functions can be delimited either by closing the system (no interaction) or closing its environment (limited or constrained interactions). Complicated systems can be fully known as a result and are mappable.

Complex

Complex systems are open systems, to the extent that it is often difficult to determine where the system ends and another start.   Complex systems are also nested they are part of larger scale complex systems, e.g. an organisation within an industry within an economy.  It is therefore impossible to separate the system from its context. This makes modeling an issue of replicating the system, it cannot be reduced. We cannot transform complex systems into complicated ones by spending more time and resources on collecting more data or developing better maps.

Some ideas for moving forward

Once you understand that you are in a complex system instead of a complicated process you can start looking for ways to deal with it. These are areas we need to increase capabilities with as quality professionals.

• Methodologies and best practices to decouple parts of a larger system so they are not so interdependent and build in redundancy to reduce the chance of large-scale failures.
• Use storytelling and counterfactuals. Stories can give great insight because the storyteller’s reflections are not limited by available data.
• Ensure our decision making captures different analytical perspectives.
• Understand our levers