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Computer Science > Information Theory

Title: Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution

Abstract: We demonstrate that Assembly Theory, pathway complexity, the assembly index, and the assembly number are subsumed and constitute a weak version of algorithmic (Kolmogorov-Solomonoff-Chaitin) complexity reliant on an approximation method based upon statistical compression, their results obtained due to the use of methods strictly equivalent to the LZ family of compression algorithms used in compressing algorithms such as ZIP, GZIP, or JPEG. Such popular algorithms have been shown to empirically reproduce the results of AT that were reported before in successful application to separating organic from non-organic molecules and in the context of the study of selection and evolution. We prove the connections and full equivalence of Assembly Theory to Shannon Entropy and statistical compression, and AT's disconnection as a statistical approach from causality. We demonstrate that formulating a traditional statistically compressed description of molecules, or the theory underlying it, does not imply an explanation or quantification of biases in generative (physical or biological) processes, including those brought about by selection and evolution, when lacking in logical consistency and empirical evidence. We argue that in their basic arguments, the authors of AT conflate how objects may assemble with causal directionality, and conclude that Assembly Theory does not explain selection or evolution beyond known and previously established connections, some of which are reviewed.
Comments: 15 pages + appendix, 2 figures
Subjects: Information Theory (cs.IT); Biomolecules (q-bio.BM)
Cite as: arXiv:2403.06629 [cs.IT]
  (or arXiv:2403.06629v4 [cs.IT] for this version)

Submission history

From: Hector Zenil [view email]
[v1] Mon, 11 Mar 2024 11:39:08 GMT (1614kb,D)
[v2] Wed, 13 Mar 2024 15:53:13 GMT (1614kb,D)
[v3] Thu, 14 Mar 2024 01:48:39 GMT (1614kb,D)
[v4] Tue, 19 Mar 2024 21:50:44 GMT (1615kb,D)
[v5] Wed, 27 Mar 2024 09:06:08 GMT (2866kb,D)
[v6] Mon, 1 Apr 2024 17:56:13 GMT (3355kb,D)

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