The Hub of Being (16-2-2) Complexity

 Complexity

We naturally assume that we perceive things as they are. There is magic behind that process.

The world is comprehensible only because our perceptions transform sensations into a state where they may be understood. Science has shown actuality to be infinitely complex, while paradoxically still striving to construe laws of Nature, which can only be simplifying heuristics (rules of thumb).

Input-output maps are ubiquitously employed to understand natural mechanics, including physics and biology. As the maps necessarily involve quantized units, represented as numbers, these models are intrinsically discrete, but are often used to comprehend continuous systems. Calculus is exemplary in being able to analyze holistic systems through discrete differentiation.

(Calculus was conceived in the late 17th century via the idea of infinitesimals: atomic quantities too small to measure. 2 centuries later, this same concept was pivotal in the quantization discoveries of Max Planck which led to modern physics.)

Without knowing details about a map, there may seem to be no a priori reason to expect that a randomly chosen input would be more likely to generate one output over another. ~ British mathematical biologist Kamaludin Dingle et al

Mathematics itself appears biased to fabricate simplicity: models inherently tend to generate simple outputs at an exponentially higher probability than complex outputs.

Simplicity bias – that simple outputs are exponentially more likely to be generated than complex outputs are – holds for a wide variety of systems in science and engineering. ~ English theoretical physicist Ard Louis