Interaction between order and randomness
The unpredictability of a system does not mean the absence of order as the name of chaos theory implies; it means something more like yin and yang: a confusing interaction between order and randomness. The natural shape of chaos takes the form of strange attractors:
- “strange” meaning the complex geometry of unpredictability;
- “attractor” meaning the system’s long-term mode of behavior, the point to which a system returns after a disturbance, like a homeostasis or equilibrium.
The order found in chaos makes it possible to harness chaos for real-world applications (Strogatz, 2008; Gleick, 2008).
The unpredictable behavior in dynamical systems presents a paradox in chaos theory: unpredictability that follows deterministic laws. Strogatz (2008) called this “deterministic predictability” (p. 13). Deterministic means that the present determines the future and that the laws of nature determine what will happen next, with only one possible future resulting from current conditions. This shifts the focus from laws of nature to consequences of laws, and “finding clever ways to infer consequences” (p. 16).
Capra (1996) said that chaos theory does not make predictions impossible, but it does put predictability in the “qualitative features of the system’s behavior rather than the precise values of its variables at a particular time” (p. 134). This shift from quantity to quality became a key feature of systems thinking. While math uses formulas to quantify reality, dynamical system theories look for patterns to recognize the qualitative aspects of reality that depend on the perspective of the observer and the horizon of predictability.
Predictability window
From the perspective of traditional science and humanity, Haley’s comet, eclipses, the solar system all seem deterministic, predictable, because they have long predictability windows. However, determinism plus periodicity determines the horizon of predictability, which Lorentz (1963) called deterministic non-periodic flow. This horizon of predictability is different depending on the system. The horizon of predictability for electricity is milliseconds, the weather is a few days, and the solar system is five million years (Strogatz, 2008). Since humans exist within the predictability horizon they can use Newtonian physics to track and predict events. However, Newtonian mechanics leads to generalizations about predictability that chaos theory disproves by explaining that even the most accurate measurements cannot forecast outside of predictability windows (Strogatz, 2008; Gleick, 2008).
Viewing the real world through chaos
The concepts of chaos theory helped to explain why the government’s weather control experiments failed in the 1960s (Gleick, 2008). As a dynamical system with high sensitivity to initial conditions and a short predictability window, general patterns of weather can be assumed but the weather cannot be predicted. Even if people could change the weather, Lorenz (1963) asserted, it would be impossible to determine what weather would have done otherwise or if the changes were for better or worse.