Lessons from developmental biology can be used to guide the behaviour of robot swarms.

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Andrea Santoro, Matteo Neri, Simone Poetto, Davide Orsenigo, Matteo Diano, Marilyn Gatica, Giovanni Petri

Traditional models of brain connectivity have primarily focused on pairwise interactions, over-looking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction (HOI) metrics have been proposed, a systematic evaluation of their comparative properties and utility is missing. Here, we present the first large-scale analysis of information-theoretic and topological HOI metrics, applied to both resting-state and task fMRI data from 100 unrelated subjects of the Human Connectome Project. We identify a clear taxonomy of HOI metrics — redundant, synergistic, and topological—, with the latter acting as bridges along the redundancy–synergy continuum. Despite methodological differences, all HOI metrics align with the brain’s overarching unimodal-to-transmodal functional hierarchy. However, certain metrics show specific associations with the neurotransmitter receptor architecture. HOI metrics outperform traditional pairwise models in brain fingerprinting and perform comparably in task decoding, underscoring their value for characterizing individual functional profiles. Finally, multivariate analysis reveals that — among all HOI metrics — topological descriptors are key to linking brain function with behavioral variability, positioning them as valuable tools for linking neural architecture and cognitive function. Overall, our findings establish HOIs as a powerful framework for capturing the brain’s multidimensional dynamics, providing a conceptual map to guide their application across cognitive and clinical neuroscience.

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Anna Mancini, Balázs Lengyel, Riccardo Di Clemente, Giulio Cimini

Recent crises like the COVID-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted increasing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here we study dynamics and determinants of firm-level systemic risk in the Hungarian production network from 2015 to 2022. We use as benchmark a heuristic maximum entropy null model that generates an ensemble of production networks at equilibrium, by preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during COVID-19, as those enabling economic exchanges become key players in the economy — a result which is not reproduced by the null model. Although the empirical systemic risk aligns well with the null value until the onset of the pandemic, it becomes significantly smaller afterwards as the adaptive behavior of firms leads to a more resilient economy. Furthermore, firms’ international trade volume (being a subject of disruption) becomes a significant predictor of their systemic risk. However, international links cannot provide an unequivocal explanation for the observed trends, as imports and exports have opposing effects on local systemic risk through the supply and demand channels.

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Arianna Salazar-Miranda, Zhuangyuan Fan, Michael Baick, Keith N. Hampton, Fabio Duarte, Becky P. Y. Loo, Edward Glaeser, and Carlo Ratti

PNAS 122 (30) e2424662122

Urban public spaces have traditionally served as places for gathering and social connection, shaping the social fabric of cities. This study reveals important shifts in pedestrian behaviors over a 30-y period in four US public spaces. By using AI and computer vision to analyze historical and contemporary video footage, we observe an increase in walking speed and a decrease in time spent lingering, along with fewer group encounters. This trend suggests a growing perception of city streets as corridors for movement rather than spaces for social interaction. These findings highlight a changing urban dynamic, where efficiency increasingly shapes public space usage, potentially impacting social connections and the community-building role of these environments.

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Dirk Helbing & Sachit Mahajan 

We critically examine the evolving functionality and challenges of democracies in the age of digital transformation and artificial intelligence (AI). Contrary to notions of democracy as a static governance form, we emphasize the importance of its adaptability, but find that recent technological and institutional shifts have undermined foundational mechanisms such as decentralized decision-making, transparent information flows, and effective self-correction. Drawing from complexity science, political theory, participatory research and computational social science, we analyze how algorithmic control, surveillance capitalism, and power asymmetries have affected core democratic principles. We pay specific attention to structural changes in political representation, civic participation, and how these have affected public trust. We further discuss a set of recent, digitally assisted approaches, ranging from deliberative platforms and participatory budgeting to fair voting systems and co-creation, which can potentially restore the legitimacy of democratic systems and their resilience. By understanding democracies as dynamic, co-evolving systems, we highlight the potential of plu-ralistic design. Aligning technological progress with constitutional principles can meaningfully repair, revive and updgrade democratic systems and institutions.

Read the full article at: www.researchgate.net