Assembly Theory and its Relationship with Computational Complexity
Christopher P. Kempes, Michael Lachmann, Andrew Iannaccone, G. Matthew, Fricke, M. Redwan Chowdhury, Sara I. Walker, Leroy Cronin

TL;DR
Assembly theory offers a unique framework for quantifying complexity based on physical assembly processes, contrasting with traditional computational complexity and information theory measures.
Contribution
This paper formally compares assembly theory's measures with classical computational complexity and information measures, highlighting fundamental differences and providing mathematical examples.
Findings
Assembly index is not equivalent to Shannon entropy, Huffman encoding, or Lempel-Ziv-Welch compression.
Assembly index belongs to a different computational complexity class than these compression algorithms.
Assembly theory emphasizes physical observables and causation, differing from abstract computational models.
Abstract
Assembly theory (AT) quantifies selection using the assembly equation and identifies complex objects that occur in abundance based on two measurements, assembly index and copy number, where the assembly index is the minimum number of joining operations necessary to construct an object from basic parts, and the copy number is how many instances of the given object(s) are observed. Together these define a quantity, called Assembly, which captures the amount of causation required to produce objects in abundance in an observed sample. This contrasts with the random generation of objects. Herein we describe how AT's focus on selection as the mechanism for generating complexity offers a distinct approach, and answers different questions, than computational complexity theory with its focus on minimum descriptions via compressibility. To explore formal differences between the two approaches, we…
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Taxonomy
TopicsManufacturing Process and Optimization
