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

There is of course an underlying structure that shapes the emergent patterns. In the social sciences these structures are often called institutions – the ‘rules of the game’, norms and arrangements that shape how we engage with each other. In complexity thinking, we say that these structures give the system a certain disposition to show certain behaviours rather than others. These institutions are not designed but evolve over long periods of time. Also, a large number of these institutions interact with and shape each other and the way they shape behavioural patterns. Hence, there cannot be a root cause on the level of these structures that can be fixed, either.

Again, the idea of many little things interacting with each other to establish structure and behavioural patterns through continuous learning and adjusting to each other. No single, one-dimensional logical cause-and-effect chains from a root cause to a symptom. The concept/neology of symmathesy, mentioned by Ward and developed by Nora Bateson tries to capture this idea.

Inspired by Aidan Ward’s blog post, I looked up the concept of symmathesy. In the essay in which Bateson introduces symmathesy (which I haven’t yet read in its entirety I have to confess), she writes [3] (again, if you want, ecological ideas and concepts can be used as analogies to economic ideas and concepts):

Interdependency is vital to the health of any system. But, the interdependency does not sit still. All of biological evolution, and development of culture and society, would seem to be a testament to the characteristics of contextual multilayered shiftings through time. Nothing stays the same, clearly. So could it be that change is a kind of learning? If a living entity transforms, even slightly, some of its contextual interrelationships, it is within that shift that a calibration change is revealed. The same kind of tree in the same forest does not necessarily grow to be the same shape. One may have higher winds to contend with, or grow with a thicker density of flora around it. The trees in these contrasting contexts live into their contexts by receiving the many forms of relational information they are within, and responding to them. Thus they grow to be different shapes, to metabolize at different levels and so on.

In the same way, institutions and behavioural patterns ‘grow’ in a context and continuously shape and are shaped by a society and culture.

What different way of thinking could we use instead?

What then, is a better way of factors that influence specific behaviours and behavioural patterns in a complex system? I found the idea of modulators quite a helpful concept to replace root causes for complex systems. Dave Snowden uses a metaphor to describe modulators (Snowden contrasts modulators to drivers of change, which in my understanding are similar to root causes) [4]:

Imagine that you have a round flat table and around that table are a series of electro-magnets. They can vary in strength and also polarity. Some you control, some are controlled by people you know and some appear to change at random. In the middle of the table are a lot of iron filings. Now as long as the magnets don’t change, the iron filings will form a complex stable pattern. However as the magnets fluctuate in strength the pattern changes. if some of them change polarity then change is sudden and drastic before a new stability emerges. At the same time some of the iron filings get magnetised in turn as they pass through electric currents, making the situation even more complex. I may not even be aware of some modulators until they suddenly come into play and their impact is seen.
The magnets in this case modulate the system. They interact with each other and with the system as a whole, they make it inherently unpredictable. Understanding what modulators are in play will help us understand emergent behaviour of the system, but not to predict its future state. Attributing cause to a limited number of dominant modulators (that is what I think people mean by drivers) is a mistake as the level of interaction is too much. … The whole driver mindset is seeking to find something to which we can allocate causality. However it’s not like that, so tempting as it is its dangerous.

What does this mean in practice? While it does not make sense to go on the search for root causes, we can still identify modulators that we believe influence the situation and lead to the unfavourable patterns we observe. We cannot influences all modulators and some we might not even be aware of. But we can interact with some of them.

The concept of modulators ties in with other concepts from complexity thinking. For example, constraints and attractors are types of modulators. To give an example, an enabling constraint like the ability of traders to build trust and work together in a value chain modulates the way the economy performs. An attractor like a dominant crop that is produced in a region does as well. Other modulators are for example institutional structures, educational practices, beliefs and values, etc.

The point here really is that unfavourable patterns of behaviour in complex systems are not caused by specific root causes that can be fixed. There might be a modulator with a strong influence on the pattern, which we can identify and change. But given the complexity of the system, the exact outcome of the is change, i.e. how the unfavourable patterns shifts, cannot be predicted, because many other modulators are at play as well.


[1] The Mental Habits of Effective Leaders: My Interview with Jennifer Garvey Berger. The Knowledge Project Ep. #43. – Highly recommended!
[2] Aidan Ward. Where will change go?
[3] Nora Bateson. Symmathesy: A Word in Progress. 
[4] Dave Snowden. Drivers and modulators.

5 thoughts on “There are no root causes in complexity

  1. Bhav

    Glenda Eoyang’s CDE approach could be added here as a way to identify the modulators of a pattern that then allow us to change the CDEs to see if they shift/modulate the pattern towards more of what ‘we’ want to see.

  2. Sven Gehlhaar

    Great thinking Marcus! You made my ground shake today! We will discuss your reasoning in a workgroup of ours and later comment to you. Cheers, Sven

  3. Sven Gehlhaar

    Hi Marcus, here a quick thought on your findings from a practitioners point of view:

    Alone standing root causes might actually be an illusion, however, a bunch of underlying causes could combined represent a “root cause”. Following that chain of thinking, it becomes necessary to find that bunch of underlying causes that are mainly responsible for the problem or symptom. An example: a symptom we would like to address might be “low productivity” in a certain crop. After the market system diagnostics and root cause analysis we might end up with several possible deep underlying or root causes: a) no training offers for farmers because training institutions are mainly operating in urban areas and are considering rural areas as non-profitable clients, b) minifundium is driving young people into the cities; minifundium cannot be stopped due to rigid cultural norms, c) xxxx, etc. For each symptom/problem we might identify a bunch of such deep underlying causes. Together they are mainly responsible for the problem/symptom to persist. In practice we prioritize the most influential deep underlying causes and try to formulate potentially successful interventions. However, here the tricky part, in order to be able to formulate such an intervention we must think about: who can help us to implement that intervention? What is the best way how to do it? These two questions lead us directly to the most relevant drivers/modulators. If my thinking is right, than it would be just a semantic issue after all: root cause vs. deep underlying causes vs. a combination of most influential underlying causes, etc. However, to arrive at the most influential drivers and modulations there might be no other way than through these underlying causes.



    1. Marcus Post author

      Hi Sven. Great thinking. Yes, what you are describing is essentially an attempt to find some modulators that shape the situation of crop production in your country. Some other ones might be hidden and out of your side, some might be hard for you to influence, like the political economy. You don’t know how exactly the system will react if you start to change some of the modulators, such as the minifundiums. Effects might be unexpected. That is why you need to run small, safe-to-fail experiments. Also, you can start describing what you call ‘deep underlying causes’ from an institutional perspective – what are the social norms, customs, beliefs, etc, that shape the patterns you see. I think you are on a good path. As long as you don’t say that there is a root cause for one symptom and then try to build a single logical chain from one intervention to the result, claiming that this particular intervention will lead to that result, than that is a good step forward for me.


Leave a Reply