MoL-2018-06: Algorithmic Complexity in Textile Patterns

MoL-2018-06: Metzler, Heidi (2018) Algorithmic Complexity in Textile Patterns. [Report]

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Algorithmic complexity, also called Kolmogorov complexity and Kolmogorov-Chaitin complexity, motivates the use of techniques to approximate the complexity of objects and measure similarity between them. This thesis explores the application of these methods to patterns in textiles. A brief history of the relevance of textile production to computability is given and it is shown that Turing Machines can be simulated by knitting. Approximations of algorithmic complexity indicate that there may be a way to distinguish meaningful information from arbitrarily populated matrices. The Turkmen tribes were nomadic people with a social structure that allowed woven ornaments to change independently of one another over time. Turkmen textiles present a difficult classification puzzle. The Normalized Compression Distance is a parameter-free, feature-free metric used to measure similarity between objects given approximations of their algorithmic complexity. This technique generates an evolutionary tree consistent with historical information on Turkmen tribes. This demonstrates how algorithmic complexity can be usefully employed in the areas of material culture, archeology, and art history.

Item Type: Report
Report Nr: MoL-2018-06
Series Name: Master of Logic Thesis (MoL) Series
Year: 2018
Subjects: Language
Depositing User: Dr Marco Vervoort
Date Deposited: 02 Aug 2018 15:22
Last Modified: 16 Aug 2018 13:51

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