In a previous post, I mentioned that the universe is deterministic, and predictable based on the laws of physics: if you know the properties of a system, and the laws of physics, then you can perfectly predict its future. This is true to a certain extent, but is somewhat simplistic in that it fails to account for the significance of chaos.
The idea of chaos was first proposed in the 1800s by Henri Poincaré, a mathematical legend. In trying to solve the three-body problem, he discovered that small, minute differences at an initial state of a system would lead to huge differences at a later state — an idea popularised as the ‘Butterfly Effect’. But Chaos Theory was only formalised when a meteorologist running a weather simulation noticed massively diverging outcomes every time he ran the simulation — though the inputs were constant, the results were different each time. Minuscule differences in initial conditions, like tiny rounding errors, would lead to enormously different predictions of the weather.
This is because the simulation was operating within the nonlinear system of the universe — in a nonlinear system, a change in the output is not proportionate to a change in input (think polynomial functions). But the universe is deterministic — it is subject to the laws of physics. So the deterministic nature of a system does not make a system predictable. The universe is written, but incalculable.
The present determines the future, but the approximate present does not approximately determine the future. Such is the effect of chaos.
This relatively modern theory of physics is consistent with the ancient Vedantic idea of Satkaryavarta: the idea that the origination and evolution of the universe can be explained through a theory of causation, wherein the manifested effect is pre-existent in the cause. Without a cause, there is no effect, and effects are simply causes in a different form.
Chaos theory reveals the significance of abstractions: using inexact values as inputs in a non-linear system can yield undesirable, disproportionate consequences. A tangible example is the messiness of arguments. In a 1v1 argument, an emotionally-charged arguer (Arguer 1) is likely to unleash an accusation with multiple facets and layers. Assuming Arguer 2 is a human, she has limited a working-memory capacity, and is restricted in her ability to maintain several 1-1 correlations to sequentially prepare a response to each stimulus. So the accusation is mentally represented as a general abstraction of the nuanced arguments presented—inexact inputs—to which Arguer 2 responds, incomprehensively, inaccurately, and additively, generating her own multi-faceted response and perpetuating a spiral. Chaos.