James DiFrisco, Richard Gawne

Journal of Evolutionary Biology, voae153

This paper evaluates recent work purporting to show that the “agency” of organisms is an important phenomenon for evolutionary biology to study. Biological agency is understood as the capacity for goal-directed, self-determining activity—a capacity that is present in all organisms irrespective of their complexity and whether or not they have a nervous system. Proponents of the “agency perspective” on biological systems have claimed that agency is not explainable by physiological or developmental mechanisms, or by adaptation via natural selection. We show that this idea is theoretically unsound and unsupported by current biology. There is no empirical evidence that the agency perspective has the potential to advance experimental research in the life sciences. Instead, the phenomena that the agency perspective purports to make sense of are better explained using the well-established idea that complex multiscale feedback mechanisms evolve through natural selection.

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Alexander F. Siegenfeld, Asier Piñeiro Orioli, Robin Na, Blake Elias, Yaneer Bar-Yam

Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies analyzing the health and economic costs of various response strategies. Here we show that describing disease transmission in a self-similar (fractal) manner across multiple geographic scales allows for the design of multi-scale containment measures that substantially reduce both these costs. We characterize response strategies using multi-scale reproduction numbers — a generalization of the basic reproduction number R0 — that describe pandemic spread at multiple levels of scale and provide robust upper bounds on disease transmission. Stable elimination is guaranteed if there exists a scale such that the reproduction number among regions of that scale is less than 1, even if the basic reproduction number R0 is greater than 1. We support our theoretical results using simulations of a heterogeneous SIS model for disease spread in the United States constructed using county-level commuting, air travel, and population data.

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Sadamori Kojaku, Filippo Radicchi, Yong-Yeol Ahn & Santo Fortunato 

Nature Communications volume 15, Article number: 9446 (2024)

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.

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Gordana Dodig-Crnkovic 

The development of naturalistic approaches to complexity of life continues a lineage of thought from Prigogine’s thermodynamics to contemporary complexity science. The paper highlights the central themes of self-organization, emergence, and the interplay between physical, informational, and biological processes. Prigogine’s concept of dissipative structures and irreversibility provided a foundation for understanding complexity in physical systems, which later expanded into biology through Kauffman’s models of creativity and evolution. Margulis’s endosymbiosis theory illuminate the cooperative dynamics underpinning life’s complexity, while Walker’s work integrates thermodynamics and information theory to bridge the gap between chemistry and biology through multiscale interactions and adaptive dynamics. By synthesizing these perspectives, this article situates life as an emergent phenomenon shaped by interactions across scales, proposing a unified framework for understanding complexity in the natural world.

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Fabian Dvorak, Urs Fischbacher, Katrin Schmelz

The Economic Journal, ueae085,

We provide systematic insights on strategic conformist—as well as anti-conformist—behaviour in situations where people are evaluated, i.e., where an individual has to be selected for reward (e.g., promotion) or punishment (e.g., layoffs). To affect the probability of being selected, people may attempt to fit in or stand out in order to affect the chances of being noticed or liked by the evaluator. We investigate such strategic incentives for conformity or anti-conformity experimentally in three different domains: facts, taste and creativity. To distinguish conformity and anti-conformity from independence, we introduce a new experimental design that allows us to predict participants’ independent choices based on transitivity. We find that the prospect of punishment increases conformity, while the prospect of reward reduces it. Anti-conformity emerges in the prospect of reward, but only under specific circumstances. Similarity-based selection (i.e., homophily) is much more important for the evaluators’ decisions than salience. We also employ a theoretical approach to illustrate strategic key mechanisms of our experimental setting.

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