Editing Complexity (section)

=== Data ===
In [[information theory]], algorithmic information theory is concerned with the complexity of strings of [[data]].

Complex strings are harder to compress. While intuition tells us that this may depend on the [[codec]] used to compress a string (a codec could be theoretically created in any arbitrary language, including one in which the very small command "X" could cause the computer to output a very complicated string like "18995316"), any two [[Turing completeness|Turing-complete]] languages can be implemented in each other, meaning that the length of two encodings in different languages will vary by at most the length of the "translation" language – which will end up being negligible for sufficiently large data strings.

These algorithmic measures of complexity tend to assign high values to [[signal noise|random noise]]. However, under a certain understanding of complexity, arguably the most intuitive one, random noise is meaningless and so not complex at all.

[[Information entropy]] is also sometimes used in information theory as indicative of complexity, but entropy is also high for randomness. In the case of complex systems, [[information fluctuation complexity]] was designed so as not to measure randomness as complex and has been useful in many applications. More recently, a complexity metric was developed for images that can avoid measuring noise as complex by using the minimum description length principle.<ref>Mahon, L.; Lukasiewicz, T. (2023). "[https://www.sciencedirect.com/science/article/pii/S0031320323005873 Minimum Description Length Clustering to Measure Meaningful Image Complexity]". Pattern Recognition, 2023 (144).</ref>