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Measuring complexity

Research output: Contribution to journalArticle

Original languageEnglish
Number of pages11
JournalarXiv
DateAccepted/In press - 29 Sep 2019

Abstract

Complexity is heterogenous, involving nonlinearity, self-organisation, diversity, adaptive behaviour, among other things. It is therefore obviously worth asking whether purported measures of complexity measure aggregate phenomena, or individual aspects of complexity and if so which. This paper uses a recently developed rigorous framework for understanding complexity to answer this question about measurement. The approach is two-fold: find measures of individual aspects of complexity on the one hand, and explain measures of complexity on the other. We illustrate the conceptual framework of complexity science and how it links the foundations to the practised science with examples from different scientific fields and of various aspects of complexity. Furthermore, we analyse a selection of purported measures of complexity that have found wide application and explain why and how they measure aspects of complexity. This work gives the reader a tool to take any existing measure of complexity and analyse it, and to take any feature of complexity and find the right measure for it.

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10 pages

    Research areas

  • nlin.AO

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  • Full-text PDF (submitted manuscript)

    Rights statement: This is the submitted manuscript (SM). It first appeared online via arXiv at https://arxiv.org/abs/1909.13243v1. Please refer to any applicable terms of use of the author.

    Submitted manuscript, 169 KB, PDF document

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