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Measuring complexity in organisms and organizations
Author(s) -
Nancy Rebout,
JeanChristophe Lone,
Arianna De Marco,
Roberto Cozzolino,
Alban Lemasson,
Bernard Thierry
Publication year - 2021
Publication title -
royal society open science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 51
ISSN - 2054-5703
DOI - 10.1098/rsos.200895
Subject(s) - flexibility (engineering) , social complexity , computer science , complexity index , basis (linear algebra) , complex system , diversity (politics) , computational complexity theory , theoretical computer science , management science , mathematics , artificial intelligence , algorithm , statistics , sociology , boolean function , social science , geometry , anthropology , economics
While there is no consensus about the definition of complexity, it is widely accepted that the ability to produce uncertainty is the most prominent characteristic of complex systems. We introduce new metrics that purport to quantify the complexity of living organisms and social organizations based on their levels of uncertainty. We consider three major dimensions regarding complexity: diversity based on the number of system elements and the number of categories of these elements; flexibility which bears upon variations in the elements; and combinability which refers to the patterns of connection between elements. These three dimensions are quantified using Shannon's uncertainty formula, and they can be integrated to provide a tripartite complexity index. We provide a calculation example that illustrates the use of these indices for comparing the complexity of different social systems. These indices distinguish themselves by a theoretical basis grounded on the amount of uncertainty, and the requirement that several aspects of the systems be accounted for to compare their degree of complexity. We expect that these new complexity indices will encourage research programmes aiming to compare the complexity levels of systems belonging to different realms.

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