R. RUTHEN writes: "Some of the best ideas for defining and measuring complexity emerged from the work of Claude E. SHANNON, Andrei N. KOLMOGOROV and Gregory J. CHAITIN during the 1950s and 1960s. They proposed that the degree of complexity is related to the size of the smallest description of a system's behavior. This theory led to a proposed technique for measuring the so-called algorithmic complexity of a process. In theory, the complexity of two systems could be compared by writing two computers programs that are the shortest capable of reproducing the original data… The program with the fewer instructions would describe the less complex system" (1993, p.114).
The same idea was expressed by V.G. DROZIN: "The complexity of a system can also be expressed by the number of steps of a prescription or algorithm needed to assemble the hole from its parts& Indeed, the greater the variety of parts and their number,… the longer will be the algorithm needed for its assembly".
And still: "(The) expressions of complexity of a system are reducible to the number of its non-interchangeable states and can be linked to the negentropy content of the system" (1975, p.9).
An original view is R.V. JENSEN's, who states: "The theory of algorithmic complexity reveals that the origin of chaotic behavior in nonlinear dynamical systems and perhaps in nature itself lie in the randomness of almost all real numbers" (1987, p.178).
However, if a system is very complex (or if the level of our analysis is high) we are led to algorithmic incompressibility, or in other cases, to the practical impossibility to compute because we would (in accordance with BREMERMANN'S limit) "literally have to wait from now to eternity to find out the results" (Ibid).
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Bertalanffy Center for the Study of Systems Science (2020). Title of the entry. In Charles François (Ed.), International Encyclopedia of Systems and Cybernetics (2). Retrieved from www.systemspedia.org/[full/url]
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