Manlio De Domenico, Valeria d’Andrea, Marco Grassia, Giuseppe Mangioni & Barbara Di Camillo 
Nature Physics (2026)

As the term ‘digital twin’ spreads across science and technology, it risks losing scientific meaning. Such a twin should not denote just a digital replica, but a computational model that can be updated, tested against intervention and shown to fail.As the term ‘digital twin’ spreads across science and technology, it risks losing scientific meaning. Such a twin should not denote just a digital replica, but a computational model that can be updated, tested against intervention and shown to fail.

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Mariana Macedo , Melanie Oyarzun , Cristian Candia , César A Hidalgo

PNAS Nexus, pgag308,

Collective memory—shared information about the past that shapes a group’s identity—is often examined through constructs such as familiarity or salience, conflating its local and global components. Here, we untangle these components using multilingual online attention traces for over 50,000 Wikipedia biographies spanning 137 billion pageviews and a large-scale behavioral recognition experiment in which more than 41,000 participants answered factual questions about famous biographies. Our experiment reveals a robust local memory premium, with people being around 10 percentage points more likely to answer correctly questions about someone from their country and about 5 points more likely to answer correctly questions about biographies with whom they share a language. Finally, we study the impact of three major life events: Nobel Prizes, Oscars, and deaths in local and global online attention, finding these events produce brief but strong surges in local attention and slower but sustained increases in global attention. These findings add important spatial and temporal nuances to our understanding of collective memory.Collective memory—shared information about the past that shapes a group’s identity—is often examined through constructs such as familiarity or salience, conflating its local and global components. Here, we untangle these components using multilingual online attention traces for over 50,000 Wikipedia biographies spanning 137 billion pageviews and a large-scale behavioral recognition experiment in which more than 41,000 participants answered factual questions about famous biographies. Our experiment reveals a robust local memory premium, with people being around 10 percentage points more likely to answer correctly questions about someone from their country and about 5 points more likely to answer correctly questions about biographies with whom they share a language. Finally, we study the impact of three major life events: Nobel Prizes, Oscars, and deaths in local and global online attention, finding these events produce brief but strong surges in local attention and slower but sustained increases in global attention. These findings add important spatial and temporal nuances to our understanding of collective memory.

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Benedikt Hartl, Milton L. Montero, Marcello Barylli, Sebastian Risi, Michael Levin

How phenotypic transformations are implemented by changes in underlying regulatory dynamics remains a central question in developmental biology. Inspired by D’Arcy Thompson’s 1917 “On Growth and Form”, we ask whether coherent large-scale transformations of morphology can be encoded as low-dimensional modulations of a self-organizing developmental system. We use neural cellular automata (NCAs) as bio-inspired models of distributed development, in which a shared local regulatory network grows target morphologies from a single cell. We apply low-rank adaptation (LoRA) to pretrained NCAs, representing each adapted developmental program as a low-rank modulation of a fixed regulatory scaffold. Horizontal and vertical scaling of a fully grown 2D emoji phenotype can each be implemented by rank-one adaptations. Their linear combinations parametrically control phenotype size, generalize beyond the training distribution, and compose with target-specific adapters. Strikingly, adaptations learned for one phenotype transfer zero-shot across structurally and semantically diverse phenotypes sharing the same reference scaffold, while largely preserving internal features. This suggests reusable system-level hyper-directions of scale rather than morphology-specific transformations. From approximately 25,000 independently trained phenotype-specific NCA adapters with a shared scaffold, we further identify latent low-dimensional directions that functionally control phenotypic variation including scaling, style, and symmetrical fission. Together, our results provide a computational realization of D’Arcy Thompson’s remarkable grid transformations in a 2D NCA—a minimal cybernetic tissue in which variations of fully grown emoji phenotypes can be encoded, combined, and controlled through low-dimensional directions in regulatory weight space.

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Santa Elena Tellez-Flores and Alberto Robledo

Complexities 2026, 2(3), 19

We present a nonlinear dynamical model for vehicular traffic jams and their dissolution based on the noise-perturbed onset of chaos. The model makes use of the bifurcation gap generated by addition of noise to quadratic iterated maps. The gap results from the elimination by noise of periodic and chaotic attractors with large periods and large numbers of chaotic bands, respectively. The bifurcation gap is recapitulated at the transition to chaos (vanishing Lyapunov exponent) as a crossover from noiseless to irregular, chaotic-like regimes at an iteration time tcross with value dependent on the noise amplitude. This behavior is employed in a model (with variants) that we design for multilane road congested traffic. We highlight four main model properties that are also present in the dynamics of glass formation: (i) plateau interrupted relaxation; (ii) Adam–Gibbs empirical law; (iii) aging; and (iv) diffusion arrest. The model bridges previous studies that have indicated analogies between glassy dynamics and vehicular traffic as well as nonlinear dynamics and same-name traffic. We also discuss the connection of the model with urban multilane road networks.

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