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Systems Thinking: a Technical Overview
Systems thinking is an alternative to a purely reductionist approach to understanding complex systems, for example healthcare. But what exactly is it?
I've had to to a bunch of reading about systems thinking with respect to healthcare recently, sparking an intense interest in the topic. You tend to find a lot of literature online talking about it. Systems thinking is described as an approach to problem solving in which one thinks about the whole system as being comprised of constituent parts and concentrates on parts and relationships both.
However, that doesn't really tell us much about what it is and how one goes about using this paradigm. If one wants to really get into what systems thinking is about, a more academic source might be handier. For the purposes of this article, I refer to a paper called "A Definition of Systems Thinking: A Systems Approach" by Arnold and Wade from 2015 which goes about the process of definition in a very interesting way.
So what really is systems thinking?
We can start by saying that systems thinking is, really, nothing more than a system of thinking about systems. If that sounds like a circular concept, buckle up. Systems tend to be the easiest to define using their function or their purpose; but that is, often, the hardest part of a system to tease out. In this case, fortunately, it is easy. The function of systems thinking is to think about systems!
To go deeper, we need to define what a system is. The Oxford Dictionary tells us that a system is "a set of things working together as parts of a mechanism or an interconnecting network; a complex whole." In other words, it is a set of things (let's call them elements) which are connected to each other, signifying relationships. So systems thinking is a framework which gives us the tools to understand how a system functions.
Why is this needed?
Understanding systems turns out to be fundamental to nearly every task we manage because systems surround us. Nothing in this world is completely independent. Society, the government, electricity transmission, the shipping industry, the ecosystem of a pond, the climate are all examples of systems. Understanding each of them is an art in of itself: understanding their effects on each other and the surrounding world might be beyond the world's fastest supercomputer!
Hold on, you might say at this point. Don't we already understand these things? Aren't we already fluent in our understanding of electricity transmission systems? Didn't we design the government? Isn't the shipping industry the bedrock of all modern consumerism?
The answer is kind of, to all of those. Or rather, it depends on how we define these systems. Sure, we understand electricity transmission systems. But a huge element of these systems is the group of consumers who sit pretty at the other end, the ones who actually consume the electricity being generated and transmitted. Another big part happens to be the workers who maintain these lines. Another element of this system might be politicians (depending on your country) who may have a vested interest in making sure that the constituents of their choice get the generated electricity. Claiming that we understand all these actors and the relationships between them is more than can be said by most people. Similarly, it would be the height of folly for any man or woman to claim that they understand the inner workings of the government or the economic eddies that govern shipping.
The classical approach to problems such as this is called reductionism. Most wielders of the scientific method are intimately familiar with this school of thought. Break a complex problem into its constituent parts, understand each part, and lo behold! The complex, intractable problem is now reduced to several manageable issues.
This tends to be one part of systems thinking. Knowing how to define the elements of a system requires one to be educated in reductionism, but understanding their relationships tends to be a problem of exponentially increasing complexity as the number of elements of a system increase.
So let's start with the easier part of systems thinking. Reducing it to its elements.
Elements of systems thinking
Stave and Hopper (2007) give us a set of elements with which we may start understanding systems thinking:
Understanding Dynamic Behavior
Differentiating types of flows and variables
Using Conceptual Models
Creating Simulation Models
Arnold and Wade (2015) add to and subtract from these elements to give us the following:
Recognizing Interconnections: This tends to be the most basic systems thinking still. One needs to be able to identify the key parts of a system and figure out the key relationships between them
Identifying and Understanding Feedback: A bunch of these relationships form feedback loops, where two elements may feed into each other. These need to be understood and evaluated separately from the others
Understanding System Structure: This is a step above the other two elements, in which one takes these key relationships and understands how the parts of a system fit together so as to describe the system as a whole
Differentiating Types of Stocks, Flows, Variables: A stock is a pool of a given resource in a system. A flow would be the inflow or outflow of that resource. A variable would be something like flow-rate or the maximum quantity of a stock. Once the system structure is understood, stocks, flows and variables need to be quantified and described separately
Identifying and Understanding Non-Linear Relationships: This has been separated out from the previous element by Arnold and Wade. In particular, this element is also about measuring stocks and flows, but it measures those which do not have linear relationships with each other
Understanding Dynamic Behavior: All the elements and relationships which have been understood interact with each other and may behave unpredictably. Such behaviour is called dynamic behaviour. Understanding this is one of the core functions of systems thinking
Reducing Complexity by Modeling Systems Conceptually: While complexity is part and parcel of most systems, understanding them often requires stripping away that complexity and observation from different angles. In other words, one needs to know how to model a system so as to be able to explain it well
Understanding Systems at Different Scales: And finally, this skill is the ability to understand systems at different scales. Each element of a system may be a system itself, and the system being viewed is probably an element in a more complex system. Being aware of these details can lead to better insights
Now that we know all the elements, we can see what the relationships between them are like. The easiest way to understand it is to map it out (adapted from Arnold and Wade, 2015).
This is a slightly complicated map. In short, the thick arrows with solid borders represent strong relationships, and the thin arrows with dotted borders represent weak relationships. The strong relationships are as follows:
Understanding system structure strongly enhances understanding of dynamic system behaviour
Understanding a system's structure leads to developing conceptual models properly
A conceptual understanding of the system may lead to insights about the system's role in as an element in bigger systems as well as the complexity of its constituent elements
The weak relationships come out thus:
Understanding dynamic behaviour of a system may prompt one to go back and take a second look at the elements and relationships already identified
It is often worth going back and re-identifying elements and relationships when making conceptual systems
Understanding a system conceptually makes it easier to identify and seek out dynamic behaviour
Once a person is able to start understanding systems at different scales, it can reveal additional elements and relationships one might not have picked up at the start of the exercise
Understanding dynamic behaviour can allow the incorporation of dynamic models when creating a conceptual system
Knowledge of the different scales at which a system operates can help in enhancing the accuracy of any conceptual models one might like to use
All the four major elements in the diagramme strongly improve one's ability to identify systems, predict their behaviours, and devise modifications if and when needed.
Definition of systems thinking
So now that we have come this far, we can understand the definition of systems thinking:
Systems thinking is a set of synergistic analytic skills used to improve the capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects. These skills work together as a system. - Arnold and Wade (2015)
These analytical skills and their interconnections are easy enough to understand, and they make complete sense when seen from the lens of systems thinking itself.
In the next post in this series, we shall look at some systems thinking frameworks used in healthcare followed by an intervention strategy informed by the same.
Arnold, R.D. and Wade, J.P., 2015. A definition of systems thinking: A systems approach. Procedia computer science, 44(2015), pp.669-678. ↩︎
Stave, K. and Hopper, M., 2007, July. What constitutes systems thinking? A proposed taxonomy. In 25th International Conference of the System Dynamics Society. ↩︎