Collective Intelligence

This study simulates the dynamics of a collection of clonal agents responding to chemical gradients (chemotaxis) to demonstrate the evolution of individual variation. To build our multi-agent simulation, we first optimized single agents that rely on a neural network to perform chemotaxis. We then constructed multi-agent simulations using clones of these evolved individuals. We find that mutual interactions lead to the emergence of behavioral variation. We also find population-level performance degradation during later evolutionary stages, despite maintained high individual performance and simplified neural architectures. This decline occurred because agents developed reduced sensory-motor coupling. This latter finding demonstrates that incentives for individual variation worked against the collective interest.

Read the full article at: journals.sagepub.com

The candidate will work with PI David Wolpert on a project investigating how the network coupling the components of the distributed computer controls the tradeoff among the thermodynamic cost of running the computer, the computer’s speed, its robustness against component error, and the precise computation it performs. A particular focus will be to see how the hierarchical and / or modular structure of the network controls the tradeoff among these aspects of distributed computers.

Apply at: santafeinstitute.teamtailor.com

Volume 16 Issue 3 | Interface Focus | The Royal Society

Data-driven modelling in the living system has increasing significance with the abundance of complex data of different modalities. Data are being collected at different scales, from molecular to genetic, cellular, organ, organism and vital signs, to electronic health records. In addition, we produce individual health data, sleep and mobility data collected with wearable devices, as well as data collected from social media, professional networks, workplace and the environment in general. Modelling these data is now possible with the advances of several methods and technologies, all part of the artificial intelligence framework. Modern data analysis is a powerful lens with which we can zoom in and out of the living system, similar to what we can observe with a microscope. This theme issue presents data-driven models which reflect several different angles and lenses to zoom in and out of the human body, to observe and analyse the role and functions of its genes, cells, organs and the interactions between them, as well as the role of the human in the society and environment.

Read the full issue at: royalsocietypublishing.org

Hash Chemistry is a family of minimalistic evolutionary models in which a deterministic hash function assigns a scalar score to entities of arbitrary size, opening a combinatorially vast possibility space (a “cardinality leap”). Since its introduction, the idea has been realized in several settings, from the original spatial formulation to a fast non-spatial variant and then to structural cellular models. Here we review the Hash Chemistry family as a coherent modeling framework and use it to explore how minimal systems can demonstrate the mechanisms behind multiscale open-ended evolutionary dynamics. The most recent model, Structural Cellular Hash Chemistry (SCHC), successfully demonstrated multiscale ecological interaction/adaptation and complexity growth of replicators in a computationally efficient manner. In this study, we first extend SCHC to incorporate spatial locality and dyadicity of competitive interactions among replicating structures. We show this extension substantially enhances SCHC’s evolutionary dynamics. Furthermore, we explore SCHC in a significantly larger spatial domain using a GPU-accelerated implementation. We show that the size of the space acts as a control parameter for a stochastic, nucleation-like transition between a compact-replicator regime and a runaway size-dominance regime, and we separate the responsible mechanism into a non-spatial, size-biased sampling feedback and a finite-size spatial effect. Altogether, these results illustrate the rich potential of Hash Chemistry as a minimal, mechanistically transparent testbed for studying open-ended evolution across scales.

Read the full article at: arxiv.org

Perspectives on Machine Consciousness asks whether any AIs are conscious today, whether any future ones could be conscious, how we could know, and what implications machine consciousness would have for us and for them.

As AI improves rapidly in performance and capability, these questions are becoming increasingly important. We do not fully understand how AIs work, and even some of the leading LLM developers say they cannot be sure that today’s models are not sentient, though many people are forming relationships with them, sometimes intimate ones. The book explores consciousness alongside our interactions with AI, including the critical need to avoid committing mind crime by causing artificial minds to suffer, as well as considering that if and when superintelligence arrives, its enormous effect on humanity may be significantly determined by whether or not it is conscious. The authors show that machines becoming conscious means we may learn a great deal about our own consciousness – arguably the most important, and yet most mysterious, thing about us.

This book is required reading for anybody developing advanced AI, working in AI safety, responsible for developing AI policies at an organizational or national level, and indeed anybody concerned with the long-term future of humanity.

More at: www.taylorfrancis.com

Swarmalator systems intertwine two forms of collective behavior, swarming and synchronization, leading to the emergence of specific space-time patterns. Scaling the model to real-world phenomena and technical applications is problematic due to the assumption of global coupling among all swarmalators, which is impractical under physical constraints on interaction range. Conversely, purely local coupling was shown to be infeasible. To address this gap, we introduce and evaluate the concept of multihop coupling for swarmalators, which preserves the locality of physical interactions but propagates state information throughout the network via hop-limited and probabilistic flooding. It is demonstrated that convergence to the original emergent patterns can be achieved in a reliable and fast manner while keeping overhead low. A practical guideline for selecting the range, hop limit, and forwarding probability is provided. The range required for convergence can be approximated by the connectivity threshold of random geometric graphs.

Read the full article at: journals.aps.org

PNAS August 18, 2026 123 (34) e2531697123

Large language models (LLMs) are increasingly deployed in large numbers, and their interactions make collective behavior harder to anticipate than that of a single model. While most studies compare one model with a collective of fixed size, we ask a key yet overlooked question: What is the role of group size? We show that interaction among LLMs can magnify individual biases, generate new ones, or even overturn individual preferences, and that, crucially, these effects scale in unexpected, nonlinear ways with group size. Our results demonstrate that more is different for LLM populations: The number of interacting agents is a key driver of the dynamics, with implications for the design and governance of multi-agent AI systems.

Read the full article at: www.pnas.org

Trends in Ecology and Evolution

Collective behaviour is widespread in the animal kingdom and can enhance individual
fitness. Yet not all collective behaviours are adaptations. Instead, some may be nonadaptive
or ‘evolutionary spandrels’—traits that originated as by-products in the sense proposed
by Stephen Jay Gould and Richard Lewontin. Here, we argue that self-organising processes
provide a route through which evolutionary spandrels in collective animal behaviour
can occur, and we provide three examples: spatial organisation in primate groups,
division of labour in ants, and insect chorusing. We then consider how such outcomes
may be co-opted into adaptive roles through exaptation and conclude by outlining the
challenges associated with testing adaptive and nonadaptive hypotheses in collective
behaviour research using individual-based studies, phylogenetic comparative analyses,
and agent-based models.

Read the full article at: www.cell.com

Swarm intelligence is a nature-inspired complex system that draws from the behaviours of social creatures such as ants and birds. This system functions through simple behavioural rules enacted by autonomous, intelligent agents. Existing literature indicates that swarm intelligence possesses a wide range of principles and characteristics derived from the theories of Biomimicry, complex adaptive systems, and parametric and generative design. While the previous studies have intensively addressed the computational aspects of intelligence, a comprehensive conceptual framework is essential for analysing complex urban forms and structures. Therefore, this research develops and applies a conceptual model of swarm intelligence by examining several projects across the following dimensions: growth strategies, mechanisms, and logic; primary and final characteristics; and the types and classifications of the system’s agents. The research emphasises the integration of theoretical and practical aspects of swarm intelligence to inform urban growth policies and promote more sustainable urban forms and structures.

Read the full article at: iqjap.uotechnology.edu.iq

Seventy years of artificial intelligence are usually told as a long preamble followed by a revolution beginning around 2012. We organize the period differently, around a question the field has answered differently at different times: what kind of thing is intelligence, such that a machine could have it? Read that way, the human contribution does not withdraw as systems learn more; it relocates, and mostly to places our instruments do not record. Whether the recent acceleration is a change in kind or a change in budget is, we suspect, the more interesting question, and not one that benchmark curves can settle.

Read the full article at: www.preprints.org

BioSystems
Volume 261, March 2026, 105711

We study the development of model biotic communities in which species play the role of environment for each other. Each experiment starts with the appearance of a single species in an abiotic environment. The properties of this initial species (together with the size of the abiotic environment) are the independent parameters of the experiment. In the following phase of macroevolutionary “unwrapping” each existing species can change its abundance (according to its current fitness) and give rise to new species (as a result of mutation). During this process, the destiny of the species becomes increasingly determined by the influence of other species rather than by the abiotic environment. With the mechanics described, artificial biotic communities experience adaptive radiation from single species to complex networks that coevolve in adaptive landscapes of their own making.
Using a number of metrics, we track the evolution of biotic communities in the hope of discovering interesting properties and patterns. We have tried to provide plausible explanations for the experiment results wherever possible. However, the main purpose of this work is not to answer questions, but rather to raise new ones, to provoke thoughts and analogies among readers with different backgrounds.

Read the full article at: www.sciencedirect.com

Read the full article at: www.preprints.org

The Santa Fe Institute Complexity Postdoctoral Fellowships, comprising the Omidyar Fellowships, are unique among postdoctoral appointments. The Fellowships offer early-career scholars the opportunity to undertake their own independent research within a collaborative research community that nurtures creative, transdisciplinary thought in pursuit of key insights about the complex systems that matter most for science and society. The Institute rejects compartmentalized thought common in academia. Instead, SFI scientists transcend boundaries between fields, freely synthesizing ideas spanning many disciplines – from math, physics, computer science and biology to the social sciences and the humanities – in pursuit of creative insights that advance our scientific frontiers.

Read the full article at: apply-sfi.smapply.org

CSMA 2027 aims to create a new international venue that can unite scholars, practitioners and students from diverse fields to address various real-world challenges and opportunities using methodologies of complex systems modeling and analysis. The conference will showcase cutting-edge modeling/analysis methods, interdisciplinary applications, and innovative solutions, fostering collaboration and sparking new ideas. Its 2027 edition will have a particular focus on the applications to education and society. By integrating insights from systems science, mathematics, computer science, engineering, economics, social sciences, psychology, healthcare, education, and many others, we seek to advance understanding and application in these crucial areas. Join us to explore how multidisciplinary approaches can drive improvements in our society!
Organized in Hybrid Mode by CHRIST University, Pune Lavasa, India & Binghamton University, State University of New York, USA

More at: csma.christuniversity.in

CompleNet is an annual international conference that unites researchers and practitioners from diverse scientific disciplines who share a deep interest in understanding the structure, dynamics, and applications of complex networks.

Since its founding in 2009 in Catania, Italy, CompleNet has grown into an established interdisciplinary venue fostering exchange across physics, computer science, biology, social science, economics, and engineering — united by the common language of network science.

More at: complenet.github.io

Bnaya Gross, Joseph Ehlert, Vadim N. Gladyshev, Joseph Loscalzo & Albert-László Barabási 
Nature Aging volume 6, pages1516–1531 (2026)

Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, due to the multifactorial nature of longevity and the interconnectedness of molecular components involved. Here we introduce a network medicine framework to map 2,358 longevity-associated genes onto the human interactome to identify drug-repurposing candidates capable of modulating specific hallmarks of aging. We find that genes associated with each hallmark form a connected subgraph, or hallmark module, allowing us to measure the network proximity of 6,442 compounds to each hallmark. We then introduce a transcription-based metric, pAGE, which evaluates whether drug-induced expression shifts reinforce or counteract known age-related expression changes within each hallmark module. By integrating network proximity and pAGE, we identify drug-repurposing candidates targeting specific hallmarks and provide a falsifiable framework to leverage genomic discoveries for accelerating drug repurposing in longevity. Our findings are interpretable, revealing molecular mechanisms through which drugs modulate hallmarks.

Read the full article at: www.nature.com

Discover Cities

Volume 3, article number 139 (2026)

Public transportation systems are vital for urban mobility, yet their robustness against disruptions remains underexplored, particularly in Latin American cities. This study quantifies the structural and functional robustness of public transport networks in Mexico City, Rio de Janeiro, and Buenos Aires, revealing that Rio de Janeiro exhibits the highest resilience due to its structural redundancy, while also establishing a strong correlation between structural connectivity and trip feasibility across the cities.

Read the full article at: link.springer.com

BioSystems
Volume 266, August 2026, 105845

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

Read the full article at: www.sciencedirect.com

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

Read the full article at: papers.ssrn.com

Organisms. Journal of Biological Sciences

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

Read the full article at: rosa.uniroma1.it

Front. Phys., 08 July 2026

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

Read the full article at: www.frontiersin.org

PNAS 123 (27) e2608847123

From aircraft to power grids, controlled systems form a crucial part of human societies. Nonetheless, catastrophic failures happen. Many of those arise from the accumulation of incremental problems, such as natural wear and tear, that can go unnoticed until it is too late. We demonstrate that generic indicators of resilience can detect growing instabilities in damaged drones. The generic nature of our approach makes it compatible across diverse controlled systems. This not only allows for on-the-fly warning of instability but also facilitates anomaly detection during manufacturing and promotes proactive maintenance. A complementary application is to use our indicators for exploratory design, allowing one to “tinker” with systems through small adjustments and sensing quickly whether those worsen or improve system resilience.

Read the full article at: www.pnas.org

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

Read the full article at: www.mdpi.com

Read the full article at: f1000research.com

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

Read the full article at: www.mdpi.com

On this episode of BeyondPhrenology, I speak with Dr. Gabriele Scheler (Carl Correns Foundation for Mathematical Biology) about the state of AI and neuroscience—past the hype, and closer to their limits.

We begin by revisiting what AI once meant: symbolic systems, logic, early neural networks, and the long-standing divide between learning and reasoning. Against that backdrop, we examine the current moment, where large language models dominate the conversation but remain, in many ways, underwhelming relative to the broader ambitions of artificial intelligence.

The discussion then turns to neuroscience, where despite decades of experimental progress, a central problem remains unresolved: the absence of integrated, functional models of cognition. We explore the consequences of a synapse-centric view, the limits of current theoretical approaches, and why accumulating more data—without perspective—fails to move the field forward. Along the way, we touch on issues that rarely make it into official narratives: the role of funding structures, the drift toward mediocrity, and the persistence of poorly framed questions.

From there, we consider an alternative direction. Dr. Scheler outlines a neuron-centric, function-driven approach to modeling the brain—one that emphasizes modularity, one-shot learning, internal inference, and decision-making as a unifying principle across cognition and emotion. Framed through evolution, this perspective highlights how biological systems bridge scales of structure and function in ways current models largely fail to capture.

The episode closes by reflecting on what this means for the future: not just for AI, but for science itself—how research cultures shift, why fascination alone is not enough, and what it would take to build models that are not just complex, but actually explanatory.
Watch at: www.youtube.com

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

Read the full article at: arxiv.org

Explore academic impact beyond rankings. Rankless offers a fresh perspective on how universities influence each geography and topic, emphasizing diverse forms of impact and providing a richer understanding of academic influence.

Read the full article at: www.rankless.org

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

Read the Special Issue at: royalsocietypublishing.org

An edited volume bringing together 25 of today’s most forward-thinking biologists and philosophers on sign processes and meaning-making in organisms.

Theoretical biology is concerned with providing science with explanatory frameworks within which to fit its findings. The relatively newer field of Biosemiotics is the study of sign processes within life processes.

In the tradition of the field-changing four-volume essay collection Towards a Theoretical Biology issued by developmental biologist Conrad Hal Waddington from 1968 to 1972, this volume brings together many of today’s leading scientists to discuss what they consider to be the most important and pressing problems in our current understandings of the biological world—and how best to advance our understandings of such life processes scientifically.

Contributors: Denis Noble, Terrance Deacon, Scott F. Gilbert, Stuart Kaufmann, Tom Froese, Erik L. Peterson, Richard I Vane-Wright, Charles Wolfe, Raymond Noble, Claus Emmeche, Alexei Sharov, Kalevi Kull, Donald Favareau, Arantza Etxeberria, Anton Markoš, Jana Švorcová, Daniel C. Mayer-Foulkes, Federico Vega, Henrik Nielsen, Karel Kleisner, David Cortés-García, Matt Kalkman, Georgii Karelin, Takashi Ikegami, and Mariana Vitti Rodrigues.

Read the full article at: mitpress.mit.edu

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

Watch at: www.youtube.com

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

Read the full article at: www.science.org

by George Musser

Funding agencies, AI, and even scientists themselves favor tried-and-tested research avenues over exploring new ideas, to the detriment of progress.

Read the full article at: qspace.fqxi.org

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

Read the full article at: www.pnas.org

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

Read the full article at: arxiv.org

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

Read the full article at: papers.ssrn.com

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.

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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.

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

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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.

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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.

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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.

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