TY - JOUR
T1 - Dynamics of ranking
AU - Iñiguez, Gerardo
AU - Pineda, Carlos
AU - Gershenson, Carlos
AU - Barabási, Albert László
N1 - | openaire: EC/H2020/952026/EU//HumanE-AI-Net
Funding Information:
In memory of Jorge Flores and Germinal Cocho. We acknowledge José A. Morales and Sergio Sánchez for data handling at the start of the project. We are grateful for data provision to Gustavo Carreón, Syed Haque, Kay Holekamp, Amiyaal Ilany, Márton Karsai, Raj Kumar Pan, Roberto Murcio, and Roberta Sinatra. G.I. thanks Tiina Näsi for valuable suggestions. G.I. acknowledges support from AFOSR (#FA8655-20-1-7020), project EU H2020 Humane AI-net (#952026), and CHIST-ERA project SAI (#FWF I 5205-N). C.P. and C.G. acknowledge support by CONACyT (#285754) and UNAM-PAPIIT (#IG100518, IG101421, IN107919, and IV100120). C.G. was also supported by the PASPA program from UNAM-DGAPA. A.-L.B. was supported by an EU H2020 SYNERGY grant (#810115-DYNASNET), the John Templeton Foundation (#61066), and AFOSR (#FA9550-19-1-0354).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/3/28
Y1 - 2022/3/28
N2 - Virtually anything can be and is ranked; people, institutions, countries, words, genes. Rankings reduce complex systems to ordered lists, reflecting the ability of their elements to perform relevant functions, and are being used from socioeconomic policy to knowledge extraction. A century of research has found regularities when temporal rank data is aggregated. Far less is known, however, about how rankings change in time. Here we explore the dynamics of 30 rankings in natural, social, economic, and infrastructural systems, comprising millions of elements and timescales from minutes to centuries. We find that the flux of new elements determines the stability of a ranking: for high flux only the top of the list is stable, otherwise top and bottom are equally stable. We show that two basic mechanisms — displacement and replacement of elements — capture empirical ranking dynamics. The model uncovers two regimes of behavior; fast and large rank changes, or slow diffusion. Our results indicate that the balance between robustness and adaptability in ranked systems might be governed by simple random processes irrespective of system details.
AB - Virtually anything can be and is ranked; people, institutions, countries, words, genes. Rankings reduce complex systems to ordered lists, reflecting the ability of their elements to perform relevant functions, and are being used from socioeconomic policy to knowledge extraction. A century of research has found regularities when temporal rank data is aggregated. Far less is known, however, about how rankings change in time. Here we explore the dynamics of 30 rankings in natural, social, economic, and infrastructural systems, comprising millions of elements and timescales from minutes to centuries. We find that the flux of new elements determines the stability of a ranking: for high flux only the top of the list is stable, otherwise top and bottom are equally stable. We show that two basic mechanisms — displacement and replacement of elements — capture empirical ranking dynamics. The model uncovers two regimes of behavior; fast and large rank changes, or slow diffusion. Our results indicate that the balance between robustness and adaptability in ranked systems might be governed by simple random processes irrespective of system details.
UR - http://www.scopus.com/inward/record.url?scp=85127234299&partnerID=8YFLogxK
U2 - 10.1038/s41467-022-29256-x
DO - 10.1038/s41467-022-29256-x
M3 - Article
C2 - 35347126
AN - SCOPUS:85127234299
VL - 13
SP - 1
EP - 7
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 1646
ER -