Assembly Theory and its Relationship with Computational Complexity

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. The assembly index is determined by the minimal number of recursive joining operations necessary to construct an object from basic parts, and the copy number is how many of the given object(s) are observed. Together these allow defining a quantity, called Assembly, which captures the amount of causation required to produce the observed objects in the sample. AT's focus on how selection generates complexity offers a distinct approach to that of computational complexity theory which focuses on minimum descriptions via compressibility. To explore formal differences between the two approaches, we show several simple and explicit mathematical examples demonstrating that the assembly index, itself only one piece of the theoretical framework of AT, is formally not equivalent to other commonly used complexity measures from computer science and information theory including Huffman encoding and Lempel-Ziv-Welch compression.