Jasper J. van Beers, Marten Scheffer, Prashant Solanki, Ingrid A. van de Leemput, Egbert H. van Nes, Coen C. de Visser Maintaining stability in feedback systems, from aircraft and autonomous robots to biological and physiological systems, relies on monitoring their behavior and continuously adjusting their inputs. Incremental damage can make such control fragile. This tends to go unnoticed until a small perturbation induces instability (i.e. loss of control). Traditional methods in the field of engineering rely on accurate system models to compute a safe set of operating instructions, which become invalid when the, possibly damaged, system diverges from its model. Here we demonstrate that the approach of such a feedback system towards instability can nonetheless be monitored through dynamical indicators of resilience. This holistic system safety monitor does not rely on a system model and is based on the generic phenomenon of critical slowing down, shown to occur in the climate, biology and other complex nonlinear systems approaching criticality. Our findings for engineered devices opens up a wide range of applications involving real-time early warning systems as well as an empirical guidance of resilient system design exploration, or "tinkering". While we demonstrate the validity using drones, the generic nature of the underlying principles suggest that these indicators could apply across a wider class of controlled systems including reactors, aircraft, and self-driving cars. Read the full article at: arxiv.org

Aparimit Kasliwal, Abdullah Alhadlaq, Ariel Salgado, Auroop R. Ganguly, Marta C. González Computer-Aided Civil and Infrastructure Engineering Volume40, Issue31, 29 December 2025, Pages 6223-6241 Modeling spreading dynamics on spatial networks is crucial to addressing challenges related to traffic congestion, epidemic outbreaks, efficient information dissemination, and technology adoption. Existing approaches include domain-specific agent-based simulations, which offer detailed dynamics but often involve extensive parameterization, and simplified differential equation models, which provide analytical tractability but may abstract away spatial heterogeneity in propagation patterns. As a step toward addressing this trade-off, this work presents a hierarchical multiscale framework that approximates spreading dynamics across different spatial scales under certain simplifying assumptions. Applied to the Susceptible-Infected-Recovered (SIR) model, the approach ensures consistency in dynamics across scales through multiscale regularization, linking parameters at finer scales to those obtained at coarser scales. This approach constrains the parameter search space, and enables faster convergence of the model fitting process compared to the non-regularized model. Using hierarchical modeling, the spatial dependencies critical for understanding system-level behavior are captured while mitigating the computational challenges posed by parameter proliferation at finer scales. Considering traffic congestion and COVID-19 spread as case studies, the calibrated fine-scale model is employed to analyze the effects of perturbations and to identify critical regions and connections that disproportionately influence system dynamics. This facilitates targeted intervention strategies and provides a tool for studying and managing spreading processes in spatially distributed sociotechnical systems. Read the full article at: onlinelibrary.wiley.com

Leroy Cronin, Sara I. Walker Assembly theory (AT) introduces a concept of causation as a material property, constitutive of a metrology of evolution and selection. The physical scale for causation is quantified with the assembly index, defined as the minimum number of steps necessary for a distinguishable object to exist, where steps are assembled recursively. Observing countable copies of high assembly index objects indicates that a mechanism to produce them is persistent, such that the object's environment builds a memory that traps causation within a contingent chain. Copy number and assembly index underlie the standardized metrology for detecting causation (assembly index), and evidence of contingency (copy number). Together, these allow the precise definition of a selective threshold in assembly space, understood as the set of all causal possibilities. This threshold demarcates life (and its derivative agential, intelligent and technological forms) as structures with persistent copies beyond the threshold. In introducing a fundamental concept of material causation to explain and measure life, AT represents a departure from prior theories of causation, such as interventional ones, which have so far proven incompatible with fundamental physics. We discuss how AT's concept of causation provides the foundation for a theory of physics where novelty, contingency and the potential for open-endedness are fundamental, and determinism is emergent along assembled lineages. Read the full article at: arxiv.org

In this timely book, Juval Portugali ties together ancient archaeology with the present to explore the city as a phenomenon that has been rapidly increasing in power and importance since its earliest emergence. He links the walled cities of ancient civilization with modern-day borderless cities, while providing connections between modern nationalism and postmodern urbanism. More at: www.e-elgar.com

Stefano Caselli, Marta Zava The study examines the structure, functioning, and strategic implications of financial ecosystems across four European countries-France, Sweden, the United Kingdom, and Italy-to identify institutional best practices relevant to the ongoing transformation of Italy's financial system. Building on a comparative analysis of legislation and regulation, taxation, investor bases, and financial intermediation, the report highlights how distinct historical and institutional trajectories have shaped divergent models: the French dirigiste system anchored by powerful state-backed institutions and deep asset management pools; the Swedish social-democratic ecosystem driven by broad household equity participation, taxefficient savings vehicles, and equity-oriented pension funds; and the British liberal model, characterized by deep capital markets, strong institutional investor engagement, and globally competitive listing infrastructure. In contrast, Italy remains predominantly bank-centric, with fragmented institutional investment, limited retail equity participation, underdeveloped public markets, and a structural reliance on domestic banking channels for corporate finance. Read the full article at: papers.ssrn.com

The scientific understanding of energy, matter, and spacetime has advanced rapidly, whereas the study of information—its properties, behavior, and dynamics—remains underdeveloped. Despite the complexity of knowledge and information, our conceptual and empirical grasp of its evolution lags significantly behind. Progress in disciplines such as artificial intelligence, genomics, cognitive science, cyber governance, global ecology, and quantum mechanics depends critically on a more rigorous understanding of information dynamics. Absent such insight, humanity risks succumbing to entropic forces that threaten systemic stability and long-term survival. In this book, Klaus Jaffe addresses the limitations of prior treatments of infodynamics, many of which have been incomplete, imprecise, or conceptually flawed. It offers an interdisciplinary investigation into the relationship between information and energy, drawing on theoretical and empirical contributions from economics, biology, and physics. By challenging conventional paradigms, the book constructs a conceptual framework that bridges disparate scientific domains and societal processes. The resulting synthesis opens new avenues for empirical inquiry and policy-relevant research, with implications for both academic scholarship and public discourse. Inviting readers to explore the evolving frontier of information science, the book highlights the role of information and its impact on both natural and social systems. More at: link.springer.com

Manlio De Domenico The possibility that evolutionary forces -- together with a few fundamental factors such as thermodynamic constraints, specific computational features enabling information processing, and ecological processes -- might constrain the logic of living systems is tantalizing. However, it is often overlooked that any practical implementation of such a logic requires complementary circuitry that, in biological systems, happens through complex networks of genetic regulation, metabolic reactions, cellular signalling, communication, social and eusocial non-trivial organization. Here, we review and discuss how circuitries are not merely passive structures, but active agents of change that, by means of hierarchical and modular organization, are able to enhance and catalyze the evolution of evolvability. By analyzing the role of non-trivial topologies in major evolutionary transitions under the lens of statistical physics and nonlinear dynamics, we show that biological innovations are strictly related to circuitry and its deviation from trivial structures and (thermo)dynamic equilibria. We argue that sparse heterogeneous networks such as hierarchical modular, which are ubiquitously observed in nature, are favored in terms of the trade-off between energetic costs for redundancy, error-correction and mantainance. We identify three main features -- namely, interconnectivity, plasticity and interdependency -- pointing towards a unifying framework for modeling the phenomenology, discussing them in terms of dynamical systems theory, non-equilibrium thermodynamics and evolutionary dynamics. Within this unified picture, we also show that “slow” evolutionary dynamics is an emergent phenomenon governed by the replicator-mutator equation as the direct consequence of a constrained variational nonequilibrium process. Overall, this work highlights how dynamical systems theory and nonequilibrium thermodynamics provide powerful analytical techniques to study biological complexity. Read the full article at: iopscience.iop.org

Federico Battiston, Valerio Capraro, Fariba Karimi, Sune Lehmann, Andrea Bamberg Migliano, Onkar Sadekar, Angel Sánchez & Matjaž Perc Nature Human Behaviour volume 9, pages 2441–2457 (2025 Traditional social network models focus on pairwise interactions, overlooking the complexity of group-level dynamics that shape collective human behaviour. Here we outline how the framework of higher-order social networks—using mathematical representations beyond simple graphs—can more accurately represent interactions involving multiple individuals. Drawing from empirical data including scientific collaborations and contact networks, we demonstrate how higher-order structures reveal mechanisms of group formation, social contagion, cooperation and moral behaviour that are invisible in dyadic models. By moving beyond dyads, this approach offers a transformative lens for understanding the relational architecture of human societies, opening new directions for behavioural experiments, cultural dynamics, team science and group behaviour as well as new cross-disciplinary research. Read the full article at: www.nature.com

David H Wolpert Journal of Physics: Complexity, Volume 6, Number 4 The simulation hypothesis has recently excited renewed interest in the physics and philosophy communities. However, the hypothesis specifically concerns computers that simulate physical universes. So to formally investigate the hypothesis, we need to understand it in terms of computer science (CS) theory. In addition we need a formal way to couple CS theory with physics. Here I couple those fields by using the physical Church–Turing thesis. This allow me to exploit Kleene’s second recursion, to prove that not only is it possible for us to be a simulation being run on a computer, but that we might be in a simulation that is being run on a computer – by us. In such a ‘self-simulation’, there would be two identical instances of us, both equally ‘real’. I then use Rice’s theorem to derive impossibility results concerning simulation and self-simulation; derive implications for (self-)simulation if we are being simulated in a program using fully homomorphic encryption; and briefly investigate the graphical structure of universes simulating other universes which contain computers running their own simulations. I end by describing some of the possible avenues for future research. While motivated in terms of the simulation hypothesis, the results in this paper are direct consequences of the Church–Turing thesis. So they apply far more broadly than the simulation hypothesis. Read the full article at: iopscience.iop.org

Stuart Bartlett; Michael L. Wong Interface Focus (2025) 15 (6): 20250019 . Learning—in addition to thermodynamic dissipation, autocatalysis and homeostasis—has been hypothesized to be a key pillar of all living systems. Here, we examine the myriad ways in which organisms on Earth learn over various time and length scales—from Darwinian evolution to protein computation to the scientific method—in order to draw abstractions about the process of learning in general. Be it in life on Earth or lyfe elsewhere in the universe, we propose that learning can be characterized by a combination of mechanisms that favour functional fitness and those that favour novelty search. We also propose that feedbacks related to learning and dissipation, learning and environmental complexity and learning and self-modelling may be general features that guide how the information-processing and predictive abilities of learning systems evolve with time, perhaps even at the scale of planetary biospheres. Read the full article at: royalsocietypublishing.org

Sergi Valverde, Blai Vidiella, Salva Duran-Nebreda This chapter investigates the evolutionary ecology of software, focusing on the symbiotic relationship between software and innovation. An interplay between constraints, tinkering, and frequency-dependent selection drives the complex evolutionary trajectories of these socio-technological systems. Our approach integrates agent-based modeling and case studies, drawing on complex network analysis and evolutionary theory to explore how software evolves under the competing forces of novelty generation and imitation. By examining the evolution of programming languages and their impact on developer practices, we illustrate how technological artifacts co-evolve with and shape societal norms, cultural dynamics, and human interactions. This ecological perspective also informs our analysis of the emerging role of AI-driven development tools in software evolution. While large language models (LLMs) provide unprecedented access to information, their widespread adoption introduces new evolutionary pressures that may contribute to cultural stagnation, much like the decline of diversity in past software ecosystems. Understanding the evolutionary pressures introduced by AI-mediated software production is critical for anticipating broader patterns of cultural change, technological adaptation, and the future of software innovation. Read the full article at: arxiv.org

Haoling Zhang, Chao-Han Huck Yang, Hector Zenil, Pin-Yu Chen, Yue Shen, Narsis A. Kiani & Jesper N. Tegnér Nature Communications , Article number: (2025) As the scale of artificial neural networks continues to expand to tackle increasingly complex tasks or improve the prediction accuracy of specific tasks, the challenges associated with computational demand, hyper-parameter tuning, model interpretability, and deployment costs intensify. Addressing these challenges requires a deeper understanding of how network structures influence network performance. Here, we analyse 882,000 motifs to reveal the functional roles of incoherent and coherent three-node motifs in shaping overall network performance. Our findings reveal that incoherent loops exhibit superior representational capacity and numerical stability, whereas coherent loops show a distinct preference for high-gradient regions within the output landscape. By avoiding such gradient pursuit, incoherent loops sustain more stable adaptation and consequently greater robustness. This mechanism is evident in 97,240 fixed-network training experiments, where coherent-loop networks consistently prioritized high-gradient regions during learning, and is further supported by noise-resilience analyses – from classical reinforcement learning tasks to biological, chemical, and medical applications – which demonstrate that incoherent-loop networks maintain stronger resistance to training noise and environmental perturbations. This work shows the functional impact of structural motif differences on the performance of artificial neural networks, offering foundational insights for designing more resilient and accurate networks. Read the full article at: www.nature.com

Richard A. Watson,  Michael Levin,  Tim Lewens` Interface Focus (2025) 15 (6): 20250025 . It is conventionally assumed that all evolutionary adaptation is produced, and could only possibly be produced, by natural selection. Natural induction is a different mechanism of adaptation. It occurs in dynamical systems described by a network of interactions, where connections give way slightly under stress and the system is subject to occasional perturbations. This differential adjustment of connections causes reorganization of the system’s internal structure in a manner equivalent to associative learning familiar in neural networks. This is sufficient for storage and recall of multiple patterns, learning with generalization and solving difficult constraint problems (without any natural selection involved). Various biological systems (from gene-regulation networks to metabolic networks to ecosystems) meet these basic conditions and therefore have potential to exhibit adaptation by natural induction. Here (and in a follow-on paper), we consider various ways that natural induction and natural selection might interact in biological evolution. For example, in some cases, natural selection may act not as a source of adaptations but as a memory of adaptations discovered by natural induction. We conclude that evolution by natural induction is a viable process that expands our understanding of evolutionary adaptation. Read the full article at: royalsocietypublishing.org

Amahury J. López-Díaz, Pedro Juan Rivera Torres, Gerardo L. Febres, Carlos Gershenson Discrete dynamical models underpin systems biology, but we still lack substrate-agnostic diagnostics for when such models can sustain genuinely open-ended evolution (OEE): the continual production of novel phenotypes rather than eventual settling. We introduce a simple, model-independent metric, {\Omega}, that quantifies OEE as the residence-time-weighted contribution of each attractor's cycle length across the sequence of attractors realized over time. {\Omega} is zero for single-attractor dynamics and grows with the number and persistence of distinct cyclic phenotypes, separating enduring innovation from transient noise. Using Random Boolean Networks (RBNs) as a unifying testbed, we compare classical Boolean dynamics with biologically motivated non-classical mechanisms (probabilistic context switching, annealed rule mutation, paraconsistent logic, modal necessary/possible gating, and quantum-inspired superposition/entanglement) under homogeneous and heterogeneous updating schemes. Our results support the view that undecidability-adjacent, state-dependent mechanisms -- implemented as contextual switching, conditional necessity/possibility, controlled contradictions, or correlated branching -- are enabling conditions for sustained novelty. At the end of our manuscript we outline a practical extension of {\Omega} to continuous/hybrid state spaces, positioning {\Omega} as a portable benchmark for OEE in discrete biological modeling and a guide for engineering evolvable synthetic circuits. Read the full article at: arxiv.org

NERCCS 2026: The Ninth Northeast Regional Conference on Complex Systems will follow the success of the previous NERCCS conferences to promote the emerging venue of interdisciplinary scholarly exchange for complex systems researchers in the Northeast U.S. region (and beyond) to share their research outcomes through presentations and online publications, network with their peers, and promote interdisciplinary collaboration and the growth of the research community. NERCCS will particularly focus on facilitating the professional growth of early career faculty, postdocs, and students in the region who will likely play a leading role in the field of complex systems science and engineering in the coming years. The 2026 conference will be held primarily in person at the University of Rochester, with an online participation option via Zoom. More at: nerccs2026.github.io

19–23 December 2025 (Hybrid) Network Science Research Lab, IIIT Kottayam, India Workshop on Complex Network Analysis with Applications in Brain Network Science and Complex Systems aims to bring together academicians, researchers, industrial experts, Ph.D. scholars, and postdoctoral fellows to explore recent advancements and foundational concepts in the fields of graph theory and its applications in network analysis. Graph theory, a cornerstone of discrete mathematics, offers a robust framework for modeling and analyzing complex networks across various domains from biological systems and brain connectivity to social, technological, and infrastructural networks.The primary aim of this five-day workshop is to provide a comprehensive introduction to the mathematical foundations and computational techniques in complex network analysis, with a particular emphasis on its applications in brain network science and biomedical data analysis. The program will cover a range of contemporary topics, including the simplicial analysis of fMRI data to study human brain dynamics during functional cognitive tasks, analysis of complex networks and prediction using deep learning models, and exploration of graph algorithms along with their computational complexity. Participants will also gain exposure to advanced methodologies such as the application of complex networks in machine learning, the characterization of resting-state fMRI for brain connectivity analysis, and diffusion MRI analysis for clinical applications. In addition, the workshop will introduce recurrence network analysis, which is used to predict climate changes and other dynamic systems. To complement these theoretical discussions, hands-on sessions will be conducted on complex network analysis using NetworkX, and nonlinear dynamics in recurrence relations. Free registration for Complex Systems Society Members. More at: sites.google.com

Alberto Aleta, Andreia Sofia Teixeira, Guilherme Ferraz de Arruda, Andrea Baronchelli, Alain Barrat, János Kertész, Albert Díaz-Guilera, Oriol Artime, Michele Starnini, Giovanni Petri, Márton Karsai, Siddharth Patwardhan, Alessandro Vespignani, Yamir Moreno, Santo Fortunato 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 standardized 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: arxiv.org

Guest Editors: Thiago B. Murari, Marcelo A. Moret, Hernane B. de B. Pereira, Tarcísio M. Rocha Filho, José F. F. Mendes, Tiziana Di Matteo Inspired by the Conference on Complex Systems 2023 (CCS2023) in Salvador, Brazil, this collection of EPJ B brings together 25 peer-reviewed articles covering a wide range of topics. This collection highlights the interdisciplinary nature of the field, with contributions from physics, biology, economics, linguistics, and artificial intelligence, and serves as a reference for researchers addressing real-world challenges through systems-based thinking. Read the full issue at: epjb.epj.org

https://www.youtube.com/watch?v=wGhRW-pJWIcWhat is life? What is intelligence? What is… complexity? Neil deGrasse Tyson and co-hosts Chuck Nice and Gary O’Reilly learn how complexity science, chaos theory, and emergence could be the key to understanding our place in the universe with David Krakauer, president of the Santa Fe Institute and professor in complex systems. Watch at: www.youtube.com

Mohsen Raoufi, Heiko Hamann & Pawel Romanczuk npj Complexity volume 2, Article number: 28 (2025) Collective estimation is a variant of collective decision-making where agents reach consensus on a continuous quantity through social interactions. Achieving precise consensus is complex due to the co-evolution of opinions and the interaction network. While homophilic networks may facilitate estimation in well-connected systems, disproportionate interactions with like-minded neighbors lead to the emergence of echo chambers and prevent consensus. Our agent-based simulations confirm that, besides limited exposure to attitude-challenging opinions, seeking reaffirming information entrap agents in echo chambers. To overcome this, agents can adopt a stubborn state (Messengers) that carries data and connects clusters by physically transporting their opinion. We propose a generic approach based on a Dichotomous Markov Process, which governs probabilistic switching between behavioral states and generates diverse collective behaviors. We study a continuum between task specialization (no switching), to generalization (slow or rapid switching). Messengers help the collective escape local minima, break echo chambers, and promote consensus. Read the full article at: www.nature.com

Michael Lissack This paper presents a unified theoretical framework that reconciles four apparently disparate approaches: Quantum Bayesianism (QBism), Robert Rosen's theory of Anticipatory Systems, the causal bubbles interpretation of quantum mechanics, and pragmatic constructivism through Hans Vaihinger's philosophy of 'as if.' We demonstrate that these frameworks converge on a fundamental insight: reality emerges from a relational causal structure-the pattern of influences that determine what can affect what-rather than from external observation. The QBist agent exemplifies a Rosen Anticipatory System operating within a causal bubble, wherein the quantum wave function serves as a heuristic fiction-an 'as if' construct-used for anticipatory modeling within the agent's architecture rather than for ontological description. This synthesis resolves longstanding quantum paradoxes, provides a naturalized account of final causality, and extends to encompass human cognition and artificial intelligence as distinct instantiations of the same anticipatory pattern. We argue that physical laws function as normative standards for coherent anticipation that acquire constraining force through selective pressure, and that this relational ontology bridges quantum physics, theoretical biology, epistemology, and cognitive science, dissolving apparent conflicts between these domains into perspectives on a shared structure. Read the full article at: papers.ssrn.com

ALIFE 2025: Ciphers of Life: Proceedings of the Artificial Life Conference 2025 Read the full Proceedings at: direct.mit.edu

Complex systems fascinate because of the way dynamic microscopic interactions give rise to striking, often unexpected macroscopic structures: convection cells in fluids, patterns in ecosystems, networks in societies, and organization in biology. What unites these diverse examples is the deep link between how the agents in systems move and what structure emerges. While diverse approaches have been proposed, in addition, a unifying language may lie in variational principles and optimal control in stochastic and dissipative regimes which can offer a powerful language for understanding this interplay. Action principles are among the most unifying ideas in science: from Lagrangian mechanics to quantum field theory, they describe how nature selects pathways. The stochastic-dissipative extensions of the principle of least action in the form of path integrals, such as by Onsager-Machlup and more recent versions provide a natural framework for describing how agents and processes, obeying fundamental physical laws, select the most probable and efficient pathways under constraints. These pathways not only govern system dynamics but also generate—and are constrained by—emergent structures. Feedback between dynamics and structure thus shapes evolution, with frozen accidents and historical contingencies balanced against tendencies toward action-efficient configurations. If dynamics select the most probable, efficient pathways, then structure itself may be seen as the lasting imprint of such pathways. Can such principles also help explain the emergence of complexity? This Collection aims to gather theoretical, computational, and empirical contributions that advance the use of variational principles to explain and predict structure–dynamics interplay in complex systems. By doing so, we hope to move toward general non-equilibrium thermodynamics capable of grounding complexity science in physics while connecting to diverse domains of application. Contributions are welcome across disciplines, from mathematics and physics to biology, engineering, and social sciences. Themes may include, but are not limited to:Stochastic and dissipative formulations of variational principles.Path integrals and optimal control.Structure formation in non-equilibrium thermodynamics.Agent-based simulations and computational models.Empirical case studies from physical, chemical, biological, or social systems.Comparative perspectives with non-variational approaches.The aim is to advance a physics-grounded framework for understanding how complex structures emerge and persist under dynamic constraints. The objective of this Collection is to foster dialogue among researchers working on different manifestations of the same fundamental questions: How do dynamics give rise to structure, how structure determines dynamics, and how can variational principles provide the key to understanding this process across scales and systems? Can variational pathways explain the emergence of complex structures from dynamics across nature and society? More at: www.nature.com

9-12 June, 2026 Nanyang Technological University, Singapore The number of scientists working on networks and complex systems in the Asia-Pacific region is increasing, but high-level conferences in these areas remain limited to NetSci, NetSciX, the International Conference on Computational Science (ICCS), and Conference on Complex Systems (CCS). Asia-Pacific scientists, especially postdocs and PhD students in these areas therefore have limited opportunities to attend these conferences. This leads to a lack of exposure of Asia-Pacific scientists to good work done elsewhere in the world, and of scientists from other parts of the world to good work done in Asia-Pacific, and seriously hampers the academic growths of Asia-Pacific scientists. Recently, we have been encouraged by the strong turnout of Indian scientists at all levels at the NetSciX 2025 conference in Indore, India. We can sense that the younger scientists treasured this opportunity to share their work. Unfortunately, it is impossible to bring these flagship conferences to Asia-Pacific every year. At best, we can host one such conference in Asia once every three to four years. This prompted us to start an Asia-Pacific Conference on Networks and Complex Systems beginning next year (2-5 Jun 2026 in Singapore) to cater to these unmet demands. With this conference, we would always have a platform to present our recent work and meet up with colleagues. While we will always invite the most exciting speakers from all around the world, our goal is to include more invited speakers from Asia-Pacific. We also aim to give as many participants as possible (including PhD students) a chance to give oral presentations. We will rotate this conference series around Asia-Pacific, where most venues are more affordable compared to Europe or North America. Hopefully, more Asian scientists would be able to attend, even if they have limited funding. This is especially true of PhD students and postdoctoral researchers, many of whom can only attend the local editions of the conference. Finally, to ensure that the conference is relevant and high-level, we will be advised by an International Advisory Committee of eminent scientists in the fields of networks and complex systems. Additionally, to ensure that good organizational practices are shared after they are developed, besides local organizers the organizing team for a given year will also comprise core members from the organizing team for the next year. More at: apcncs2026.github.io

Gülşah Akçakır, John C. Lang & P. J. Lamberson npj Complexity volume 2, Article number: 35 (2025) Collaboration enables groups to solve problems beyond the reach of their individual members in contexts ranging from research and development to high-energy physics. While communication networks play a pivotal role in group success, there is a longstanding debate on the optimal network topology for solving complex problems. Prior research reaches contradictory conclusions–some studies suggest networks that slow information transmission help maintain diversity, leading groups to explore more of the problem space and find better solutions in the long run, while others argue that networks that maximize communication efficiency allow groups to exploit known solutions, boosting overall performance. Many existing models assume that individuals use their network connections only to copy better-performing group members, but we show that such groups often perform worse than if individuals worked independently. Instead, our model introduces a crucial distinction: in addition to copying, individuals can actively collaborate, leveraging diverse perspectives to uncover solutions that would otherwise remain inaccessible. Our findings reveal that the optimal network structure depends on the balance between copying and collaboration. When copying dominates, inefficient, exploration-focused networks lead to better outcomes. However, when individuals primarily collaborate, highly connected, efficient networks win out. We also show how groups can reap the benefits of both strategies by employing a collaborate first-copy later heuristic in highly connected networks. The results offer new insights into how organizations should be structured to maximize problem-solving performance across different contexts. Read the full article at: www.nature.com