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Roots and spiders make the world go ’round?

How systems thinking can be used to conceptualise complex systems.
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We must consider systems in a nuanced way – including the ones we sit within.

At a basic level, a system is a set of things working together as parts of a mechanism or as an interconnecting network. Yet there is more to it than that. Enter systems thinking.

Systems thinking

‘Systems thinking’ suggests that learning the component parts of complex systems is not enough to understand the whole. In essence, complex systems are more than the sum of their parts, and can behave in ways that are non-linear and unpredictable. Changes seem to ‘emerge’ from complex systems in ways that are not easily traceable, nor can be explained by their component parts. This makes interacting with – and in – the systems quite challenging.

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Instead of thinking hierarchically, social scientists and philosophers explore how networks can represent decentralised nodes and pathways of power, in ways that provide an interconnected model of life. Here, individuals and organisations can exert influence through their strategic positioning within the various networks they are part of, influencing and being influenced by those that they are connected to.

Somewhat infamously (in academic circles anyway), the metaphor of the rhizome is used by philosophers Deleuze and Guattari, to describe complex systems.

In botany, ‘rhizome’ refers to a horizontal plant stem that grows underground, sending out roots and shoots randomly. Unlike a typical vertical plant stem that grows from a centralised root towards the sun, rhizomes seem to run amok.

Deleuze and Guattari thus conceptualise the rhizome as a model for a decentralised, intricate, and multi-dimensional method of thinking (and writing). We might visualise this in contrast to centralised, structured, ‘tree-like’ thinking, which starts from a core principle and grows outwardly.

We can also use the metaphor of a spiderweb. From our interrelated web of relations, any tremors on one strand reverberate across to others, informing us of a need to act.

Within both of these metaphors, we find a common concept: feedback. Feedback is how networks relay signals to other parts. When something happens to one part of the network, it influences another in ways that might – or might not – be predictable.

The study of this type of feedback – to communicate and control system outputs and direction – is called ‘cybernetics’. It provides a mechanistic view of how such things work. You can steer entire systems by providing specific inputs and expecting certain feedback mechanisms to shift the outcomes.

Systems thinking in practice?

In an age of accelerating global and digital relations (Castells 2010) it’s easy to consider how complex systems can be understood through the idea of a network; people have transposed how neurons or computers interact in an attempt to explain the more diverse global systems that seem to be functioning without one central point of control.

This type of thinking is prevalent in policy, especially ‘evidence-based policy’, that takes a technocratic view of how to steer society to better outcomes.

For example, consider an area of fisheries that is about to collapse, both ecologically and economically. In our thought experiment, it’s clear that overfishing is removing more fish than can be replenished in the ecosystem. So, the fix is to work with the fisheries as a system.

As the clever neoliberal policy maker, you institute a market, not for fish, but for fishing. You do this by capping the amount of fishing licences. Licences can be traded. This mechanism regulates the fishers and means that more fish are sticking around to make more (and then even more) fish.

Initial system: amount of fishing affects amount of consumption, which affects amount of fishers, which affects amount of fish. Intervened system: amount of fish affects amount of licences, which affects both amount of fishers and amount of fishing. Created by Luke Heemsbergen (2023); © Deakin University.

With this decision, you’ve turned the dial to create a positive feedback loop on fish stock and steered away from disaster. Great job!

Now let’s consider microcredit schemes for helping poor people of developing nations. If we reduce the complexity, we can see that flows of money in an economic system involve credit. Credit is acquired through a combination of leveraging assets and having the social capital to successfully engage in the system (i.e. it seems like a good investment to those lending the money).

Yet, credit often does not reach the poor, because they don’t have substantial assets nor sufficient social capital. So what is to be done?

Microcrediting is one solution. Instead of relying on the economic system that ignored the poor, you finance a new microcredit system that lends out small amounts to poor entrepreneurs, or even modifies the flows of the system to lend money between them. You create a new set of system mechanics that create new flows and networks (specifically, financial ones).

Microcrediting is one solution. Instead of relying on the economic system that ignored the poor, you finance a new microcredit system that lends out small amounts to poor entrepreneurs, or even modifies the flows of the system to lend money between them. You create a new set of system mechanics that create new flows and networks (specifically, financial ones). See Figure 2 (p. 14) of Nghiem (2005), The Conceptual Framework of Efficiency and Effectiveness: Microfinance Application for a map of what this system could look like.

Nice stories, right? We’ve solved overfishing and the poor are getting richer?

Unfortunately, these are examples of clumsy systems thinking that reinforce the assumptions of the policy makers who designed them. The power relations involved – between policy makers, fishers, and even the fish(!) – were not adequately considered. Microcredit still puts power in the hands of the creditors, and debt obligations can lock people into debilitating cycles of loan and interest repayments.

Clearly, people – and the planet – are not spider webs or computer networks or diagrams like the above. Such fixes to fisheries and poverty are going to be even more complex than their pieces suggest. Thus, we should also avoid reducing the world to diagrams and metaphors. Perhaps truly ‘rhizomatic’ thinking would avoid using the word ‘rhizome’ itself?

While cybernetic thinking about systems is relatively new (Weiner 1948), there are deeper and older ways of understanding systems that do not force simplification. We will continue to explore them in the next steps.


Donella Meadows was one of the pioneers of thinking about challenges as systems. Watch a portion (34:30–37:00) of her following lecture, and follow the instructions to draw a very basic system.

This is an additional video, hosted on YouTube.

  1. First, sketch a diagram of what you think is a simple cause and effect relationship, where (A) causes (B). For example, more fishingless fish.
  2. Now, think about how (B) comes back around to influence (A). This means trying to draw in missing feedback loops (positive or negative). For example, less fishless demandless fishersmore fish.

Upload your map to the [Padlet]. (Because this is a simple map, it’s okay to draw it by hand and then upload a photo of it.)


Castells, M. (2010). The rise of the network society. Wiley-Blackwell.

Deleuze, G., & Guattari, F. (1987). A thousand plateaus : capitalism and schizophrenia. University of Minnesota Press.

Nghiem, H. S. (2005). Analysing the effectiveness of microfinance in Vietnam: A conceptual framework (The Conceptual Framework of Efficiency and Effectiveness: Microfinance Application). School of Economics, The University of Queensland.

Wiener, N. (1948). Cybernetics : or, Control and communication in the animal and the machine. MIT Press.

© Deakin University
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