Dynamics of ranking

Gerardo Iñiguez, Carlos Pineda, Carlos Gershenson & Albert-László Barabási 

Nature Communications volume 13, Article number: 1646 (2022)

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.

Read the full article at: www.nature.com

How to Make Things Evolve by Hiroki Sayama


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The idea of creating artifacts that evolve by themselves has been at the heart of the Artificial Life research, dating back to the early motives of John von Neumann’s monumental work on self-reproducing automata in the 1940’s. This vein of research is unique and fundamentally different from other more widely studied evolutionary computation research, because basic processes of evolution (heredity, variation, selection) are not given a priori as built-in mechanisms but they need to emerge as a result of interactions among microscopic components. In this talk, I will provide a brief review of how this problem has been approached in ALife using various kinds of methodologies, including classic frameworks (e.g., cellular automata, evolving programs) and more modern ones (e.g., artificial chemistry, AI/ML). I aim to highlight several key ingredients in order for complex systems to show spontaneous evolutionary behaviors by themselves and, in particular, to exhibit open-ended exploration of the possibility space.

Watch at: www.youtube.com

Intermunicipal Travel Networks of Mexico (2020-2021)

Oscar Fontanelli, Plinio Guzmán, Amílcar Meneses, Alfredo Hernández, Marisol Flores-Garrido, Maribel Hernández-Rosales, Guillermo de Anda-Jáuregui
We present a collection of networks that describe the travel patterns between municipalities in Mexico between 2020 and 2021. Using anonymized mobile device geo-location data we constructed directed, weighted networks representing the (normalized) volume of travels between municipalities. We analysed changes in global (graph total weight sum), local (centrality measures), and mesoscale (community structure) network features. We observe that changes in these features are associated with factors such as Covid-19 restrictions and population size. In general, events in early 2020 (when initial Covid-19 restrictions were implemented) induced more intense changes in network features, whereas later events had a less notable impact in network features. We believe these networks will be useful for researchers and decision makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

Read the full article at: arxiv.org