There are no root causes in complexity – marcus jenal – Medium

Source: There are no root causes in complexity – marcus jenal – Medium

There are no root causes in complexity

I have never been very comfortable with the concept of root causes. I do see the need to go below the surface and not just look at the ‘symptoms’. Yet, it seems to me that the concept of root causes — one problem causing one or a number of symptoms — is at odds with the idea of complex systems, where patterns emerge as a result of a number of different interconnected and interdependent elements and structures.

The idea or root causes is linked to a linear cause-effect kind of thinking. This often plays out as follows: development agents going into a country, observing an undesirable pattern or symptom, doing some analysis to find a root cause, fixing it, and assuming the symptom will disappear — a linear causal chain is assumed from the root cause to the symptom. This is also the reason why many projects use results chains — chains of boxes and arrows indicating steps in a causal chain from the root cause to the symptom.

The problem with this type of thinking is that it does not reflect how the world really works. Still, this is how development generally approaches complex problems. Complexity thinking offers a different way of thinking about intractable or ‘messy’ issues such as getting stronger and more inclusive economies. One concept in particular seems helpful to replace the linear causal logic from root causes to symptoms: the concept of modulators.

What is the problem with root causes?

In the field of market systems development, which is one of the main areas of my work, it is ‘good practice’ to do a root cause analysis pretty early on in a project. It is usually done after selecting and analysing a market sector to figure out why it is underperforming and/or excluding a certain part of the population. The logic is simple: we want to go beyond tackling mere symptoms (e.g. poor people being excluded, women being not able to earn money, youth not getting jobs) and discover the ‘deeper’ reasons for these patterns — the root causes. The method used to find a root cause is for a particular symptom to keep asking ‘why does this happen?’ until one hits some sort of a bottom (why do youth have no jobs? because there is a mismatch of available skills and labour demand. why is there a mismatch of available skills and labour demand? … and so on, you get the point). Once this root cause is found (I haven’t really figured out when to stop with the why’s), you design some interventions to ‘fix’ it, expecting that once it is fixed, also the symptom will disappear.

This approach will definitely work for some problems. For example, if people cannot reach a promising market place because there is no way to get from A to B, a road or railroad connection will fix that. But thinking that one could change the reason why a whole segment of the population is excluded or why private businesses do not respond to a market opportunity or do not innovate does need a different type of thinking about systems.

This morning I listened to an episode of the Knowledge Project podcast [1], in which Jennifer Garvey Berger, co-author of the book “Simple Habits for Complex Times: Powerful Practices for Leaders” put it this way:

We tend to be looking for the root cause of something, but in complexity, there’s no root cause. There’s no root cause of a hurricane. There’s no root cause of a tsunami. There’s no root cause in nature. There are just many forces that interact together to get you a particular effect. Similarly, there’s no root cause of trust. There’s no root cause of leadership. These are all a series of things that happen together.

It is hard for us to imagine that there are no discernible single causes for certain problems. But in complex systems, all the elements are highly interconnected and interact with each other while at the same time continuously adapting their own strategy as a result of what they observe. There is continuous learning happening in these systems and the resulting patterns is the result of a multitude of small continuous interactions.

Aidan Ward puts it this way in a recent blog post (try to exchange ecosystem with economy and species with companies in this quote) [2]:

In an ecosystem there is non-directive change. There are many, many experiments with form and function. These experiments lead to a new situation where the species and their niches interact slightly (or significantly) differently. Forget about “competition” or “fitness” or all the other one-dimensional approximations to what happens. Think instead of Nora Bateson’s symmathesy, the subtle, beautiful, and infinitely complex mutual accommodation and learning that takes place.

Going deeper

Continues in source.