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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Ximena Dávila Yáñez and Humberto Maturana Romesín

Adaptive Behavior 31(5)

More than 20 years ago, Humberto Maturana and Ximena Dávila initiated a research program on the nature of human coexistence within the framework of molecular-autopoietic systems and the understanding of the organism-niche ecological dynamic unit (UDEON). In this article, we focus on the potential of conversation and reflection of living beings as transformative and liberating practices in the configuration of intimate feelings that define at every moment their emotional-relational operation as a totality in the understanding of the worlds they generate. We refer to the main contributions of cultural-biology which invite us to a journey through the nature of knowing, of human pain and suffering, of languaging, conversation, and reflection as cultural-biology beings.

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R. Maria del Rio-Chanona, Alejandro Hermida-Carrillo, Melody Sepahpour-Fard, Luning Sun, Renata Topinkova & Ljubica Nedelkoska
EPJ Data Science volume 12, Article number: 49 (2023)

To study the causes of the 2021 Great Resignation, we use text analysis and investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit. We find that the Reddit discourse evolution resembles the dynamics of the U.S. quit and layoff rates. Furthermore, when the COVID-19 pandemic started, conversations related to working from home, switching jobs, work-related distress, and mental health increased, while discussions on commuting or moving for a job decreased. We distinguish between general work-related and specific quit-related discourse changes using a difference-in-differences method. Our main finding is that mental health and work-related distress topics disproportionally increased among quit-related posts since the onset of the pandemic, likely contributing to the quits of the Great Resignation. Along with better labor market conditions, some relief came beginning-to-mid-2021 when these concerns decreased. Our study underscores the importance of having access to data from online forums, such as Reddit, to study emerging economic phenomena in real time, providing a valuable supplement to traditional labor market surveys and administrative data.

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Michael L. Wong , et al.

PNAS 120 (43) e2310223120

The universe is replete with complex evolving systems, but the existing macroscopic physical laws do not seem to adequately describe these systems. Recognizing that the identification of conceptual equivalencies among disparate phenomena were foundational to developing previous laws of nature, we approach a potential “missing law” by looking for equivalencies among evolving systems. We suggest that all evolving systems—including but not limited to life—are composed of diverse components that can combine into configurational states that are then selected for or against based on function. We then identify the fundamental sources of selection—static persistence, dynamic persistence, and novelty generation—and propose a time-asymmetric law that states that the functional information of a system will increase over time when subjected to selection for function(s).

Read the full article at: www.pnas.org