Nick Holton, Marianne Cottin, Adam Wright, Michael Mannino, Dayanne S. Antonio, Marcelo Bigliassi

Antifragility challenges conventional thinking by proposing that adversity is not merely to be survived but actively used to promote growth. This scoping review synthesizes 18 emerging research studies focused on antifragility in human systems across disciplines, distinguishing antifragility from resilience and robustness and highlighting key empirical gaps, particularly in psychological research. During the screening process, articles were categorized as human or non-human systems. Non-human systems (n = 29; e.g., robotics, logistics, information systems, urban planning, artificial intelligence) were excluded from synthesis to align with the review’s focus on human domains (e.g., psychology, leadership, coaching, health). Drawing from biology, psychology, and organizational studies, the review summarizes applications in mental health, performance, and quality of life. Findings emphasize the proactive nature of antifragility, in which stressors are intentionally engaged to strengthen capabilities. Biological concepts like hormesis and psychological frameworks such as post-traumatic growth align with mechanisms relevant to growth through adversity. Yet empirical studies remain scarce, underscoring the need for robust measurement tools and longitudinal designs. Future directions include refining antifragility as a state, trait, or process, developing dose-specific models, and exploring biopsychosocial correlates. Embracing antifragility could transform how individuals and systems confront challenge, not by resisting breakdown, but by evolving beyond it.

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Xiangyi Meng, Benjamin Piazza, Csaba Both, Baruch Barzel & Albert-László Barabási 

Nature volume 649, pages 315–322 (2026)

The brain’s connectome1,2,3 and the vascular system4 are examples of physical networks whose tangible nature influences their structure, layout and, ultimately, their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organization. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of wiring minimization. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimization problem. We discover, however, an exact mapping of surface minimization onto high-dimensional Feynman diagrams in string theory5,6,7, predicting that, with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimization. Specifically, surface minimization predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organization of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which are not only prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi.

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Giulia Fischetti, Anna Mancini, Giulio Cimini, Jessica T. Davis, Abby Leung, Alessandro Vespignani, Guido Caldarelli
Accurate representations of the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, high-resolution, real-time data on the WAN are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model of the WAN that treats air travel as a stochastic process within a maximum-entropy framework. The model uses airport-level passenger flows to probabilistically generate connections while preserving traffic volumes across geographic regions. The resulting reconstructed networks reproduce key structural properties of the WAN and enable simulations of dynamic spreading that closely match those obtained using the real network. Our approach provides a scalable, interpretable, and computationally efficient framework for forecasting and policy design in global mobility systems.

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Maxime Lucas, Luca Gallo, Arsham Ghavasieh, Federico Battiston & Manlio De Domenico
Nature Communications , Article number: (2026)

Empirical complex systems can be characterized not only by pairwise interactions, but also by higher-order (group) interactions influencing collective phenomena, from metabolic reactions to epidemics. Nevertheless, higher-order networks’ apparent superior descriptive power—compared to classical pairwise networks—comes with a much increased model complexity and computational cost, challenging their application. Consequently, it is of paramount importance to establish a quantitative method to determine when such a modeling framework is advantageous with respect to pairwise models, and to which extent it provides a valuable description of empirical systems. Here, we propose an information-theoretic framework, accounting for how structures affect diffusion behaviors, quantifying the entropic cost and distinguishability of higher-order interactions to assess their reducibility to lower-order structures while preserving relevant functional information. Empirical analyses indicate that some systems retain essential higher-order structure, whereas in some technological and biological networks it collapses to pairwise interactions. With controlled randomization procedures, we investigate the role of nestedness and degree heterogeneity in this reducibility process. Our findings contribute to ongoing efforts to minimize the dimensionality of models for complex systems.

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