Complexity as defined in most scientific disciplines today refers to systems which are "greater than the sum of their parts," i.e., where the behavior of the wh

Complexity as defined in most scientific disciplines today refers to systems which are "greater than the sum of their parts," i.e., where the behavior of the whole system cannot be predicted by looking at each component in isolation, but only can be understood by the action and interaction between components and how that evolves over time. Most systems of interest are too big to enumerate all the possible n-ary interactions between constituent parts, so we must study them by other means: probe plausible simulations, define and compute statistics from observable data, or fit models which capture plausible correlations. To complicate this further, it usually takes just one cycle or feedback loop in the network / system to introduce enough complexity that most traditional analytical mathematical tools break down. I studied complex (adaptive) systems as my Master's education and can confirm most techniques reduce to "linearize around the important bits and extrapolate from there". Examples of complex systems include: social networks (IRL and online), economies, biological systems. Machine learning is one of the most promising tools for probing these because if we can fit an appropriate model, we can hopefully capture enough of the behavior that we can reliably extrapolate to unseen data / states.