Measuring complexity
Karoline Wiesner, James Ladyman
TL;DR
This paper reviews a rigorous framework for measuring complexity, illustrating how various features can be quantified and evaluating existing measures to determine their effectiveness in capturing complexity.
Contribution
It provides a comprehensive analysis of complexity measures using a new framework, clarifying their capabilities and limitations across different scientific contexts.
Findings
Some measures effectively quantify disorder and nonlinearity.
Certain classical information-theoretic measures are limited in capturing complexity.
The framework offers a practical toolkit for analyzing complexity in diverse systems.
Abstract
Complexity is a multi-faceted phenomenon, involving a variety of features including disorder, nonlinearity, and self-organisation. We use a recently developed rigorous framework for complexity to understand measures of complexity. We illustrate, by example, how features of complexity can be quantified, and we analyse a selection of purported measures of complexity that have found wide application and explain whether and how they measure complexity. We also discuss some of the classic information-theoretic measures from the 1980s and 1990s. This work gives the reader a tool kit for quantifying features of complexity across the sciences.
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Computational Drug Discovery Methods · Complex Network Analysis Techniques