Usually, the complexity of systems, from satellite motions to heart rhythms, is assessed with averaged entropy (a measure of unpredictability, as understood by Boltzmann). But that's like evaluating the ocean only by wave height: the average figure hides the full richness of underwater currents.
The CWMMSE method dives deeper. It automatically groups similar trajectories, as if isolating distinct currents in the ocean, and computes the complexity for each one. Then the overall picture is assembled from these pieces, with the differences between currents being just as important, like warm and cold flows combining to create something greater than the sum of their parts.
This explains paradoxes: in heart disease, rhythm diversity drops even though averaged entropy stays unchanged. And an earthquake can be seen as a collapse of complexity — many small underground movements suddenly merging into one giant jolt.
Thus the method reminds us: in the ocean, organism, or society, the value lies not in average chaos, but in the hidden mosaic of patterns.
Entropy, originally devised for steam engines in the 19th century, now reveals the hidden rhythms of hearts.
Just as Asimov's psychohistory predicts the fate of civilizations by capturing the statistical patterns of crowds, CWMMSE sees order in the chaos of many trajectories.