Yessica Herrera-Guzmán, Alexander J. Gates, Cristian Candia & Albert-László Barabási 
Scientific Reports volume 13, Article number: 18594 (2023)

In the recent decade, we have seen major progress in quantifying the behaviors and the impact of scientists, resulting in a quantitative toolset capable of monitoring and predicting the career patterns of the profession. It is unclear, however, if this toolset applies to other creative domains beyond the sciences. In particular, while performance in the arts has long been difficult to quantify objectively, research suggests that professional networks and prestige of affiliations play a similar role to those observed in science, hence they can reveal patterns underlying successful careers. To test this hypothesis, here we focus on ballet, as it allows us to investigate in a quantitative fashion the interplay of individual performance, institutional prestige, and network effects. We analyze data on competition outcomes from 6363 ballet students affiliated with 1603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021. Through multiple logit models and matching experiments, we provide evidence that schools’ strategic network position bridging between communities captures social prestige and predicts the placement of students into jobs in ballet companies. This work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.

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Andrea Zhok

Foundations of Science 28, pages 1047–1058 (2023)

This text aims to show how some substantial ontological conclusions, consistent with the notion of ‘complexity’, can be demonstrated through elementary phenomenological analyzes. In particular, we will show that it is necessary to acknowledge an ontology where the forms of ontological efficacy cannot be reduced to efficient causality, the relations between properties are irreducible to deduction, irreducible qualities must exist originally, further qualities emerge from existing qualities, and no explanatory key less complex than the fullness of consciousness’ functions can account for reality.

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David C. Krakauer

Front. Complex Syst., 18 October 2023

Complexity science and machine learning are two complementary approaches to discovering and encoding regularities in irreducibly high dimensional phenomena. Whereas complexity science represents a coarse-grained paradigm of understanding, machine learning is a fine-grained paradigm of prediction. Both approaches seek to solve the “Wigner-Reversal” or the unreasonable ineffectiveness of mathematics in the adaptive domain where broken symmetries and broken ergodicity dominate. In order to integrate these paradigms I introduce the idea of “Meta-Ockham” which 1) moves minimality from the description of a model for a phenomenon to a description of a process for generating a model and 2) describes low dimensional features–schema–in these models. Reinforcement learning and natural selection are both parsimonious in this revised sense of minimal processes that parameterize arbitrarily high-dimensional inductive models containing latent, low-dimensional, regularities. I describe these models as “super-Humean” and discuss the scientic value of analyzing their latent dimensions as encoding functional schema.

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Claudi L. Bockting, Eva A. M. van Dis, Robert van Rooij, Willem Zuidema & Johan Bollen

Nature

Establish an independent scientific body to test and certify generative artificial intelligence, before the technology damages science and public trust.

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