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

Read the full article at: arxiv.org

Watch at: vimeo.com

The American Naturalist

Environmental variability, in the form of either temporal fluctuations or intermittent perturbations, affects virtually all ecological systems. However, while temporal variability is widely recognized to play an important role across many ecological and evolutionary subdisciplines, there is no high-level cross-cutting concept that describes how species, communities, and ecosystems respond to variability. In this article we propose that “antifragility” could serve well as such a concept. Initially used in economics, antifragility denotes that a property or metric of performance increases with variability. To showcase the breadth of applicability and utility of the concept, we examine two mathematical models for antifragility in ecosystem services and competition. We also demonstrate some of the nuances and possible misapplications of the concept. Under global change, the variability of environmental conditions is expected to change. We believe that antifragility could serve as a useful concept in coordinating research efforts toward understanding the effects of these changes.

Read the full article at: www.journals.uchicago.edu

PNAS 123 (7) e2509729123

Diversification and specialization are central to complex adaptive systems, yet overarching principles across domains remain elusive. We introduce a general theory that unifies diversity and specialization across disparate systems, including microbes, federal agencies, companies, universities, and cities, characterized by two key parameters. We show from extensive data that function diversity scales with system size as a sublinear power law-resembling Heaps’ law-in all but cities, where it is logarithmic. Our theory explains both behaviors and suggests that function creation depends on system goals and structure: federal agencies tend to ensure functional coverage; cities slow new function growth as old ones expand, and cells occupy an intermediate position. Once functions are introduced, their growth follows a remarkably universal pattern across all systems.

Read the full article at: www.pnas.org

Dashun Wang

As artificial-intelligence systems take on more of the scientific workflow, the central goal should not be complete automation, but designing platforms that preserve creativity, responsibility and surprise.

Read the full article at: www.nature.com

PNAS Nexus, Volume 5, Issue 2, February 2026, pgag010,

The term emergence is increasingly used across scientific disciplines to describe phenomena that arise from interactions among a system’s components but cannot be readily inferred by examining those components in isolation. While often invoked to explain higher-level behaviors—such as flocking, synchronization, or collective intelligence—the term is frequently used without precision, sometimes giving rise to ambiguity or even mystique. In this perspective paper, I clarify the scientific meaning of emergence as a measurable and physically grounded phenomenon. Through concrete examples—such as temperature, magnetism, and herd immunity in social networks—I review how collective behavior can arise from local interactions that are constrained by global boundaries. By refining the concept of emergence, it is possible to gain a clearer and more grounded understanding of complex systems. My goal is to show that emergence, when properly framed, offers not mysticism, but rather insight.

Read the full article at: academic.oup.com

The contemporary global economy exhibits unprecedented structural complexity—characterized by nonlinear dynamics, adaptive behaviors, and emergent properties. Understanding these phenomena requires theoretical frameworks capable of addressing complexity, path dependence, and evolutionary processes.

Complexity economics has developed to address such intellectual challenges. Originating in a seminal 1987 Santa Fe Institute workshop and first described in The Economy as an Evolving Complex System (1988), this approach fundamentally reconceptualizes economic systems as complex adaptive systems. Subsequent volumes (1997, 2005) progressively developed this framework, offering new insights into finance, technological innovation, and social interactions.

Like each of its predecessors, this fourth volume is the product of an interdisciplinary workshop hosted at the Santa Fe Institute. It represents the latest synthesis, reflecting theoretical advances and methodological developments achieved over nearly four decades. Drawing on contributions from leading scholars worldwide, the chapters span foundational questions to policy applications—from agent-based modeling and network theory to macroeconomic dynamics, innovation systems, sustainability transitions, and inequality.

The result demonstrates complexity economics’ capacity to generate novel insights into phenomena that remain puzzling within traditional frameworks: financial instability, technological disruption, climate economics, and institutional change. This volume positions complexity economics as an essential analytical framework for understanding twenty-first-century economic realities.

More at: www.sfipress.org

Read the full article at: arxiv.org

Read the full article at: arxiv.org

Advances in Complex Systems Vol. 28, No. 08, 2530001 (2025)

Network analysis has become a powerful tool in various fields. However, the increasing popularity comes with potential problems. Unfamiliarity with the characteristics of the systems under investigation complicates network model construction and interpretation of analysis outcomes. While these issues require special attention in studies that apply the increasingly complex higher-order connectivity models, similar problems are associated with all, even the most simple, network models. Alongside technical issues, network scientists face a philosophical question: can the network approach discover the fundamental nature of a system, on the one hand, and produce useful information, on the other hand. In this perspective, I review the potential problems of the network approach and propose two solutions to address them: active evaluation of the potential and limitations of the network framework before applying a network model and a transition toward an interdisciplinary research practice to interpret analysis outcomes in their right context.

Read the full article at: www.worldscientific.com

This paper introduces a constraint-limited model of combinatorial growth that examines how feasibility scales with increasing system dimensionality. The framework analyzes the balance between expanding possibility spaces and constraint structures that prune feasible configurations. The model shows that when feasible configurations grow as c^n within a combinatorial space of size 2^n, the feasible fraction collapses for constant c < 2. Sustained novelty generation therefore requires c(n) to approach the combinatorial base, producing a narrow “complexity corridor” between regimes of trivial repetition and combinatorial sparsity. The paper derives the analytic structure of this corridor and explores it through numerical simulations and visualizations. The results suggest a possible structural explanation for why complex systems may emerge only within a narrow range where combinatorial expansion and constraint relaxation operate at comparable scales.  The manuscript includes the full mathematical derivation, simulation results, and discussion of implications for complex systems.

Read the full article at: zenodo.org

Georgi Yordanov Georgiev

BioSystems

Volume 262, April 2026, 105647

Read the full article at: www.sciencedirect.com

See Also: Part II: Empirical estimation, Average Action Efficiency, and applications to ATP synthase

Most of the everyday phenomena we see around us can be categorized as “complex.” Such systems consist of many strongly interacting parts and yet, despite this, they exhibit a certain emergent qualitative unity which endows them with a distinct being, separate, although not independent, from that of their constituent elements.
These complex systems thus possess a kind of “simplicity” as well, which makes them intelligible and allows them to be studied in their own right. The sheer diversity of complex phenomena—from magnets to climate to the economy to the human brain—prevents them from being investigated under a single theoretical framework. Still, studies such as those of Lorenz and Mandelbrot in the 1970s began to reveal a surprisingly large number of common motifs across these systems, including transitions to chaos, fractal structures, pattern formation, and more.
The search for common features of complex systems still remains open. However, most efforts today are focused on understanding particular phenomena. The “Belgrade School of Complex Systems,” organized by the Faculty of Physics at the University of Belgrade (http://www.ff.bg.ac.rs/Engleski/index_eng.html), is an attempt to bring together experts from around the world working on various fields that fall under the broad category of complex systems in order to encourage the exchange of knowledge and promote collaboration between like-minded researchers that may be working in seemingly disparate fields.

More at: becomplex.net

Junhua Yuan 
Nature Physics (2026)

Spontaneous switching between active and inactive states in bacterial chemosensory arrays is shown to operate near a critical point. Through biologically controlled disorder, cells balance high signal gain with fast response.

Read the full article at: www.nature.com

Research Policy Volume 55, Issue 4, May 2026, 105454

Efforts to apply economic complexity to identify diversification opportunities often rely on diagrams comparing the relatedness and complexity of products, technologies, or industries. Yet, the use of these diagrams, is not based on empirical or theoretical evidence supporting some notion of optimality. Here, we introduce an optimization-based framework that identifies diversification opportunities by minimizing a cost function capturing the constraints imposed by an economy’s pattern of specialization. We show that the resulting portfolios often differ from those implied by relatedness–complexity diagrams, providing a target-oriented optimization layer to the economic complexity toolkit.

Read the full article at: www.sciencedirect.com

Read the full article at: arxiv.org

The collective behavior of numerous animal species, including insects, exhibits scale-free behavior indicative of the critical (second-order) phase transition. Previous research uncovered such phenomena in the behavior of honeybees, most notably the long-range correlations in space and time. Furthermore, it was demonstrated that the bee activity in the hive manifests the hallmarks of the jamming process. We follow up by presenting a discrete model of the system that faithfully replicates some of the key features found in the data, such as the divergence of correlation length and scale-free distribution of jammed clusters. The dependence of the correlation length on the control parameter, density, is demonstrated for both the real data and the model. We conclude with a brief discussion on the contribution of the insights provided by the model to our understanding of the insects’ collective behavior.

Read the full article at: link.aps.org

The Emerging Researcher Award recognizes promising researchers in Complex Systems within 3 years of their PhD defense.

The Junior Scientific Award is aimed at recognizing excellent scientific record of young researchers within 10 years of their PhD defense.

The Senior Scientific Award will recognize outstanding contributions of Complex Systems scholars at any stage of their careers.

Deadline: April 30th, 2026.

See https://cssociety.org/community/awards for the list of previous awardees.

More at: cssociety.org

PNAS 123 (7) e2522998123

People often get public opinion wrong, assuming their own views are unpopular when in fact many others share them. This widespread misperception, called pluralistic ignorance, can trap societies in harmful or outdated norms. We build a mathematical model showing how these misperceptions form and change over time, depending on whether cultures are “tight” (with strict norms) or “loose” (with flexible ones). Our results explain why support for issues like climate action or women’s rights is often underestimated, and why change happens faster in some societies than others. The model also points to practical solutions: in loose cultures, sharing accurate information works best, while in tight ones, lowering the costs of speaking up can spark social change.

Read the full article at: www.pnas.org

Network Science Colloquium Series, 09/24/2025

In 1905 the biologist Edmund Selous wrote of his wonderment when observing a flock of starlings flying overhead “they circle; now dense like a polished roof, now disseminated like the meshes of some vast all-heaven-sweeping net…wheeling, rending, darting…a madness in the sky”. He went on to speculate “They must think collectively, all at the same time, or at least in streaks or patches — a square yard or so of an idea, a flash out of so many brains”. Today, we still know relatively little about how the network of social interactions connect brains—and thus how sensing and information processing arises in such organismal collectives. Employing automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) experiments, I will discuss newly-discovered geometric principles of collective decision-making that occur across scales of biological organization; from neural networks to the social networks of animal groups. I will also show how this finding can impact humans, including how it can be translated to highly effective control laws for swarming robots, as well as how it has transformed our understanding of locust swarms, one of the most destructive natural phenomena on Earth.

Watch at: www.youtube.com

Read the full article at: arxiv.org

BioSystems Volume 262, April 2026, 105733

Our universe appears to be fine-tuned for life. But once life emerges, it does not evolve randomly. Evolution has a trajectory. Both evolvability and cooperative integration increase as evolution proceeds. Until now, this trajectory has largely been driven blindly by gene-based natural selection. But humans are developing cognitive capacities that are far superior than natural selection at adapting and evolving humanity. These capacities will enable humanity to use an understanding of evolution’s future trajectory to guide its own evolution, avoiding the destructive selection that will otherwise reinforce the trajectory. Humans who help realize this potential will be fulfilling vital evolutionary roles that are meaningful and purposeful in a much larger scheme of things. The paper considers whether these roles remain meaningful when considered in the wider context of possible origins of the universe. But this analysis is faced with a potentially infinite number of origin hypotheses (including innumerable ‘God hypotheses’), which are not falsified by current knowledge. The paper addresses this challenge using methods that enable rational decision-making despite radical uncertainty. Broadly, this approach reinforces the conclusions reached by consideration of the evolutionary trajectory within the universe, and opens some new possibilities. Finally, the paper demonstrates that extending this analysis also largely overcomes Hume’s critique of induction, placing scientific methodologies on a firmer footing. It achieves this by recognising that a universe which exhibits a trajectory towards increasing evolvability must contain discoverable regularities that provide adaptive advantages for evolvability.

Read the full article at: www.sciencedirect.com

Application deadline: February 28th

Voting: March 1-15th

More at: yrcss.cssociety.org

Kleber Andrade Oliveira , Henrique Ferraz de Arruda , Yamir Moreno 

PNAS Nexus, Volume 5, Issue 1, January 2026, pgaf402

We investigate how information-spreading mechanisms affect opinion dynamics and vice versa via an agent-based simulation on adaptive social networks. First, we characterize the impact of reposting on user behavior with limited memory, a feature that introduces novel system states. Then, we build an experiment mimicking information-limiting environments seen on social media platforms and study how the model parameters can determine the configuration of opinions. In this scenario, different posting behaviors may sustain polarization or reverse it. We further show the adaptability of the model by calibrating it to reproduce the statistical organization of information cascades as seen empirically in a microblogging social media platform. Our model combines mechanisms for platform content recommendation, connection rewiring, and limited-attention user behavior, paving the way for a robust understanding of echo chambers as a specialized phenomenon of opinion polarization.

Read the full article at: academic.oup.com

Read the full article at: arxiv.org

Read the full article at: arxiv.org

Mathematics 2026, 14(3), 535

According to the Church–Turing thesis, the limit of what is computable is bounded by Turing machines. Following from this, given that general computable functions formally describe the notion of recursive mechanisms, it is sometimes argued that every organismic process that specifies consistent cognitive responses should be both limited to Turing machine capabilities and amenable to formalization. There is, however, a deep intuitive conviction permeating contemporary cognitive science, according to which mental phenomena, such as consciousness and agency, cannot be explained by resorting to this kind of framework. In spite of some exceptions, the overall tacit assumption is that whatever the mind is, it exceeds the reach of what is described by notions of computability. This issue, namely the nature of the relation between cognition and computation, becomes particularly pertinent and increasingly more relevant as a possible source of better understanding the inner workings of the mind, as well as the limits of artificial implementations thereof. Moreover, although it is often overlooked or omitted so as to simplify our models, it will probably define, or so we argue, the direction of future research on artificial life, cognitive science, artificial intelligence, and related fields.

Read the full article at: www.mdpi.com

Call for Abstracts
The International Conference on Computational Social Science (IC2S2) is the premier conference bringing together researchers from different disciplines interested in using computational and data-intensive methods to address relevant societal problems. IC2S2 hosts academics and practitioners in computational science, social science, complexity, and network science, and provides a platform for new research in the field of computational social science.

More at: ic2s2-2026.org

As part of the European Social Simulation Association‘s life-long learning strategy, the ESSA Summer School 2026 will take place from Monday 17 to Friday 21 August 2026 at the James Hutton Institute, Aberdeen. Led by Gary Polhill, this one-week intensive course offers an introduction to agent-based modelling (ABM), connecting theories of complex systems with practical model design, programming, and experimentation in NetLogo.

Participants will learn how agent-based models can represent heterogeneous actors, dynamic environments, and emergent socio-ecological patterns. The course combines conceptual theory, coding exercises, and group projects to help participants understand the purpose, design, and implementation of ABMs for socio-environmental systems.

Key themes include:

• Complex systems thinking and agent-based theory
• Translating conceptual systems into computational models
• Programming ABMs in NetLogo and developing clear model structures
• Setting up experiments, analysing results, and communicating model findings

The summer school is designed for PhD students, researchers, and practitioners interested in modelling socio-ecological systems, environmental policy, behavioural dynamics, and other complex adaptive systems.

More at: large-scale-modelling.hutton.ac.uk

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.

Read the full article at: ntml.lakecomoschool.org

The Max Planck Institute for Demographic Research (MPIDR) in Rostock is one of the leading demographic research centers in the world. It’s part of the Max Planck Society, the internationally renowned German research society.

More at: www.demogr.mpg.de

June 21–26, 2026 | Northeastern University London
Open to Master’s students, PhD students, and postdocs
Application deadline: February 28th, 2026

Registration fee: €710 (includes 5 nights accommodation, workshop facilities, coffee breaks, lunches, invited lectures, and social events).

More information and applications: https://complexity72h.com 

Anil Seth

Why consciousness is more likely a property of life than of computation and why creating conscious, or even conscious-seeming AI, is a bad idea.

Read the full article at: www.noemamag.com

Zihan Yu, Jingtao Ding & Yong Li 
Nature Computational Science (2025)

Network dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains, mathematical models exist in only a few areas with clear underlying principles. Here we show that a neural symbolic regression approach can bridge this gap by automatically deriving formulas from data. Our method reduces searches on high-dimensional networks to equivalent one-dimensional systems and uses pretrained neural networks to guide accurate formula discovery. Applied to ten benchmark systems, it recovers the correct forms and parameters of underlying dynamics. In two empirical natural systems, it corrects existing models of gene regulation and microbial communities, reducing prediction error by 59.98% and 55.94%, respectively. In epidemic transmission across human mobility networks of various scales, it discovers dynamics that exhibit the same power-law distribution of node correlations across scales and reveal country-level differences in intervention effects. These results demonstrate that machine-driven discovery of network dynamics can enhance understandings of complex systems and advance the development of complexity science.

Read the full article at: www.nature.com

Gourab Kumar Sar, Kevin O’Keeffe, Joao U.F. Lizárraga, Marcus A.M. de Aguiar, Christian Bettstetter, Dibakar Ghosh

Physics Reports Volume 1167, 14 April 2026, Pages 1-52

Swarmalators, entities that combine the properties of swarming particles with synchronized oscillations, represent a novel and growing area of research in the study of collective behavior. This review provides a comprehensive overview of the current state of swarmalator research, focusing on the interplay between spatial organization and temporal synchronization. After a brief introduction to synchronization and swarming as separate phenomena, we discuss the various mathematical models that have been developed to describe swarmalator systems, highlighting the key parameters that govern their dynamics. The review also discusses the emergence of complex patterns, such as clustering, phase waves, and synchronized states, and how these patterns are influenced by factors such as interaction range, coupling strength, and frequency distribution. Recently, some minimal models were proposed that are solvable and mimic real-world phenomena. The effect of predators in the swarmalator dynamics is also discussed. Finally, we explore potential applications in fields ranging from robotics to biological systems, where understanding the dual nature of swarming and synchronization could lead to innovative solutions. By synthesizing recent advances and identifying open challenges, this review aims to provide a foundation for future research in this interdisciplinary field.

Read the full article at: www.sciencedirect.com

Complexities 2026, 2(1), 3

We detail a procedure to transform the current empirical stage in the study of complex systems into a predictive phenomenological one. Our approach starts with the statistical-mechanical Landau-Ginzburg equation for dissipative processes, such as kinetics of phase change. Then, it imposes discrete time evolution to explicit back feeding, and adopts a power-law driving force to incorporate the onset of chaos, or, alternatively, criticality, the guiding principles of complexity. One obtains, in closed analytical form, a nonlinear renormalization-group (RG) fixed-point map descriptive of any of the three known (one-dimensional) transitions to or out of chaos. Furthermore, its Lyapunov function is shown to be the thermodynamic potential in q-statistics, because the regular or multifractal attractors at the transitions to chaos impose a severe impediment to access the system’s built-in configurations, leaving only a subset of vanishing measure available. To test the pertinence of our approach, we refer to the following complex systems issues: (i) Basic questions, such as demonstration of paradigms equivalence, illustration of self-organization, thermodynamic viewpoint of diversity, biological or other. (ii) Derivation of empirical laws, e.g., ranked data distributions (Zipf law), biological regularities (Kleiber law), river and cosmological structures (Hack law). (iii) Complex systems methods, for example, evolutionary game theory, self-similar networks, central-limit theorem questions. (iv) Condensed-matter physics complex problems (and their analogs in other disciplines), like, critical fluctuations (catastrophes), glass formation (traffic jams), localization transition (foraging, collective motion).

Read the full article at: www.mdpi.com

Sara Venturini, Satyaki Sikdar, Martina Mazzarello, Francesco Rinaldi, Francesco Tudisco, Paolo Santi, Santo Fortunato, Carlo Ratti
The COVID-19 pandemic shifted academic collaboration from in-person to remote interactions. This study explores, for the first time, the effects on scientific collaborations and impact of such a shift, comparing research output before, during, and after the pandemic. Using large-scale bibliometric data, we track the evolution of collaboration networks and the resulting impact of research over time. Our findings are twofold: first, the geographic distribution of collaborations significantly shifted, with a notable increase in cross-border partnerships after 2020, indicating a reduction in the constraints of geographic proximity. Second, despite the expansion of collaboration networks, there was a concerning decline in citation impact, suggesting that the absence of spontaneous in-person interactions-which traditionally foster deep discussions and idea exchange-negatively affected research quality. As hybrid work models in academia gain traction, this study highlights the need for universities and research organizations to carefully consider the balance between remote and in-person engagement.

Read the full article at: arxiv.org

DANIEL THILO SCHROEDER, MEEYOUNG CHA, ANDREA BARONCHELLI, NICK BOSTROM, NICHOLAS A. CHRISTAKIS, DAVID GARCIA, AMIT GOLDENBERG, YARA KYRYCHENKO, KEVIN LEYTON-BROWN, NINA LUTZ, GARY MARCUS, FILIPPO MENCZER, GORDON PENNYCOOK, DAVID G. RAND, MARIA RESSA, FRANK SCHWEITZER, DAWN SONG, CHRISTOPHER SUMMERFIELD, AUDREY TANG, JAY J. VAN BAVEL, SANDER VAN DER LINDEN, AND JONAS R. KUNST

SCIENCE 22 Jan 2026 Vol 391, Issue 6783 pp. 354-357

Read the full article at: www.science.org