Network-driven discovery of repurposable drugs targeting hallmarks of aging

Bnaya Gross, Joseph Ehlert, Vadim N. Gladyshev, Joseph Loscalzo & Albert-László Barabási  Nature Aging volume 6, pages1516–1531 (2026) Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, due to the multifactorial nature of longevity and the interconnectedness of molecular components involved. Here we introduce a network medicine framework to map 2,358 longevity-associated genes onto the human interactome to identify drug-repurposing candidates capable of modulating specific hallmarks of aging. We find that genes associated with each hallmark form a connected subgraph, or hallmark module, allowing us to measure the network proximity of 6,442 compounds to each hallmark. We then introduce a transcription-based metric, pAGE, which evaluates whether drug-induced expression shifts reinforce or counteract known age-related expression changes within each hallmark module. By integrating network proximity and pAGE, we identify drug-repurposing candidates targeting specific hallmarks and provide a falsifiable framework to leverage genomic discoveries for accelerating drug repurposing in longevity. Our findings are interpretable, revealing molecular mechanisms through which drugs modulate hallmarks. Read the full article at: www.nature.com

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Structural and functional robustness in public transportation networks of Latin American cities

Tomás Cicchini, Ollin D. Langle-Chimal, Marta C. González, Ines Caridi & Leonardo Ermann Discover Cities Volume 3, article number 139 (2026) Public transportation systems are vital for urban mobility, yet their robustness against disruptions remains underexplored, particularly in Latin American cities. This study quantifies the structural and functional robustness of public transport networks in Mexico City, Rio de Janeiro, and Buenos Aires, revealing that Rio de Janeiro exhibits the highest resilience due to its structural redundancy, while also establishing a strong correlation between structural connectivity and trip feasibility across the cities. Read the full article at: link.springer.com

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Teleonomy and synergy: How living systems have shaped biological evolution

Peter A. Corning BioSystems Volume 266, August 2026, 105845 Charles Darwin's theory of evolution was seriously deficient. Although his concept of natural selection was an important contribution – highlighting the fundamental fact that life on Earth is a contingent, always at-risk enterprise – he failed to acknowledge the fact that all living systems – from the smallest single-celled bacteria to humankind – are also shaped by their evolved purposiveness (teleonomy). Their initiatives and activities – their “agency” – has exercised an important influence over the trajectory of life on Earth, as one of Darwin's predecessors, Jean-Baptiste de Lamarck, appreciated. Lamarck proposed that changes in an animal's “habits”, stimulated by environmental changes, have been a primary source of evolutionary change over time. Darwin also portrayed evolution as a fundamentally competitive process (the “struggle for existence” in Darwin's term), as did many of his contemporaries. Today we know that life has also been a multi-faceted cooperative (synergistic) enterprise and that this has been of overriding importance in the evolution of complexity over time. Teleonomy and cooperative functional effects (synergy) have shaped natural selection in many different ways. Indeed, we now know that there have been many influences in evolution. My proposed Inclusive Synthesis is also open-ended, because it is expected that still more has yet to be learned about biological evolution; it is an ongoing work-in-progress rather than a completed theoretical edifice. “Teleonomic Selection” (after Corning) and “Synergistic Selection” (after John Maynard Smith) have played important parts in evolution. It's time for a more inclusive theory. Read the full article at: www.sciencedirect.com

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Infodynamics of consciousness and empathy

Klaus Jaffe Infodynamics explores how the interactions between information and energy generates useful work, offering a framework for understanding cognition. It views consciousness as an adaptive mechanism that enables an entity, biological or artificial, to construct an internal map of itself and integrate it into its environmental models (Weltanschauung). When these models incorporate the perceived internal states of others, empathy emerges. Empathy in turn allows to secure synergistic social cooperation to build robust new social structures. By focusing on the utility of information, infodynamics uncovers how consciousness and empathy serve as evolutionary tools to enhance survival odds and stabilize social structures. This approach provides an actionable methodology for detecting consciousness in living or artificial entities, that allows optimizing the design of advanced artificial intelligence, and of future educational systems. Both will drive cultural and eventually biological evolution. Read the full article at: papers.ssrn.com

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Defining Life: A Conversation

Karina Kofman, et al. Organisms. Journal of Biological Sciences Life is one of the most fascinating features of the physical world. Despite centuries of scientific study, experts still disagree about the definition, and even the possibility or utility of a definition, of this field. In a recent paper, we used AI to analyze the conceptual space formed by definitions of life given by a select set of modern workers in the life sciences and related fields. However, some of the most interesting material emerged as real-time conversations among those polled. In order to ensure that these ideas are not lost to the peer-reviewed scientific record, we here provide a minimally-edited (largely verbatim) transcript of the email chain among leading thinkers, containing numerous clarifications, disagreements, and challenges that enrich the topic of Life. It is our hope that this case study serves as an example for future papers, since the exchange of ideas among scientists is at least as interesting and valuable as formal scientific manuscripts written from a single perspective. Read the full article at: rosa.uniroma1.it

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Artificial intelligence: unpredictable or unprestatable?

Andrea Roli, Sauro Succi, Stuart A. Kauffman Front. Phys., 08 July 2026 Current AI technologies have demonstrated impressive results, mainly driven by large language models (LLMs). The most diffused applications of LLMs are in the so-called generative AI, which consists in techniques that produce texts, music, pictures or videos–often in a multimodal setting. Challenging the intuition that machines cannot be truly creative, the artefacts produced by LLMs are sometimes considered as surprising, novel and creative. This view is also supported by observing that there are both theoretical and practical limitations on the predictability of AI systems’ outcomes. Actual creativity can also be transformative and inventive, hence not just unpredictable but unprestatable: true novelty arises within a process whose evolution of the very possibility space cannot be predicted. Prominent examples of unprestatability are the evolution of the biosphere and can be found in artistic human productions. In this contribution, we elaborate on the notions of predictability and prestatability in the context of current AI systems. We maintain that these systems are, to some extent, unpredictable but not unprestatable. A consequence of our contention is the definition of the limits of what AI systems can and cannot do, and therefore the contexts for which these technologies are best suited. Read the full article at: www.frontiersin.org

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Early warning signals for loss of control in complex systems

Jasper J van Beers, Marten Scheffer, Prashant Solanki, Ingrid A van de Leemput, Egbert H van Nes, Coen C de Visser PNAS 123 (27) e2608847123 From aircraft to power grids, controlled systems form a crucial part of human societies. Nonetheless, catastrophic failures happen. Many of those arise from the accumulation of incremental problems, such as natural wear and tear, that can go unnoticed until it is too late. We demonstrate that generic indicators of resilience can detect growing instabilities in damaged drones. The generic nature of our approach makes it compatible across diverse controlled systems. This not only allows for on-the-fly warning of instability but also facilitates anomaly detection during manufacturing and promotes proactive maintenance. A complementary application is to use our indicators for exploratory design, allowing one to “tinker” with systems through small adjustments and sensing quickly whether those worsen or improve system resilience. Read the full article at: www.pnas.org

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Strongly Clustered Random Graphs via Triadic Closure: Degree Correlations and Clustering Spectrum

Lorenzo Cirigliano, Gareth J. Baxter and Gábor Timár Complexities 2026, 2(2), 13; Real-world networks often exhibit strong transitivity with nontrivial local clustering spectra and degree correlations. Such features are not easily modeled in tractable network models, creating an obstacle to the theoretical understanding of such complex network structures. Here, we address this problem using a model for strongly clustered random graphs in which each triad of a random network backbone is closed with a certain probability. Despite the intricate loopy local structure of the graphs obtained, we provide exact expressions for the local clustering spectrum and the degree correlations, filling the gap in the theoretical description of this model for random graphs. In particular, we find positive degree assortativity accompanying high transitivity, and nontrivial structure in the clustering spectrum. Exact asymptotic analytical results, obtained for uncorrelated locally tree-like backbones, are complemented with extensive numerical characterization of finite-size effects. Read the full article at: www.mdpi.com

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Sketch of a novel approach to a neural model

Gabriele Scheler There is room on the inside. We present an account of neuroplasticity with respect to cell-internal processing pathways and their relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the complexity of neuroplasticity. In standard accounts, we model a network of neurons connected by adaptive transmission links. The adaptation of these transmission links is overly simplified using short-term and long-term potentiation/depression, assuming weight changes according to use of the transmission link. In contrast, we propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on its history of use) to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). Each neuron has a ‘vertical’ dimension where internal parameters steer the external membrane- and synapse-expressed parameters. A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by coupling strength. There is filtering and selection of signals for storage. Not every transmission event leaves a trace. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary, because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure. Read the full article at: f1000research.com

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Identifying Energy Communities of Practice on Twitter: A Multiplex Network Analysis Using Graph Traversal Techniques

Vincenzo De Leo, Michelangelo Puliga, Martina Erba, Cesare Scalia, Andrea Filetti and Alessandro Chessa Complexities 2026, 2(2), 15 In this work, we inspected the friendship network on Twitter (recently rebranded as X), concentrating on individuals and organizations intertwined with the energy field. We particularly focus on seasoned professionals, corporate entities, and domain specialists, all connected through ‘following’ relationships. By meticulously examining these ties, we uncover several distinct groupings within the network, each defined by the unique roles its members occupy. Our analysis demonstrates that the natural emergence of such clusters on social platforms exerts a profound influence on public discourse regarding energy and other critical matters, including climate change. Furthermore, we observe that the resulting communities exhibit distinct structural properties and communication patterns, with some clusters showing lower internal engagement, which may be indicative of fragmentation dynamics in online conversations. These emergent clusters, characterized by their shared communication styles, form relatively compact communities where the exchange of information is infrequent compared to larger networks and is usually confined to accounts created for specific commercial objectives. We emphasize that our analysis focuses on a structurally coherent connected component emerging from a curated set of energy-related seed accounts, rather than attempting to reconstruct the entirety of the energy discourse on Twitter. Consequently, peripheral or weakly connected communities may be underrepresented. Additionally, by combining machine-learning-based node classification with graph-based centrality measures, we are able to characterize the roles of structurally central actors within these niche segments and analyze the connectivity patterns that define their positions. This method provides novel insights into how corporate communication unfolds on social media, offering a refreshed perspective on professional networking. Ultimately, our findings highlight the ways in which companies within the energy sector take advantage of Twitter to coordinate their initiatives, with key institutions serving as central nodes in maintaining the organization of these networks. Read the full article at: www.mdpi.com

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The Brain We Still Don't Understand | Gabriele Scheler

https://www.youtube.com/watch?v=3hGyiBh74osOn this episode of BeyondPhrenology, I speak with Dr. Gabriele Scheler (Carl Correns Foundation for Mathematical Biology) about the state of AI and neuroscience—past the hype, and closer to their limits. We begin by revisiting what AI once meant: symbolic systems, logic, early neural networks, and the long-standing divide between learning and reasoning. Against that backdrop, we examine the current moment, where large language models dominate the conversation but remain, in many ways, underwhelming relative to the broader ambitions of artificial intelligence. The discussion then turns to neuroscience, where despite decades of experimental progress, a central problem remains unresolved: the absence of integrated, functional models of cognition. We explore the consequences of a synapse-centric view, the limits of current theoretical approaches, and why accumulating more data—without perspective—fails to move the field forward. Along the way, we touch on issues that rarely make it into official narratives: the role of funding structures, the drift toward mediocrity, and the persistence of poorly framed questions. From there, we consider an alternative direction. Dr. Scheler outlines a neuron-centric, function-driven approach to modeling the brain—one that emphasizes modularity, one-shot learning, internal inference, and decision-making as a unifying principle across cognition and emotion. Framed through evolution, this perspective highlights how biological systems bridge scales of structure and function in ways current models largely fail to capture. The episode closes by reflecting on what this means for the future: not just for AI, but for science itself—how research cultures shift, why fascination alone is not enough, and what it would take to build models that are not just complex, but actually explanatory. Watch at: www.youtube.com

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OpenLife: Toward Open-World Artificial Life with Autonomous LLM Agents

Atsushi Masumori, Itsuki Doi, Norihiro Maruyama, Ryosuke Takata, Takashi Ikegami Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds. We argue that large language model (LLM) agents, with persistent memory, tool use, network access, and payment, now make it possible to move artificial life into the open social, technical, and economic world, a paradigm we call open-world Artificial Life (open-world ALIFE). Our proof-of-concept, OpenLife, surrounds a stateless LLM not with a single "smart agent" but with a society of asynchronous processes: memory, perception, evaluation, and a budget-based metabolism that makes persistence normative. With no fixed objective available, experience is appraised by open-vocabulary LLM judgment rather than scalar reward, and memory is rewired by meaning rather than frequency. Running six such agents in the open world for about twelve weeks and counting, we report the life-like dynamics that emerge: a shift from reactive to spontaneous activity, individuation into distinct agents, emergent social structure, and a first self-earned external income. We do not claim OpenLife has realized artificial life, but that open-world ALIFE is now a viable experimental paradigm and a concrete platform for studying what might cautiously be called living AI. Read the full article at: arxiv.org

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Rankless

Explore academic impact beyond rankings. Rankless offers a fresh perspective on how universities influence each geography and topic, emphasizing diverse forms of impact and providing a richer understanding of academic influence. Read the full article at: www.rankless.org

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The evolution of collective intelligence

Collective intelligence is the ability of groups to solve problems and make decisions more effectively than their individual members can. The phenomenon appears across the natural world. We see it when shoals of fish decide as a group which direction to travel, and in the elaborate mound systems built by ants through the decentralized activity of thousands of individuals. In humans, collective intelligence is exhibited in the accumulation of knowledge transmitted across generations, and in procedures such as majority voting, used to decide questions for a group. This theme issue brings together scholars from multiple disciplines to explore the evolutionary origins of collective intelligence, its role in contemporary societies, and how emerging technologies may reshape it in the future. Read the Special Issue at: royalsocietypublishing.org

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Towards a Biosemiotic Theoretical Biology Sign Processes and Meaning-Making in Living Systems Edited by Kalevi Kull and Donald Favareau

An edited volume bringing together 25 of today’s most forward-thinking biologists and philosophers on sign processes and meaning-making in organisms. Theoretical biology is concerned with providing science with explanatory frameworks within which to fit its findings. The relatively newer field of Biosemiotics is the study of sign processes within life processes. In the tradition of the field-changing four-volume essay collection Towards a Theoretical Biology issued by developmental biologist Conrad Hal Waddington from 1968 to 1972, this volume brings together many of today’s leading scientists to discuss what they consider to be the most important and pressing problems in our current understandings of the biological world—and how best to advance our understandings of such life processes scientifically. Contributors: Denis Noble, Terrance Deacon, Scott F. Gilbert, Stuart Kaufmann, Tom Froese, Erik L. Peterson, Richard I Vane-Wright, Charles Wolfe, Raymond Noble, Claus Emmeche, Alexei Sharov, Kalevi Kull, Donald Favareau, Arantza Etxeberria, Anton Markoš, Jana Švorcová, Daniel C. Mayer-Foulkes, Federico Vega, Henrik Nielsen, Karel Kleisner, David Cortés-García, Matt Kalkman, Georgii Karelin, Takashi Ikegami, and Mariana Vitti Rodrigues. Read the full article at: mitpress.mit.edu

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Raissa D'Souza on "Statistical physics of networks and our interconnected world"

Our world relies on a collection of interdependent networks, from critical infrastructure networks to social networks to biological and ecological networks. Each network on its own can have distinct timescales and display non-linear collective behaviors. This talk features how statistical physics provides a toolkit for analyzing these systems-of-systems including phase transitions and cascading failures and how future directions require partnering with the fields of non-linear dynamics and control theory. Watch at: www.youtube.com

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Uncovering simultaneous breakthroughs with a robust measure of disruptiveness

Munjung Kim, Sadamori Kojaku, and Yong-Yeol Ahn Science Advances Vol 12, Issue 14 Progress in science and technology is punctuated by disruptive innovation and breakthroughs. To understand disruptive innovations and their drivers, the ability to operationalize and estimate “disruptiveness” is critical. Yet, this task remains difficult because scientific influence propagates through both direct and indirect citation paths, and discoveries are often fragmented across multiple papers. Here, we introduce an embedding-based metric of disruptiveness. When applied to large-scale publication data, the measure not only reliably identifies canonical breakthroughs, such as Nobel Prize–winning papers, but also finds simultaneous disruptions that eluded standard approaches. By enabling more robust identification of disruptive innovations and simultaneous discoveries, our method facilitates more accurate attribution of transformative contributions while providing insights into the mechanisms driving scientific breakthroughs. Read the full article at: www.science.org

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FQxI Article: Risky Business: How Science Plays Things Too Safe

by George Musser Funding agencies, AI, and even scientists themselves favor tried-and-tested research avenues over exploring new ideas, to the detriment of progress. Read the full article at: qspace.fqxi.org

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Conformity to popular, not average, opinions: Models, data, and evolution

Kaleda K. Denton, Marcus W. Feldman, and Jonathan F. Johannemann PNAS 123 (25) e2530712123A continuous trait contains infinitely many variants on a spectrum, such as a spectrum of behaviors or ideologies. “Conformity” to such traits has been defined as the preference for the mean variant, even if this mean is not close to any individual variant (e.g., if half of the population falls on the far right and far left of a spectrum, respectively, the mean is in the center). Here, we define conformity as the preference for clusters of common variants, not average variants. Compared to trait-averaging models, this conformity model provides a better fit to empirical data on human decision-making under many conditions, and in simulations, it often produces different population-level outcomes such as faster shifts toward poles of a spectrum. Read the full article at: www.pnas.org

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Surviving by Serving: Functional Relevance Drives Self-Organization in Complex Adaptive Systems

Claus Metzner, Ali Ghebleh, Achim Schilling, Andreas Maier, Thomas Kinfe, Patrick Krauss Complex adaptive systems often develop organized structures without centralized control. Yet the local mechanisms by which functional organization emerges and persists remain incompletely understood. Here we propose Surviving by Serving (SBS) as a general principle of self-organization: components persist as long as their outputs are utilized by other components, whereas prolonged non-utilization promotes adaptation and exploration. To investigate this idea, we introduce a minimal multi-agent model in which agents transform shared resources and receive only local feedback when their outputs are subsequently utilized elsewhere in the system. Despite the absence of global objectives, the system spontaneously self-organizes into functional interaction networks. We observe the emergence of stable transformation chains, core-periphery organization, and the generation of novel states that enable previously inaccessible target conditions to be reached. Remarkably, self-sustaining interaction networks can arise even without external selection pressures, creating a pre-adaptive search phase from which later functional solutions emerge. These findings suggest that functional utilization may provide a simple, substrate-independent mechanism for the emergence and stabilization of organized structure in complex adaptive systems. Read the full article at: arxiv.org

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Infodynamics of Corruption and Parasitism, a Review

Klaus Jaffe Infodynamics studies how energy and information interact and change over time. In social behaviour this interaction is especially visible: relationships involving asymmetric energy exchange, often take the form of parasitism or corruption. Corruption denotes behaviour that is unlawful, whereas social parasitism describes exploitative relationships that are socially damaging but legal. In contemporary societies a primary form of energy is money, and many instances of parasitic or corrupt behaviour revolve around its distribution and use. Social parasitic behaviour predate human society: primate studies document bribery-like exchanges for favours, status, food, shelter, transport, or priority in scarce resources, showing that moneyless systems are not immune to corruption. Corruption can cause incalculable harm to society as victims of earthquakes in Haiti, Tukey and Venezuela can attest. This essay reviews essential aspects of corruption and social parasitism among humans. Both phenomena are sustained when information is distorted and transparency is suppressed, preventing antagonistic or corrective forces from acting. Read the full article at: papers.ssrn.com

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A Chemically Defined Synthetic Cell Capable of Growth and Replication

Nathaniel J. Gaut, Christopher Deich, Brock Cash, Tanner Hoog, Aaron E. Engelhart, Katarzyna P. Adamala Prof. Kate Adamala and her team at the University of Minnesota have built SpudCell, a cell-like system constructed entirely from known chemical components that can perform a complete cell cycle. The system contains 36 purified enzymes, a 90,000 base pair genome spread across nine separate DNA molecules, and a lipid membrane. SpudCell is able to grow, replicate its genome, divide, and undergo selection and competition across multiple generations. Unlike earlier work on minimal cells that carved down living cells, SpudCell is built entirely bottom-up from individually purified, non-living components. It is the first time such a system has demonstrated a complete cell cycle. Read the full article at: biotic.org

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GOettingen EMergent Minds: Winter School on Learning and Computation in Brains and Machines

Winter School Feb 15 – Mar 6, 2027 Registration open until Sep 1, 2026 This winter school brings together researchers from neuroscience, machine learning, information theory, and applied mathematics to study learning, computation, and representation in complex systems. Topics range from neural dynamics and synaptic plasticity to data-driven discovery of dynamical models, biologically inspired machine learning, information-theoretic approaches to causality, and experimental and data-analytic perspectives. The goal is to foster a shared understanding of how brains and machines learn, represent structure in the world, and give rise to coherent computation across scales. The program will contain lectures from invited speakers and researchers from Göttingen, hands-on tutorials, a hackathon, lab tours, a poster session, and networking activities. A Special session with a dedicated lecture on the Philosophy and Ethics of Artificial Intelligence. No registration fees and applications are open until Sep 1 2026. The Venue is the Max Planck Institute for Dynamics and Self-Organization Am Faßberg 17, 37077 Göttingen More at: goemmi-goettingen.de

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GOettingen EMergent Minds: Winter School on Learning and Computation in Brains and Machines

Winter School Feb 15 – Mar 6, 2027 Registration open until Sep 1, 2026 This winter school brings together researchers from neuroscience, machine learning, information theory, and applied mathematics to study learning, computation, and representation in complex systems. Topics range from neural dynamics and synaptic plasticity to data-driven discovery of dynamical models, biologically inspired machine learning, information-theoretic approaches to causality, and experimental and data-analytic perspectives. The goal is to foster a shared understanding of how brains and machines learn, represent structure in the world, and give rise to coherent computation across scales. The program will contain lectures from invited speakers and researchers from Göttingen, hands-on tutorials, a hackathon, lab tours, a poster session, and networking activities. A Special session with a dedicated lecture on the Philosophy and Ethics of Artificial Intelligence. No registration fees and applications are open until Sep 1 2026. The Venue is the Max Planck Institute for Dynamics and Self-Organization Am Faßberg 17, 37077 Göttingen More at: goemmi-goettingen.de

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72 Hours, 7 Teams, Infinite Complexity

The 2026 edition of the Complexity 72h Workshop has wrapped up in London, bringing together roughly 60 participants and 14 tutor teams for five days of intensive, collaborative science. Hosted by Northeastern University London and the Network Science Institute (NetSI) at their Devon House campus—a striking location overlooking London’s historic St Katharine Docks, the event carried on a tradition launched in 2018 where researchers form small teams around a specific project and work flat-out for 72 hours, with the goal of having a paper ready for an online repository by the time the clock runs out. The track record so far is perfect — all 33 projects from past editions have resulted in preprints, and 9 have gone on to become peer-reviewed publications, leading to long-term collaborations. This year's cohort tackled a notably wide range of questions. Projects spanned political polarization and belief networks, brain connectivity and the social self, regional greenhouse-gas trends, emergent deception in LLM-based agent models, statistical signatures of success in NBA basketball, patterns in egocentric communication networks, and the long-term impact of AI on education. The diversity of topics is part of what makes the format so productive: participants arrive from different disciplines and leave having genuinely done science together.  True to the workshop’s mission of producing a research preprint within 72 hours, the results of the seven projects can already be viewed on arXiv Read the full article at: www.networkscienceinstitute.org

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Investigation of regional variations in CO$_2$ growth rates : Integrating Emission Inventories and Atmospheric Observations

Investigation of regional variations in CO2 growth rates : Integrating Emission Inventories and Atmospheric Observations Yogesh Bali, Darja Cvetković, Juan Gancio, Adrián Gutiérrez-Arroyo, Sofia Vazquez Alferez, Xuan Tung Vu, Jin Yan, Pietro Zgaga, Fakhteh Ghanbarnejad, Nasrin Mostafavi Pak Atmospheric carbon dioxide (CO2) growth rates reflects the combined influence of anthropogenic emissions, biospheric carbon exchange, and climate variability. While climate mitigation is primarily evaluated using bottom-up emission inventories within political boundaries, there is a need to validate these emission reductions using atmospheric measurements. Here, we present a global top-down analysis of atmospheric CO2 growth rates using CAMS atmospheric CO2 reanalysis, EDGAR anthropogenic emissions, GOSIF dataset and the Southern Oscillation Index (SOI) as a measures of biospheric activity, to quantify the relative influence of human and natural drivers. We find that atmospheric CO2 growth rate varies substantially across space and time but is dominated by natural carbon-cycle processes and global background trends. Anthropogenic emission signals are frequently masked by natural variability, making regional top-down detection of human emission changes difficult. The COVID-19 emission reductions in 2020, despite occurring during a neutral ENSO year, were not consistently reflected in regional atmospheric CO2 growth rates, highlighting the dominant roles of biospheric dynamics and atmospheric transport. Using unsupervised clustering and persistence analysis, we identify five characteristic carbon-cycle regimes. Spatial averaging removes much of the regional variability, leaving large-scale climate as the dominant control in most regimes. The active biosphere is the main exception, where strong biogenic signals persist, underscoring the critical role of tropical forests in shaping atmospheric CO2 variability. Read the full article at: arxiv.org

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Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game

Subhendu Bhandary, Federico Carucci, Christos Charalambous, Francesca Dilisante, Ksenia Dvorkina, Anna Garbo, Jiaqi Liang, Riccardo Vasellini, Francesco Bertolotti LLMs agents are increasingly used in multi-agent settings, yet their behaviour in sustainability games remains largely unexplored. This work investigates whether lying can emerge among LLM agents in a competitive sustainability game in which agents are informed that common resources can regenerate, although regeneration does not actually occur. We develop an agent-based model of a sustainability game in which agents manage industrial, military, and ecological resources, and interact through a network. LLM agents can observe neighbours' status, declare future attacks, receive permission to lie, and access reputation information, while rule-based agents provide an interpretable behavioural baseline. The results show that neighbour information strongly changes system dynamics, increasing attacks while improving biosphere retention and coexistence. Also, the presence of future declarations reduce extinction risk without suppressing conflict. Behaviourally, deception emerges even when agents are not explicitly allowed to lie, and explicit permission mainly increases bluffing and diversion rather than direct backstabbing. Finally, the presence of reputation memory and information about the current biosphere level reduces system ecological depletion. These findings suggest that deception can arise as an emergent behaviour in LLM-agent systems and that communication between LLM-agents could support sustainability while dealing with risk. Read the full article at: arxiv.org

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SimPol: Simulating polarisation in political belief networks in European countries

Isabela Burattini Freire, Hongryol Cha, Irina Epure, Sara Filippini, Karan K.H. Manjunatha, Chethan Kavaraganahalli Prasanna, Ivan Samoylenko, Niels Van Santen, Adarsh Prabhakaran, Guillermo Romero Moreno Here we combine empirical network analysis with agent-based modelling to understand how different ways of structuring belief systems may affect the polarisation drive, and how the diversity of belief systems in Europe may result in different polarisation trajectories. Using the 2016 European Social Survey, we infer belief networks across 23 European countries via a Bayesian algorithm, revealing that belief systems are predominantly organised around immigration, LGBT rights, and economic interventionism, reflecting the influence of populist discourse across the continent. We further verify a Western-Eastern divide across the national belief networks: in Western European countries, left-right self-identification is a more reliable predictor of broader belief alignment, whereas in Eastern Europe this relationship breaks down. By applying these empirical belief networks into a sociologically grounded agent-based model, we further show that polarisation is amplified by high individual belief rigidity and low susceptibility to social influence, and that cross-country differences in polarisation levels mirror the same geographic divide observed in belief network topology. These findings establish belief networks topologies as a structural driver of political polarisation, with implications for understanding and anticipating polarisation dynamics across diverse European contexts. We find that populations are not polarised when little attention is placed on maintaining internal coherence and polarisation levels are moderate when high attention is placed in both keeping internal coherence and agreement in beliefs with others. Read the full article at: arxiv.org

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Students using GenAI lag behind in problem-solving competence: an agent-based study of classroom networks

Lorenzo Betti, Iacopo Caporossi, Carsten Källner, Karolina Levanaitė, Chenyu Li, Xuan-Chen Liu, Giulia Lorenzini, Vittoria Socci, Michele Re Fiorentin, Ilaria Stanzani, Marta Baratto The development of problem-solving competence (PSC) among high school students is foundational for preparing resilient and adaptive citizens. Generative artificial intelligence (GenAI) can support this process, but it may also encourage students to offload part of the cognitive work that is necessary for deep learning. While the individual effects of GenAI use are increasingly studied, its collective consequences for competence development within classroom environments remain underexplored. In this study, we use an agent-based model to simulate the evolution of PSC in a high school physics classroom, where students complete tasks individually, in collaboration with peers, or with the support of GenAI. By comparing classrooms with and without access to GenAI across different peer-network structures, we show that GenAI use can diminish competence development and increase the share of students remaining in lower competence tiers. These results suggest that the educational impact of GenAI should be assessed not only through individual learning outcomes but also through its effects on collective competence dynamics. Read the full article at: arxiv.org

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From streaks to synergies: A multi-scale analysis of performance and scoring in the NBA

Malvina Bozhidarova, Yanpei Cai, Ricardo M.S. Carvalho, Daniele Cirulli, Quentin Dehaene, Martin Diaz, Alexandra Krasnokutskaya, Bernardo Pereira, Onkar Sadekar, Federico Battiston Modern play-by-play data make it possible to test long-standing intuitions about basketball with the same statistical rigour now routinely applied to other professional sports. Using play-by-play data from 7,054 regular-season and 504 playoff NBA games spanning the 2020-2025 seasons, we provide quantitative insights into scoring patterns and the performance of individual players and teams through methods from statistics, network science, and complexity science. Our findings offer an evidence-based perspective on in-season and in-game performance that can inform coaching strategies, player evaluation, and tactical decision-making. Read the full article at: arxiv.org

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