I decided to chase "how are they measuring randomness?" down, and: - the study mentions using the "acss" R package in the Methods section: https://journals.plo

I decided to chase "how are they measuring randomness?" down, and: - the study mentions using the "acss" R package in the Methods section: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005408 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo... - acss's documentation directs you to another site for describing the metric: https://www.rdocumentation.org/packages/acss/versions/0.2-5/topics/acss https://www.rdocumentation.org/packages/acss/versions/0.2-5/... - that site links to some other articles in its bibliography at the bottom, and mentions using "algorithmic probability" to approximate complexity: https://complexitycalculator.com/methodology.html https://complexitycalculator.com/methodology.html - the first of which is this, which describes algorithmic probability over halting 2-symbol 4-state Turing machines by exhaustive execution: https://www.sciencedirect.com/science/article/abs/pii/S0096300311012367 https://www.sciencedirect.com/science/article/abs/pii/S00963... So assuming I followed all that correctly: Your strings are more complex if there are fewer Turing machines that produce it, which is then normalized to the average of all strings to become "randomness". Too few or too many ways to create a string means less random, within this category of simple and very small Turing machines (otherwise it'd be impractical to compute). I have no idea how broadly applicable that metric is though. It seems fairly niche to my complete amateur reading... but AFAICT all Kolmogorov complexity measures are extremely niche, as it's extremely sensitive to what the execution environment is. But this was still an interesting rabbit hole. Figured I'd share:)