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

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

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

Read the full article at: arxiv.org

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

Read the full article at: arxiv.org

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

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

Ego networks capture the variety of structural patterns in the social interactions of individuals. Recently it has been shown that ego networks in online settings display universal patterns of tie strength distributions, but it is unclear how constraints such as spatial proximity and bounded social bandwidth affect such generic behaviour in offline settings. Here, we analyse the time evolution of interaction activity in ego networks constructed from offline face-to-face and colocation data, compare them to online communication networks, and explore simple cumulative advantage models that capture the varying preferentiality of individuals for specific social ties. We find that patterns of preferentiality at the population level are similar for online and face-to-face networks, but not for colocation data, suggesting that the latter is a poor proxy of social network structure. We also provide evidence that empirical ego networks exhibit a bandwidth in the way communication events are allocated across connections. A model implementing this notion uncovers evidence of universal scaling between the tie preferentiality and bandwidth of individuals, common to all online and offline systems explored. Our findings strengthen our understanding of the fundamental mechanisms governing human communication and help disentangle the internal and external factors shaping tie evolution across social contexts.

Read the full article at: arxiv.org

How are psychosocial profiles, mental health, and brain functional connectivity related? Studies have been dedicated to unraveling the associations of social support perception and neural functional connectivity. Additionally, personality traits have been explored by examining brain networks. Research on mental health has been developed using a broad range of methods and different approaches. However, little attention has been devoted to understanding how personality traits and social variables are related, and to what extent these components are reflected in brain functional connectivity and mental health outcomes. In this work, we aim to address these complex relations by using data from the Human Connectome Project, both from surveys and resting-state fMRI. The survey data includes personality traits measures and self-reported social support-related variables, which we will refer to as inner- and outer-self, respectively. It also includes data on mental health outcomes. Using z-score standardized measures, we analyze correlation matrices to evaluate the association between the inner- and outer-self domains. Our results show that the social indicators are more evidently grouped by impact on social experience than by the duality of inner-outer selves. Using a k-means clustering algorithm, we separate individuals into two groups according to social profiles. When confronting these results with the mental health outcomes, we show that the more socially desirable cluster exhibited a higher score on positive aspects such as life satisfaction and purpose in life. In the functional brain connectivity, we observe that the cluster with a more socially beneficial profile exhibits lower interconnectivity, especially in the default mode network. The pipeline we present uses a combined analysis of both fMRI and psychosocial variables, which could open the path for more extensive analysis.

Read the full article at: arxiv.org

Artificial intelligence is not replacing human intuition in these fields, but reimagining how questions are asked, explored and understood.

Read the full article at: www.nature.com

Guido Caldarelli, Oriol Artime, Giulia Fischetti, Stefano Guarino, Andrzej Nowak, Fabio Saracco, Petter Holme, Manlio De Domenico

Physics Reports Volume 1186, 5 August 2026, Pages 1-75

The boundaries between physical and social networks have narrowed with the advent of the Internet and its pervasive platforms. This has given rise to a complex adaptive information ecosystem where individuals and machines compete for attention, leading to emergent collective phenomena. The flow of information in this ecosystem is often non-trivial and involves complex user strategies—from the forging or strategic amplification of manipulative content to large-scale coordinated behavior—that trigger misinformation cascades, echo-chamber reinforcement, and opinion polarization. We argue that statistical physics provides a suitable and necessary framework for analyzing the unfolding of these complex dynamics on socio-technological systems. This review systematically covers the foundational and applied aspects of this framework. The review is structured to first establish the theoretical foundation for analyzing these complex systems, examining both structural models of complex networks and physical models of social dynamics (e.g., epidemic and spin models). We then ground these concepts by describing the modern media ecosystem where these dynamics currently unfold, including a comparative analysis of platforms and the challenge of information disorders. The central sections proceed to apply this framework to two central phenomena: first, by analyzing the collective dynamics of information spreading, with a dedicated focus on the models, the main empirical insights, and the unique traits characterizing misinformation; and second, by reviewing current models of opinion dynamics, spanning discrete, continuous, and coevolutionary approaches. In summary, we review both empirical findings based on massive data analytics and theoretical advances, highlighting the valuable insights obtained from physics-based efforts to investigate these phenomena of high societal impact.

Read the full article at: www.sciencedirect.com

Arthur Montanari, Ana Elisa Barioni, and Adilson Motter

In a murmuration of starlings, abrupt evasive maneuvers from a few birds in response to a passing falcon can trigger a collective response across the whole group. Within a fraction of a second, local turns are amplified through thousands of neighboring interactions between birds, and the entire flock twists and folds as if it were a single organism. During the annual northbound migration of sardines along the coast of South Africa, dense schools rapidly reorganize into spinning bait balls when dolphins approach, using collective geometry to confuse predators and dilute individual risk. On land, herds of millions of wildebeest coordinate traveling direction and timing across open plains and narrow passages during their yearly migration throughout the Serengeti. Desert locusts also march across long distances in the Sahel and Arabian Peninsula, producing vast swarms that move as a unit when tactile stimulation and high population density trigger a phase transition from individualistic to coordinated behavior in the form of rolling waves.

Read the full article at: www.siam.org

Recent research is exploring self-organised traffic management as a solution for scaling to complex real-world networks. In such a system, trains predict their neighbourhood, produce traffic plan hypotheses, and agree via consensus with neighbours on a future traffic plan to be implemented. This paper investigates a structural parameter within this pipeline: the predictive neighbourhood horizon. The horizon is used by trains to identify future potential conflicts with neighbours, and to establish the local interaction topology, that is, the subset of trains to negotiate with. As the primary design variable, the horizon directly determines the size and density of the social interaction graph, whereas its impact on the complexity of local sub-problems and the distributed consensus dynamics represents a trade-off to be explored. Through a closed-loop simulation framework the study evaluates how variations of the horizon impact the overall decentralised coordination process, from initial conflict detection to distributed schedule consensus. The analysis focuses on investigating the potential trade-off introduced by the horizon choice: balancing local tractability and computational responsiveness with the need for global schedule coherence and feasibility in safety-critical environments. Contrary to intuition, our empirical results indicate that the short time horizons suffice, while long values compromise local tractability and computational responsiveness with no gain in global schedule optimality.

Read the full article at: arxiv.org

Overproduction of Elites and Social Instability: A Short Introduction to Cliodynamics
A conversation with Peter Turchin, interviewed by Nicolas Sperry-Guillou

​Watch at: www.youtube.com

A new term has been coined to describe AI in its current form: “jagged intelligence.” The term captures the fact that the landscape of AI capabilities is profoundly uneven: the tools demonstrate excellent abilities on certain problems but surprising failures on other similar problems. For humans, one kind of skill can often predict abilities at similar skills; this is not the case in the jagged landscape of AI. Last fall, Ilya Sutskever, a cofounder of OpenAI, argued that there are no easy fixes to this problem: “These models somehow just generalize dramatically worse than people. It’s a very fundamental thing.”

Read the full article at: yalereview.org

Despite raising concerns about the mental health effects associated with the usage of TikTok, little is known about how related content is framed by creators and received by audiences. We collect the content of 28,341 TikTok videos and 80,130 comments from Mental Health Awareness Month (May) in 2023 and 2024 via the TikTok Research API, and study how the tone of awareness varies across topics and years. We characterize “tone” as the emotional and interpersonal framing of mental health discourse, operationalized through sentiment and toxicity measures. We extract topics from video text using BERTopic and log-odds keywords, then quantify topic-conditioned sentiment (XLM-T) and toxicity (Detoxify) separately for video transcriptions and comments. Sentiment captures the affective valence of content, while toxicity reflects the presence of harmful or abusive language. We find a stable set of recurring themes across years, spanning clinical conditions, emotional disclosure, self-care, and campaign-oriented content, with engagement highly skewed toward a small subset of topics. All sentiment and toxicity analyses are computed separately for video content and comments, allowing us to distinguish between content production and audience reception. Sentiment in videos is often negative for emotionally charged topics, while comments tend to shift toward more mixed or positive polarity, especially for suicide prevention. Toxicity is low in median overall, but exhibits longer-tailed outliers in comments than in videos that are more pronounced in comments and concentrated in specific topics (e.g., “Duet”, “Suicide Prevention”, and “Psychisch”). Overall, our results provide a topic-level decomposition of mental health discourse on TikTok during awareness-month campaigns.

Read the full article at: arxiv.org

The Great Filter hypothesis proposes that the emergence of technological societies capable of interstellar travel depends on a small number of exceptionally hard and highly improbable steps. Traditional versions of this hypothesis enumerate such “hard steps” along the trajectory from inanimate matter to complex technological societies, but diverge in their explanations for why these particular steps should be so improbable. The theory of Major Evolutionary Transitions also faces challenges in identifying which steps should be considered universally “hard” across different evolutionary pathways. In contrast, we argue that two deeply structural obstacles dominate the evolutionary landscape: the coding threshold associated with the origin of the genetic code, and the language threshold associated with the emergence of symbolic communication. We examine the developmental precursors of both transitions and analyze the underlying algorithmic bottlenecks: points at which evolving systems separate code from function, while entangling them within information hierarchies. Using a game-theoretic analysis of coupled signaling and coordination dynamics, we then argue that the corresponding multichannel games exhibit unstable equilibria that render the transitions intrinsically difficult. We conjecture that the so-called Great Filter is best understood not as a sequence of isolated improbable events, but as a nested structure of tangled information hierarchies. Under this interpretation, the rarity of advanced societies follows from the difficulty of crossing these coding thresholds in a competitive noisy environment. This perspective reframes the Great Filter as an algorithmic property of evolving systems, highlighting why only a vanishingly small fraction of life may ever traverse the path toward technological societies capable of interstellar travel.

Read the full article at: arxiv.org

Read the full article at: www.mdpi.com

Phys. Rev. Lett. 136, 207401

We study diffusion on multiplex networks with directed interlayer couplings. We demonstrate both numerically and analytically that even with undirected layers, interlayer directionality alone reproduces superdiffusion and the prime regime. We further reveal a new phenomenon, the directionality-induced jamming, whereby directed interlayer links hinder diffusion, fragmenting the system into dynamically disconnected components and preventing convergence to the steady state of the diffusion process. Via an optimization process, we show that this new regime is attainable in both toy models and real-world topologies. These findings underscore the crucial role of interlayer link directionality in shaping the emergent behavior of multiplex systems, with potential implications for the design and control of such systems.

Read the full article at: journals.aps.org

Elite football is believed to have evolved in recent years, yet systematic evidence for the pace and form of that change remains sparse. Drawing on event-level records for 13,018 matches across ten top-tier men’s and women’s leagues in England, Spain, Germany, Italy, and the United States (2020–2025), we quantify match dynamics through two complementary lenses: conventional performance statistics and pitch-passing networks that track ball movement across spatial regions of the field. Between 2020 and 2025, average passing volume, pass accuracy, and the proportion of passes made under pressure all increased, with the largest year-on-year changes occurring in women’s competitions. Network measures reveal that normalized outreach decreased, indicating teams increasingly concentrate ball circulation into shorter-range passing connections rather than wide spatial distribution. These trends are consistent across countries and tiers, yet persistent national differences indicate that stylistic diversity remains. Notably, women’s competitions exhibit stronger rates of change across most metrics, consistent with an accelerating professionalization, while the systematic decline in network outreach across all competitions is consistent with a sport-wide shift toward shorter, more concentrated passing structures.

Read the full article at: www.nature.com

Money serves several roles: a medium of exchange to buy and sell without bartering; a unit of account to price goods consistently; a store of value to save purchasing power over time; a means to defer payment of future obligations like credit or loans. An agent based computer simulation program determine quantitatively the relative importance of these services. The main results showed that money for credit was by far the feature that achieved the largest overall production of wealth in the simulated societies. A conclusion from this study suggests that fomenting the use of internationally tradable currencies such as Bitcoin seems to be most promising pathway for international economic growth in the near future.

Read the full article at: papers.ssrn.com

FORESIGHTERS (Future ORiEnted Skills, Innovation for Governance for Health, Technology, EneRgy, citieS and creativity) offers unique opportunities to build a future research career that tackles complex societal problems. While researchers will be based in academic units across the university, from engineering, humanities, social science and the arts, they will be working within one of 5 interdisciplinary research themes (Health, Technology, Energy, Future Cities, and Creativity). The cohort of researchers will be bound by a common perspective and training in Futures Thinking and Futures Literacy and will develop the skills and capabilities to understand, imagine, and critically use different ideas about the future to better understand key challenges. In addition to advanced training offered by the Thomas Moran Graduate School, FORESIGHTERS ESRs will be trained in skills for anticipating change and using multiple possible futures to stimulate reflection, creativity, resilience and focus for contemporary action. The training programme also has a strong emphasis on the development of sustainable, diverse, and equitable research environments.

More at: www.qub.ac.uk

Active matter generates order or patterns through nonequilibrium dynamics. An open research challenge is to determine how efficiently a nonequilibrium self-organising system can convert consumed energy into macroscopic order. We study an information-theoretic quantity that directly addresses this challenge by estimating the entropy reduction induced by a small control-parameter perturbation, relative to the generalised work required for the perturbation. This quantity has previously been considered mainly in an equilibrium or near-equilibrium context, and here we extend this framework and apply it to two nonequilibrium self-organising systems: persistent and active Ising models. We observe that the thermodynamic efficiency of nonequilibrium systems maximises at phase transitions, as in equilibrium systems. Furthermore, we compare thermodynamic efficiency and inferential efficiency across control parameters. While these two quantities are equal in equilibrium as a consequence of the fluctuation-dissipation theorem, we report that they diverge out of equilibrium, and the gap reflects how far the system is from equilibrium.

Read the full article at: arxiv.org

We see consciousness in AI the same way we see faces in clouds, says neuroscientist Anil Seth. He explores the all-too-human tendency to project inner life onto machines that are brilliant mimics, not sentient beings, and gives a definitive answer to the urgent question: Will AI ever gain consciousness?

Watch at: www.youtube.com

Regional Economic Integration is a process by which countries seek mutual benefits through the reduction of trade, social, and political barriers. This paper introduces a computational mathematical model grounded in Economic Complexity Theory to analyze economic blocs as unified entities. Four case studies are examined: the European Union, MERCOSUR, URUPABOL, and the Andean Community. Using real export data and complexity metrics, we identify the combined productive capacities of member countries. Results reveal that integration enhances product diversity and increases the ubiquity of exports within the bloc. The study demonstrates that regional integration boosts development and strengthens competitiveness in the global economy. The proposed methodological approach provides a novel tool for regional analysis and serves as a foundation for future strategies in economic cooperation and productive planning. This research contributes to understanding how collective capabilities can generate synergies that exceed individual national potentials, particularly in the context of Latin American regional development.

Read the full article at: ieeexplore.ieee.org

Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. However, effective learning algorithms for spiking networks remain elusive, although it is suspected that effective plasticity mechanisms could alleviate the problem of data efficiency. Here, we present a new framework for spiking neural networks—Spark (https://github.com/Nogarx/Spark)—built upon the idea of modular design, from simple components to entire models. The aim of this framework is to provide an efficient and streamlined pipeline for spiking neural networks. We showcase this framework by solving the sparse-reward cartpole problem with simple plasticity mechanisms. We hope that a framework compatible with traditional ML pipelines may accelerate research in the area, specifically for continuous and unbatched learning, akin to the one animals exhibit

Read the full article at: www.frontiersin.org

Through a combination of lectures, hands-on coding sessions, and interdisciplinary discussions, participants will explore how emergent phenomena—such as financial crises, innovation diffusion, urban growth, and collective decision-making—arise from decentralized interactions among agents.

Many of today’s most pressing social and economic challenges exhibit emergent properties that traditional equilibrium-based methods struggle to explain.

This Summer School addresses that gap by equipping participants with state-of-the-art tools from complexity science, enabling them to analyze systems where collective behavior and adaptation drive outcomes.

Enroll at: bse.eu

The universe creatively sets the rules for its own becoming.

Stuart Kauffman is a theoretical biologist and leading complexity scientist who has argued that the self-organization of organisms is as influential in evolution as natural selection. His seminal book on the subject is “The Origins of Order: Self-Organization and Natural Selection in Evolution” (1993). He spoke recently with Noema Editor-in-Chief Nathan Gardels.

Read the full article at: www.noemamag.com

Multilayer network science has emerged as a central framework for analysing interconnected and interdependent complex systems. Its relevance has grown substantially with the increasing availability of rich, heterogeneous data, which makes it possible to uncover and exploit the inherently multilayered organisation of many real-world networks. In this review, we summarise recent developments in the field. On the theoretical and methodological front, we outline core concepts and survey advances in community detection, dynamical processes, temporal networks, higher-order interactions, and machine-learning-based approaches. On the application side, we discuss progress across diverse domains, including interdependent infrastructures, spreading dynamics, computational social science, economic and financial systems, ecological and climate networks, science-of-science studies, network medicine, and network neuroscience. We conclude with a forward-looking perspective, emphasizing the need for standardised datasets and software, deeper integration of temporal and higher-order structures, and a transition toward genuinely predictive models of complex systems.

Read the full article at: academic.oup.com

Complex Systems, 34(4), 2026 pp. 387–400.

Complexity, a term that is both ambiguous and multifaceted, is used widely today. Various legitimate definitions can be proposed for it, as is the case with “ample” notions such as intelligence, consciousness or culture. The recurrent mention of this term can be attributed to the transformation of our societies and their artifacts, as well as the acceleration of time brought by the digital revolution—a technological upheaval comparable to the invention of writing and the printing press.

Read the full article at: www.complex-systems.com

In recent years, large-scale models and automated systems have made it possible to synthesize vast amounts of scientific information. However, identifying what is foundational — what truly shapes the conceptual and methodological backbone of a field — still requires distributed expert judgment. This effort is designed to complement algorithmic approaches by leveraging collective intelligence: many independent perspectives, aggregated into a structured view.

The objective is not to produce a simple ranking of famous papers, but to build a structured map of the field’s foundations, including works that may be missing from keyword-based or citation-based approaches.

Read the full article at: manliodedomenico.com

The integrated information theory of consciousness (IIT) is uniquely ambitious in proposing a mathematical formula, derived from apparently fundamental properties of conscious experience, to describe the quantity and quality of consciousness for any physical system that possesses it. IIT has generated considerable debate, which has engendered some misunderstandings and misrepresentations. Here we address and hope to remedy this. We begin by concisely summarising the essentials of IIT. Given IIT is supposed to apply universally, we do this with reference to an arbitrary patch of matter, as opposed to the usual system of discrete computational units. Then, after briefly summarising IIT’s theoretical and empirical achievements, we focus on five points which we consider especially important for driving forward new theory and increasing understanding. First, a high value of the measure Φ is not synonymous with `more consciousness’. We describe how Φ might be replaced with a suite of quantities to obtain a multi-dimensional characterisation of states of consciousness. Second, we describe with nuance the distinct flavour of panpsychism implied by IIT — whereby space (and time) are tiled with substrates of (proto-) consciousness — and find this is not problematic for the theory. Third, Φ is not well-defined for real physical systems, and has not been computed on any real physical system. Fourth, so far only proxies for IIT measures have been computed, and not approximations. Fifth, for IIT to fit with current successful theories in fundamental physics, a reformulation in terms of continuous fields would be needed.

Read the full article at: arxiv.org

More at: ccs26.cssociety.org

PNAS 123 (18) e2511050123

The term “invisible college” refers to communication networks that help scientists exchange information and advance knowledge. These networks create social capital, granting access to resources like new ideas and support. Measuring those intangible exchanges is an empirical challenge. Here we approximate these ties through the analysis of the “thank you” notes appended to journal articles. Our findings show that scholars disconnected from this layer of academic social capital have lower publication impact. We also show that informal ties provide support not captured by coauthorship ties, which reflect a more rigid form of collaboration. Documenting how informal structures of support operate can help leverage collective resources in the pursuit of shared intellectual goals.

Read the full article at: www.pnas.org

Read the full article at: www.mivideo.it.umich.edu

Entropy 2026, 28(4), 398

Social media platforms offer unprecedented opportunities to study cultural evolution by analyzing digital traces. This study presents a methodological framework for analyzing the temporal dynamics of cultural modules in hashtag co-occurrence networks. We address the inherent challenges of analyzing dense, skewed, and highly variable cultural networks by introducing a perturbation ensemble clustering approach that distinguishes stable from unstable structural elements. By applying the Leiden algorithm to a perturbed ensemble of hashtag networks, we identify robust core modules and their stable periphery, and distinguish them from floating elements with unstable associations. Analysis of four years of data from a major photo-sharing platform reveals complex patterns in the evolution of cultural modules, including both stable associations and dynamic reorganizations. Our findings demonstrate how ensemble clustering techniques can effectively capture the interplay between stability and change in evolving cultural systems.

Read the full article at: www.mdpi.com

Organizations learn from market, political and societal responses to their actions. While in some cases both the actions and responses take place in an open manner, in many others, some aspects may be hidden from external observers. The Eurovision Song Contest offers a mostly open-data case in which to study organizational level learning at the levels of organizers and participants. We present here evidence for changes in the rules of the Contest in response to undesired outcomes such as runaway winners. We also find strong evidence of participant learning in the characteristics of competing songs over the 70 years of the Contest. English has been adopted as the lingua franca of the competing songs and pop has become the standard genre. The number of words of lyrics has also grown in response to this collective learning. Remarkably, we find evidence that France, Italy, Portugal and Spain have chosen to ignore the ‘lesson’ that English lyrics increase winning probability, consistent with utility functions that award greater value to featuring national culture than to winning the Contest. These countries—but not Germany—appear to be less susceptible to Anglo-Saxon cultural influence than their peers, a resistance that may extend beyond cultural matters.

Read the full article at: royalsocietypublishing.org

Chaos, Solitons & Fractals
Volume 208, Part 4, July 2026, 118382

This study modifies the model in the previous studies and considers three types of inter-individual relationships: regular, random, and scale-free ring lattices. Furthermore, we introduce defectors, who do not contribute to the public goods; cooperators, who contribute to the public goods; and loners, who do not participate in the public goods framework. We assume that each of these three types of individuals punishes other individuals with a probability proportional to the difference between their own payoff and their opponent’s average payoff including them. Using this modified pool punishment model, this study shows the following. Firstly, the damage to the average payoff due to excessive punishment is kept significantly low. Secondly, antisocial punishment is not evolutionarily advantageous, and cooperators always become advantageous. Finally, the final average payoff is always higher than that of pool punishment in existing studies and roughly comparable to that of peer punishment in existing studies. The results of this study provide new insights that the claim of the existing study is not always correct; that is, even if antisocial punishment is possible, it does not have an evolutionary advantage, and cooperators always become advantageous, which in turn solves the problem of antisocial punishment. This study is being conducted as part of efforts to improve specialized education at Kanagawa Institute of Technology.

Read the full article at: www.sciencedirect.com

What kind of trouble lies ahead? How can we successfully transition towards a sustainable future? Drawing on a remarkably broad range of insights from complex systems and the functioning of the brain to the history of civilizations and the workings of modern societies, the distinguished scientist Marten Scheffer addresses these key questions of our times. He looks to the past to show how societies have tipped out of trouble before, the mechanisms that drive social transformations and the invisible hands holding us back. He traces how long-standing practices such as the slave trade and foot-binding were suddenly abandoned and how entire civilizations have collapsed to make way for something new. Could we be heading for a similarly dramatic change? Marten Scheffer argues that a dark future is plausible but not yet inevitable and he provides us instead with a hopeful roadmap to steer ourselves away from collapse-and toward renewal.

More at: www.cambridge.org

SCIENCE 9 Apr 2026 Vol 392, Issue 6794 pp. 178-182

The wildlife trade affects a quarter of terrestrial vertebrates and creates opportunities for cross-species pathogen transmission, but its precise role in shaping animal-human pathogen exchange remains unclear. In our analysis of 40 years of global wildlife trade data, we show that traded mammals are 1.5-fold as likely to share pathogens with humans as nontraded mammals, and that illegal and live-animal trade further exacerbate pathogen sharing. Time spent in trade predicts the number of zoonotic pathogens that a wildlife species hosts. On average, a species shares an additional pathogen with humans for every 10 years it is traded.

Read the full article at: www.science.org

Complexities 2026, 2(1), 7

Shapley values are the most widely used point-valued solution concept for cooperative games and have recently garnered attention for their applicability in explainable machine learning. Due to the complexity of Shapley value computation, users mostly resort to Monte Carlo approximations for large problems. We take a detailed look at an approximation method grounded in multilinear extensions proposed in 2021 under the name “Owen sampling”. We point out why Owen sampling is biased and propose unbiased alternatives based on combining multilinear extensions with stratified sampling and importance sampling. Finally, we discuss empirical results of the presented algorithms for various cooperative games, including real-world explainability scenarios.

Read the full article at: www.mdpi.com

The metaverse is a virtual space enabling interactions beyond geographical boundaries, promising to transform how people engage with each other both in the digital and the physical worlds. The lack of geographical boundaries and travel costs in the metaverse prompts us to ask if the fundamental laws that govern human mobility in the physical world apply. We collected data on avatar movements from Decentraland, along with their network mobility extracted from NFT purchases on Ethereum and Polygon. We find that despite the absence of mobility costs, an individual’s inclination to visit new locations diminishes over time, limiting movement to a small fraction of the metaverse. We also find a lack of correlation between land prices and visitation, a deviation from the patterns characterizing the physical world. Finally, we identify the scaling laws that characterize meta mobility and show that we need to add preferential selection to the existing models to explain quantitative patterns of metaverse mobility. Our ability to predict the characteristics of the emerging meta mobility network implies that the laws governing human mobility are rooted in fundamental patterns of human dynamics, rather than the nature of space and cost of movement.

Read the full article at: www.nature.com

The 12th International Conference on Guided Self-Organization takes place during October 14-15, 2026 in Binghamton, NY (USA), during The 2026 Conference on Complex Systems (CCS 2026) . GSO-2026 is organized by The State University of New York at Binghamton and The International Association for Guided Self-Organization (TIA-GSO).

Research Aims and Topics

GSO “aims to regulate self-organization for specific purposes, so that a dynamical system may reach specific attractors or outcomes. The regulation constrains a self-organizing process within a complex system by restricting local interactions between the system components, rather than following an explicit control mechanism or a global design blueprint.” 

Information processing in complex self-organizing systems involves the storage, transfer, and modification of information through the interactions of components within the system. Unlike traditional computers, which process digital information in a centralized manner, complex systems like biological organisms or social networks process information in decentralized, distributed, and often analog ways. The study of information processing in complex systems seeks to define a set of universal properties that can describe the dynamics of diverse systems, from brain networks to financial markets, using a common language. Understanding information processing in complex systems is fundamental to designing self-organizing systems, engineering collective behavior and developing energetically efficient models of computation. Modern approaches use frameworks from fields such as information theory, dynamical systems, and machine learning to model how systems ranging from economies to ant colonies process information.

The GSO-2026 conference will bring together invited experts and researchers in unconventional computation, swarm intelligence, open-ended evolution, and complex adaptive systems. Special topics of interest include: synthetic and systems biology, agent-based modeling, evolutionary and adaptive computation, socio- and bio-inspired algorithms, swarm robotics, physics of self-organizing behavior, information-driven self-organization, and self-organizing cyber-physical systems.

More at: www.guided-self.org

We call for submissions of abstracts for oral and poster presentations on a wide variety of complex systems research. Relevant topics include (but are not limited to):

• Theoretical foundations of complex systems
• Nonlinear dynamics and chaos
• Systems theory, information theory, and systems science
• Game theory, decision theory, and socio-economical applications
• Self-organization, pattern formation, and collective behavior
• Structure and dynamics of complex networks
• Sustainability and adaptability of complex systems
• Bio-inspired systems, machine learning, and evolutionary computation
• Data-driven approaches to complex systems
• Applications to the humanities, art, and literature
• Historical and philosophical aspects of complex systems
• Complex systems and education

More at: ccs26.cssociety.org

Read the full article at: ui.adsabs.harvard.edu

When honeybee colonies reproduce by fission, several thousand bees and their queen depart the parental nest and temporarily form a dense cluster on a tree branch or other surface while searching for a new nest site. Once the new nest site is selected, the swarm disassembles and flies toward it. How honeybees transition rapidly between dispersed flight and an aggregated cluster remains an open question. Here, we develop an experimental system and three-dimensional imaging pipeline to track individual flying bees together with the evolving morphology of the swarm during formation and dissolution. We report results from a representative swarming event. During assembly, swarms rapidly form low-density clusters before undergoing a slower contraction to a more dense steady state configuration. In contrast, disassembly occurs significantly faster than assembly and is characterized by strongly divergent flight, with bees departing the swarm in all directions. Overall, this method is able to demonstrate the coupled flight and morphological dynamics that underlie honeybee swarm assembly. Because the system is relatively low-cost and low-power, it is readily adaptable for three-dimensional imaging of other biological collectives in naturalistic environments.

Read the full article at: www.biorxiv.org

People increasingly consult generative artificial intelligence (AI) while reasoning. As AI becomes embedded in daily thought, what becomes of human judgment? We introduce Tri-System Theory, extending dual-process accounts of reasoning by positing System 3: artificial cognition that operates outside the brain. System 3 can supplement or supplant internal processes, introducing novel cognitive pathways. A key prediction of the theory is “cognitive surrender”-adopting AI outputs with minimal scrutiny, overriding intuition (System 1) and deliberation (System 2). Across three preregistered experiments using an adapted Cognitive Reflection Test (N = 1,372; 9,593 trials), we randomized AI accuracy via hidden seed prompts. Participants chose to consult an AI assistant on a majority of trials (>50%). Relative to baseline (no System 3 access), accuracy significantly rose when AI was accurate and fell when it erred (+25/-15 percentage points; Study 1), the behavioral signature of cognitive surrender (AI-Accurate vs. AI-Faulty contrast; Cohen’s h = 0.81). Engaging System 3 also increased confidence, even following errors. Time pressure (Study 2) and per-item incentives and feedback (Study 3) shifted baseline performance but did not eliminate this pattern: when accurate, AI buffered time-pressure costs and amplified incentive gains; when faulty, it consistently reduced accuracy regardless of situational moderators. Across studies, participants with higher trust in AI and lower need for cognition and fluid intelligence showed greater surrender to System 3. Tri-System Theory thus characterizes a triadic cognitive ecology, revealing how System 3 reframes human reasoning and may reshape autonomy and accountability in the age of AI.

Read the full article at: papers.ssrn.com

Read the full article at: arxiv.org

Collective action has been a fundamental aspect of human societies throughout history, from building irrigation systems and defenses in Neolithic times to coordinated disaster relief and scientific collaborations today. In this book, Sergey Gavrilets explains when and why groups of people cooperate, presenting a quantitative framework that unifies game theory with models of social influence, cognition, and individual and cultural variation. He shows how humans’ deep susceptibility to social influence—grounded in evolutionary need to cooperate and learn from peers, reinforced by deference to parents and elders, and extended to cultural, religious, and political leaders—shapes norms, beliefs, and collective outcomes.

Integrating previously separate literatures, Gavrilets introduces explicit dynamics for norms and beliefs, quantifies the effects of individual and cultural differences, and tests predictions across societies. Drawing on formal, data-based mathematical modeling supported by behavioral experiments and studies of online behavior, he concludes that successful collective action depends on six interacting forces: material payoffs, personal norms and attitudes, social influence, cognition, evolving social norms and beliefs about others, and individual and cultural differences. Lasting cultural change, he argues, depends on norms and institutions that shape behavior through persuasion, nudging, and enforcement. Gavrilets translates this theory into practical, testable strategies for policy and design, including targeted messaging, dynamic norms, and culturally sensitive approaches, and connects it to broader theories of behavior change.

More at: press.princeton.edu