Allotaxonometry and rank-turbulence divergence: a universal instrument for comparing complex systems
Name
13688_2023_Article_400.pdf
Size
5.45 MB
Format
Adobe PDF
Checksum (MD5)
217c81c811d4d82afa7f8e6b4a0cb073
Author(s)
Dodds, Peter S.
Minot, Joshua R.
Arnold, Michael V.
Alshaabi, Thayer
Adams, Jane L.
Dewhurst, David R.
Gray, Tyler J.
Frank, Morgan R.
Reagan, Andrew J.
Danforth, Christopher M.
Date Issued
September 19, 2023
Publisher
Springer Berlin Heidelberg
Citation
EPJ Data Science. 2023 Sep 19;12(1):37
Version
Final published version
Abstract
Abstract
Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1140/epjds/s13688-023-00400-x
