Viewing human behaviour through feedback cycles might seem cold and mechanical at first glance. However, feedback cycles belong to the realm of chaos theory, the study of complexity itself. Within these complex systems, boundaries are intricate rather than straight lines, patterns repeat across vastly different scales, and individual parts can resemble the whole, just as a single floret of broccoli looks like the entire head of broccoli. Also, prediction becomes difficult because microscopic differences in a system can drastically alter its trajectory. So, understanding human behaviour through feedback cycles is being open to chaos theory, a realistic lens for human nature
A Simple Population Model
Feedback can create complexity.
Consider a simple feedback population model for the number of rabbits on an island. The model uses this year’s population to predict next year’s population.
In this cycle:
- The number of rabbits becomes the input to an equation giving the next year’s population
- The model updates the number of rabbits, and the cycle repeats.
Here is the equation setting the population:
- The population in the current year = P
- The population next year = PN
- The population fertility = F
- The population can vary between 0 and 1
- Multiplication: *
- Subtraction: –
- PN = F * P * (1- P)
This is the “Logistic Equation”, an elegantly simple population model. Its definition is so clear and simple that you’d expect the output from the model to be predictable and almost useless.
For many values of fertility, the population follows a regular, repeating pattern. However, this equation makes the population (1) smaller when it gets large, and (2) larger when it gets small, and this is a recipe for chaos. Many fertility values, e.g., F = 3.5801, produce infinite complexity and even unpredictable chaos. Different computers using this equation can give different results (Stewart, p. 159). It is counterintuitive.
This complexity arises because the model uses the equation repeatedly in a feedback loop.
Now consider the human feedback cycle linking dread, defence, and damage.
The pairwise interactions between these three factors are far more intricate than any simple population equation, and the model uses these complex interactions repeatedly in a feedback loop. Analysing human behaviour through the lens of feedback systems is anything but one-dimensional.
This is one of the paradoxes of chaos theory: repeatedly applying simple non-linear equations that govern a system can create complexity and unpredictability.
The Logistic Map / Population model: Wikipedia
The Mandelbrot Set
The mathematical formula behind the Mandelbrot set also uses feedback and involves simple algebra.
You can see the complexity of this simple mathematical feedback in the moving image of the Mandelbrot set on the Wikipedia “Mandelbrot Set” page. As the video zooms into an area of the Mandelbrot diagram, you see the same patterns repeated again and again, and the whole Mandelbrot image keeps reappearing at a smaller scale. This repetition of pattern is ornate and beautiful.
The Classical Paradigm
The great eighteenth-century scientists thought they had discovered the immutable laws of a universe that runs like clockwork, but they were quite wrong… Nothing in the universe ever behaves in a way that is totally predictable… To cope with a chaotic world, pioneering mathematicians have developed chaos theory. (Ian Stewart, 1990, Does God play dice )
In the classical scientific view, when you know a system’s initial conditions and the laws governing it, you can predict its future behaviour. This worldview treats the universe like a clockwork mechanism: precise, orderly, and predictable.
The Chaos Theory Paradigm
Chaos theory challenges this assumption. It shows that while some regions of a system may be simple and predictable, other regions are so complex that it is impossible to know initial conditions accurately enough to forecast what will happen next. This is what happens in the rabbit population example above.
The famous butterfly-effect metaphor captures this sensitivity to initial conditions: The flap of a butterfly’s wings in Brazil may lead to a tornado in Texas. The insight is that a very small change can lead to very large consequences.
The butterfly effect actually reflects everyday experience. A chance meeting, a single decision, or an unfortunate stumble can redirect a person’s life.
Even systems once thought to be perfectly predictable are not immune to this uncertainty. While planetary motion appears regular in the short term, over long time scales, even the movement of planets is unpredictable.
Amplifying feedback
Amplifying feedback explains how such dramatic change can occur. Deviation-amplifying processes can reinforce almost imperceptible changes rather than extinguishing them. This allows a minute change to cascade into a major transformation, exactly the dynamic suggested by the butterfly effect.
Upending the law of causality
As cybernetician Magoroh Maruyama observed:
“A sacred law of causality in classical philosophy stated that similar conditions produce similar effects. … In the light of the deviation-amplifying mutual causal process, the law of causality is now revised to state that similar conditions may result in dissimilar products.” (Maruyama, 1968)
Little things can be enough
This perspective is optimistic for those who seek change. It suggests that transformation does not always require massive effort or dramatic intervention. Apparently, small things, like attentive listening, a new insight, or developing a new habit, can initiate an amplifying process that reshapes a life.
References
- Stewart, Ian (1990) Does God Play Dice: The New Mathematics of Chaos, London, Penguin.
- Wikipedia has good articles on Chaos Theory and the Mandelbrot Set
Related pages
Loaded 13 Jan 2026. Modified 10 September 2026.
Feature Image: Banner: Chaos & Amplifying Feedback