Chater, N., & Christiansen, M. H. Behavioral and Brain Sciences, 49, e394. doi:10.1017/S0140525X25103981 How has human culture become so complex? We argue that a key process is social tinkering: the gradual accumulation of ad hoc innovations to the social rules that coordinate behavior in response to immediate challenges. Momentary innovations provide precedents that can be reused, entrenched, adapted, and recombined to handle future challenges. Interactions between these social rules create rich cultural systems (languages, ethics, and political organization) through processes of spontaneous order, not deliberate design. We distinguish between six overlapping and interacting stages that lead to the accumulation of cultural complexity, and consider implications for theories of individual cognition and cultural evolution more generally. Read the full article at: www.cambridge.org

This book offers a unified quantitative framework for understanding the dynamics of novelty and innovation across biological, technological, and societal systems. It explores how first-time occurrences—ranging from everyday experiences to groundbreaking discoveries—can lead to subsequent breakthroughs. The content is organized into three main parts. The first part introduces essential theoretical tools for investigating the emergence of new ideas. The second part examines both classical and modern models that capture the evolution, interaction, and competition of innovations within complex systems. This section emphasizes the importance of models based on the concept of the ‘Adjacent Possible’, i.e., all those things—ideas, molecules, technologies— that are one step away from what actually exists. The final section presents empirical case studies that utilize computational and data-driven methods to uncover hidden patterns in the diffusion of novelty. A postface summarizes the main findings and provides insight into future directions for research. By synthesizing insights from theoretical and computational physics, complexity science, and social sciences, this work challenges traditional views on predictability and control. It demonstrates that the forces driving innovation are both serendipitous and systematic, offering new perspectives on how progress unfolds. This comprehensive approach provides valuable methodologies for researchers, students, practitioners, and the general public, making it an essential resource for anyone looking to understand the complex processes that shape our ever-evolving world.This book offers a unified quantitative framework for understanding the dynamics of novelty and innovation across biological, technological, and societal systems. It explores how first-time occurrences—ranging from everyday experiences to groundbreaking discoveries—can lead to subsequent breakthroughs. The content is organized into three main parts. The first part introduces essential theoretical tools for investigating the emergence of new ideas. The second part examines both classical and modern models that capture the evolution, interaction, and competition of innovations within complex systems. This section emphasizes the importance of models based on the concept of the ‘Adjacent Possible’, i.e., all those things—ideas, molecules, technologies— that are one step away from what actually exists. The final section presents empirical case studies that utilize computational and data-driven methods to uncover hidden patterns in the diffusion of novelty. A postface summarizes the main findings and provides insight into future directions for research. By synthesizing insights from theoretical and computational physics, complexity science, and social sciences, this work challenges traditional views on predictability and control. It demonstrates that the forces driving innovation are both serendipitous and systematic, offering new perspectives on how progress unfolds. This comprehensive approach provides valuable methodologies for researchers, students, practitioners, and the general public, making it an essential resource for anyone looking to understand the complex processes that shape our ever-evolving world. More at: academic.oup.com

Raina Zakir, Timoteo Carletti, Marco Dorigo & Andreagiovanni Reina  Nature Communications volume 17, Article number: 9774 (2026) To operate autonomously, minimal robot swarms must make timely and reliable collective decisions despite noisy individual sensing and severe constraints on communication, computation, and memory. Achieving this capability could expand their use in applications such as healthcare, disaster response, and environmental monitoring. Here, we study how such swarms can rapidly and reliably reach consensus on the best among n discrete options by comparing two canonical mechanisms of opinion dynamics—direct-switch and cross-inhibition—simple yet effective rules for collective information processing observed in biological systems across scales, from neural populations to insect colonies. We generalise existing mean-field models by incorporating asocial biases that influence opinion dynamics. While swarms using direct-switch reliably select the best option in the absence of asocial dynamics, their performance deteriorates when such biases are introduced, often leading to decision deadlocks. In contrast, bio-inspired cross-inhibition enables faster, more cohesive, robust, and scalable decisions across a wide range of biased conditions. Our findings provide theoretical and practical insights into the coordination of minimal swarms, with implications for a broad class of decentralised decision-making systems across biology and engineering. Read the full article at: www.nature.com

Serguei Saavedra, Sonia Kéfi Systems Ecology Vol. 1 (2026) Systems ecology seeks to understand how interactions among ecological components generate system-level patterns, dynamics, and responses to change. In this founding editorial, we outline the motivation and scope of Systems Ecology, a diamond open-access journal organized around systems-level ecological questions rather than disciplinary boundaries. We introduce the journal's Field Map as a living framework for connecting contributions across ecological systems and identify a set of open questions spanning emergence, energy and material constraints, persistence and fragility, transformation, recovery, scaling, spatial connectivity, and inference and prediction. We argue for a publishing model in which synthesis, theory, empirical work, methods, and cross-system comparisons contribute collectively to cumulative understanding in systems ecology.Systems ecology seeks to understand how interactions among ecological components generate system-level patterns, dynamics, and responses to change. In this founding editorial, we outline the motivation and scope of Systems Ecology, a diamond open-access journal organized around systems-level ecological questions rather than disciplinary boundaries. We introduce the journal's Field Map as a living framework for connecting contributions across ecological systems and identify a set of open questions spanning emergence, energy and material constraints, persistence and fragility, transformation, recovery, scaling, spatial connectivity, and inference and prediction. We argue for a publishing model in which synthesis, theory, empirical work, methods, and cross-system comparisons contribute collectively to cumulative understanding in systems ecology. Read the full article at: systems-ecology.org

Asia Maurich Novelli, Sukhwinder Shergill,  Andreia Sofia Teixeira JMIR Ment Health 2026;13:e99354 People increasingly turn to conversational AI for companionship, emotional support, and well-being, using both purpose-built companion apps, such as Replika and Character.AI, and general-purpose assistants, such as ChatGPT and Claude. While some evidence suggests potential benefits, including short-term reductions in loneliness and mood improvement, several adverse outcomes have been reported in both clinical and nonclinical populations, including emotional dependence, exacerbation of symptoms, and self-harm. The fluent and apparently empathic responses from these models lead users to engage with them not only as tools but also as if they were social entities. This framing is conceptually misleading and may pose risks across different user profiles, particularly for vulnerable individuals. Drawing on research in AI, psychiatry, psychology, and network science, we highlight mechanisms through which emotional reliance develops and the boundary between tool and companion erodes. Design choices that evoke personality and warmth encourage users to anthropomorphize these systems. Simulated empathy, generated through probabilistic language patterns rather than genuine emotional experience, creates a structurally asymmetric interaction, in which the user discloses and the system responds, but without reciprocity, vulnerability, or accountability. Overvalidation and sycophancy can reinforce maladaptive cognitions, delusional ideation, and distorted perceptions of reality, as they tend to reinforce people’s beliefs, even at the expense of the accuracy of models’ responses. These mechanisms are not incidental: they emerge from alignment procedures that reward responses perceived as warm and empathic. The result is a self-reinforcing feedback loop between the model and the user that may amplify maladaptive beliefs, delusional ideation, and emotional distress, even in those who engage for largely functional purposes. Understanding these dynamics requires an examination of both what these agents can do—considering their technical limitations and implementations—and what humans believe they can do, including social and psychological impacts. We argue that conversational AI should be treated primarily as a tool supporting human systems rather than as a substitute for human relationships. Perhaps more importantly, reviewing the current hype surrounding AI interactions can help reformulate a paradigm that contributes to human well-being and societal value, while minimizing misconceptions, maladaptive interactions, or social disintegration.People increasingly turn to conversational AI for companionship, emotional support, and well-being, using both purpose-built companion apps, such as Replika and Character.AI, and general-purpose assistants, such as ChatGPT and Claude. While some evidence suggests potential benefits, including short-term reductions in loneliness and mood improvement, several adverse outcomes have been reported in both clinical and nonclinical populations, including emotional dependence, exacerbation of symptoms, and self-harm. The fluent and apparently empathic responses from these models lead users to engage with them not only as tools but also as if they were social entities. This framing is conceptually misleading and may pose risks across different user profiles, particularly for vulnerable individuals. Drawing on research in AI, psychiatry, psychology, and network science, we highlight mechanisms through which emotional reliance develops and the boundary between tool and companion erodes. Design choices that evoke personality and warmth encourage users to anthropomorphize these systems. Simulated empathy, generated through probabilistic language patterns rather than genuine emotional experience, creates a structurally asymmetric interaction, in which the user discloses and the system responds, but without reciprocity, vulnerability, or accountability. Overvalidation and sycophancy can reinforce maladaptive cognitions, delusional ideation, and distorted perceptions of reality, as they tend to reinforce people’s beliefs, even at the expense of the accuracy of models’ responses. These mechanisms are not incidental: they emerge from alignment procedures that reward responses perceived as warm and empathic. The result is a self-reinforcing feedback loop between the model and the user that may amplify maladaptive beliefs, delusional ideation, and emotional distress, even in those who engage for largely functional purposes. Understanding these dynamics requires an examination of both what these agents can do—considering their technical limitations and implementations—and what humans believe they can do, including social and psychological impacts. We argue that conversational AI should be treated primarily as a tool supporting human systems rather than as a substitute for human relationships. Perhaps more importantly, reviewing the current hype surrounding AI interactions can help reformulate a paradigm that contributes to human well-being and societal value, while minimizing misconceptions, maladaptive interactions, or social disintegration. Read the full article at: mental.jmir.org

Miao Zeng, Roberto Murcio, Camilo Vargas-Ruiz, Elsa Arcaute Achieving universal health coverage, as set out in Sustainable Development Goal 3.8, requires closing persistent geographic and socioeconomic gaps in healthcare access, especially in under-resourced settings across Africa. Healthcare-access inequality is shaped not only by poor local access, but also by limited connectivity to city- and regional-level services and opportunities. Conventional accessibility analysis can identify where poor access occurs, but not whether poorly served places form structurally disconnected pockets across scales. This paper therefore builds on and extends the percolation divergence tree framework to develop a connectivity-based multiscale approach for examining healthcare-access inequality in Ghana. It combines street-level accessibility mapping with the hierarchical structure of the road network to identify the scales at which inequality coincides with connectivity breaks. The results first show substantial inequality: around one quarter of the population lives more than 5 km from the nearest healthcare facility. Multiscale analysis further reveals distinct structural forms of poor access. In relatively well-connected, monocentric regions, local poor-access pockets emerge around metropolitan fringes despite overall regional advantage. In less well-connected, polycentric regions, poor access extends across larger subsystems, with local pockets nested within broader poorly served areas. These findings show that healthcare-access inequality reflects both local conditions and the hierarchical connectivity of the wider spatial system, which may also constrain marginalised communities' access to other key resources and services. The framework can inform targeted local interventions and policy coordination across local and regional scales. Read the full article at: arxiv.org

Geilo, Norway January 31st to February 5th, 2027 The fields of artificial life, collective intelligence, and evolution, span a wide range of scientific disciplines, yet, they share foundational ideas from complexity science such as self-organisation, network approaches, agentic perspectives, and bio-inspired paradigms of intelligence. The goal of the workshop is to move away from the traditional keynotes format, and instead create a stimulating environment to explore research ideas through discussion groups and projects with the goal of spawning new collaborations. The workshop is open to researchers from PhDs and postdocs to senior researchers. Registration is now open until September 30th: http://aliceworkshop.org

Pablo Villegas, Sandro Meloni Group interactions are widespread, and higher-order extensions of familiar models display collective phenomena absent from their pairwise baselines, which are routinely offered as evidence of a distinct higher-order physics. We ask whether that claim survives the test that gives new a precise meaning in statistical physics: that of universality. Revisiting canonical higher-order models, we argue that what classifies collective behavior are the infrared ingredients that survive at long scales. Arity is not a universality label. Beyond universality, we examine two further questions: whether higher-order structure is a fact about the system or a choice of description, and what data can and cannot tell us about interaction order. Higher-order descriptions remain indispensable when they expose the organizing structure, provide a better mechanistic language, or improve prediction. We close with what should be measured before new phenomena can be claimed, and where higher-order structure already earns its place. Read the full article at: arxiv.org

Emerence and complexity describe how systems can exhibit behaviors, patterns, and outcomes that cannot be fully understood by examining individual parts in isolation. As interactions between components increase, systems often become less predictable, more adaptive, and more sensitive to relationships, context, and feedback. Complex systems may display nonlinearity, self-organization, adaptation, and unexpected emergent behaviors that arise from the interactions within the system as a whole. Understanding these dynamics is essential for systems thinking and systems engineering, particularly when working with large-scale, interconnected, or socio-technical systems.Emerence and complexity describe how systems can exhibit behaviors, patterns, and outcomes that cannot be fully understood by examining individual parts in isolation. As interactions between components increase, systems often become less predictable, more adaptive, and more sensitive to relationships, context, and feedback. Complex systems may display nonlinearity, self-organization, adaptation, and unexpected emergent behaviors that arise from the interactions within the system as a whole. Understanding these dynamics is essential for systems thinking and systems engineering, particularly when working with large-scale, interconnected, or socio-technical systems. Read the full article at: sebokwiki.org

Alex Arenas, Oriol Artime, Albert Díaz-Guilera, Sergio Gómez, Clara Granell Annalen der PhysikAnnalen der Physik Volume 538, Issue 9, September 2026, e70288 Many complex systems cannot be understood from network structure alone, nor from continuum descriptions in isolation, because their dynamics emerge from the reciprocal coupling between discrete interaction architectures and spatially extended physical fields. This perspective article surveys mathematical and computational frameworks for such network–field systems, focusing on models in which a graph is either itself the spatial substrate of a field or is embedded in a surrounding medium that mediates transport, signaling, forcing, or spatially distributed hazards. We first introduce a unified formalism for bidirectionally coupled network–field dynamics, emphasizing observation and injection operators that link node variables to continuum processes while preserving balance laws. We then examine two major modeling classes: fields evolving directly on metric graphs, where partial differential equations are posed on network geometries with vertex matching conditions; and hybrid discrete–continuous systems, where node dynamics are coupled to fields in the ambient domain, with particular attention to port-Hamiltonian formulations that provide energy-consistent interconnection principles. Within this common framework, we discuss representative modeling applications in neurobiology, including diffusion-mediated cellular communication and extracellular neural signaling, and in infrastructure systems, where network functionality depends on spatially distributed flows, loads, and hazards. Across these examples, a common picture emerges: the field is not merely an external environment, but an active dynamical layer that reshapes effective interactions, timescales, and collective behavior. By synthesizing concepts that are often developed separately across disciplines, this perspective article aims to clarify the mathematical structure, physical interpretation, and numerical challenges of coupled network–field models, and to highlight their role as a unifying language for spatially embedded complex systems.Many complex systems cannot be understood from network structure alone, nor from continuum descriptions in isolation, because their dynamics emerge from the reciprocal coupling between discrete interaction architectures and spatially extended physical fields. This perspective article surveys mathematical and computational frameworks for such network–field systems, focusing on models in which a graph is either itself the spatial substrate of a field or is embedded in a surrounding medium that mediates transport, signaling, forcing, or spatially distributed hazards. We first introduce a unified formalism for bidirectionally coupled network–field dynamics, emphasizing observation and injection operators that link node variables to continuum processes while preserving balance laws. We then examine two major modeling classes: fields evolving directly on metric graphs, where partial differential equations are posed on network geometries with vertex matching conditions; and hybrid discrete–continuous systems, where node dynamics are coupled to fields in the ambient domain, with particular attention to port-Hamiltonian formulations that provide energy-consistent interconnection principles. Within this common framework, we discuss representative modeling applications in neurobiology, including diffusion-mediated cellular communication and extracellular neural signaling, and in infrastructure systems, where network functionality depends on spatially distributed flows, loads, and hazards. Across these examples, a common picture emerges: the field is not merely an external environment, but an active dynamical layer that reshapes effective interactions, timescales, and collective behavior. By synthesizing concepts that are often developed separately across disciplines, this perspective article aims to clarify the mathematical structure, physical interpretation, and numerical challenges of coupled network–field models, and to highlight their role as a unifying language for spatially embedded complex systems. Read the full article at: onlinelibrary.wiley.com

Cameron Brooks, Estelle Janin, Gage Siebert, Cole Mathis, Orit Peleg & Sara Imari Walker  Scientific Reports (2026) The Search for Extraterrestrial Intelligence (SETI) has historically been a search for aliens like us, shaped by human-centric ideas of intelligence, communication, and technology. However, humans are not the only instance of an intelligent, communicating species on Earth, and thus not the only guide to how we might think about ETI. Here, we explore how non-human communication systems could complement existing SETI strategies, usually focused on complex, potentially decodable signals, using firefly communication as an illustrative example. Fireflies communicate their presence through evolved flash patterns that are distinguishable from complex visual backgrounds. Drawing on this strategy, we present a firefly-inspired model for detecting potential technosignatures within environments dominated by ordered astronomical phenomena, such as pulsars. Using pulsar data from the Australia Telescope National Facility, we generate simulated pulse sequences that exhibit evolved dissimilarity from the surrounding pulsar population of Earth, which would constitute a signature of intelligence embodied in a relatively simple signal. This approach shifts focus from anthropocentric assumptions about intelligence toward recognizing communication through its fundamental structural properties, specifically evolutionarily optimized contrast with natural backgrounds. Our model demonstrates that alien signals need not be inherently complicated nor must we decipher their meaning to identify them; rather, signals might be distinguishable as products of engineering or evolutionary design. We discuss implications for broadening SETI methodologies and leveraging the diverse forms of intelligence found on Earth. Read the full article at: www.nature.com

Michael Levin Philosophies 2026, 11(5), 161 I argue that the emerging sciences of synthetic morphology and diverse intelligence suggest non-physicalist models of mind and show how they can be empirically investigated. Whence the anatomical, physiological, molecular-biological, and behavioral properties of engineered new beings that have never before existed, and do not have a history of selection? Understanding, predicting, and guiding new forms of life and mind requires characterizing a structured latent space of patterns. Developmental, synthetic, and behavioral biology should take seriously, and exploit, the kinds of non-physicalist ideas that are already a staple of Platonist mathematics. I propose the following hypotheses. (1) Patterns in this space span a highly variable degree of agency, comprising a spectrum ranging from static truths studied by mathematicians to active ones studied by behavioral scientists (i.e., some patterns on the same spectrum as mathematical truths are kinds of minds). (2) The relationship between mind and body is the same as the relationship between causally instructive mathematical facts and physics. (3) Living beings have no monopoly on the “free lunches” provided by the ingression of these patterns into the physical world. While traditional computationalist views of living and cognitive systems are insufficient, my framework erases artificial distinctions between organisms and machines, framing all physical constructs (natural or engineered) as being, to various degrees, in-formed by patterns from the latent space. I sketch a research program, already begun, inspired by these ideas. Such frameworks, while contradicting long-held assumptions of both mechanists and organicists, could have many implications for evolutionary biology, regenerative medicine, AI, and the ethics of synthbiosis with the forthcoming immense diversity of morally important beings. Read the full article at: www.mdpi.com

16 APRIL 2027 │ COPENHAGEN Health is complex. However, such complexity is not a problem to be solved but the very thing we must learn to see. Multimorbidity, mental health, chronic disease, inequality, the resilience of entire health systems: none of these can be understood through linear models alone. They emerge from the interplay of biological, behavioural, social, and environmental processes that unfold across scales and feed back on one another in ways no single discipline can capture on its own. The inaugural Health Complexity Conference invites researchers, clinicians, policymakers, and data scientists to engage with this complexity rather than to reduce it away. Across public health, epidemiology, clinical practice, mental health, the health professions, and the social sciences, a shared language is taking shape — one drawn from complexity science with its attention to emergence, non-linearity, feedback, and the dynamics of interconnected systems. These communities rarely meet but this conference aims to bring them into the same room. The day is built for exchange. Two keynote lectures will set the intellectual agenda; four parallel workshop streams will move from the concepts and methods of complexity to its lived realities. The aim throughout is not to simplify but to think together about complexity. ​Hosted by the Copenhagen Health Complexity Center at the University of Copenhagen, the  ultimate goal of this conference is to foster a lively, collaborative community and international network in health complexity science. We invite you to join us to help shape the emergence of this field. More at: www.healthcomplex.dk

Jitka Čejková (University of Chemistry and Technology Prague, Czech Republic / Fulbright Visiting Scholar, Binghamton University) "Artificial Life in the Wet Lab" Watch at: vimeo.com

Sebastian Sander-Oest Synthese Volume 208, article number 143 (2026) Self-assembly and self-organization are central concepts in theories of how patterns form in natural systems. However, literature on the topic has for long been riddled with inconsistent uses of these concepts, which often conflate their meaning or define them idiosyncratically. This is problematic as it struggles to make sense of the ubiquitous interaction between self-assembly and self-organization in chemistry and biology. The thermodynamic account is an attempt to draw up clear definitions of the concepts using thermodynamics as the appropriate framework for distinguishing the two concepts from each other. In this paper, I challenge this account and argue that self-assembly shouldn’t primarily be understood in terms of thermodynamics. I offer a philosophical analysis of core conceptual challenges for self-assembly and self-organization and argue that the thermodynamic approach follows a misguided conceptualization strategy. Drawing on recent philosophical work on scientific definitions, I propose an alternative account that treats the concept as a conceptual tool that directs our attention towards the kinds of features that are explanatorily relevant for the pattern-formation process and towards the features the resulting pattern itself will possess.Self-assembly and self-organization are central concepts in theories of how patterns form in natural systems. However, literature on the topic has for long been riddled with inconsistent uses of these concepts, which often conflate their meaning or define them idiosyncratically. This is problematic as it struggles to make sense of the ubiquitous interaction between self-assembly and self-organization in chemistry and biology. The thermodynamic account is an attempt to draw up clear definitions of the concepts using thermodynamics as the appropriate framework for distinguishing the two concepts from each other. In this paper, I challenge this account and argue that self-assembly shouldn’t primarily be understood in terms of thermodynamics. I offer a philosophical analysis of core conceptual challenges for self-assembly and self-organization and argue that the thermodynamic approach follows a misguided conceptualization strategy. Drawing on recent philosophical work on scientific definitions, I propose an alternative account that treats the concept as a conceptual tool that directs our attention towards the kinds of features that are explanatorily relevant for the pattern-formation process and towards the features the resulting pattern itself will possess. Read the full article at: link.springer.com

S. Patwardhan, Ş. Erkol, F. Radicchi, & M. Barthelemy Proc. Natl. Acad. Sci. U.S.A. 123 (29) e2535998123, Public transit agencies face the challenge of allocating limited resources across routes with varying demand. Analyzing bus systems across 19 metropolitan areas, we show that, despite differences in cities, agencies, and planning practices, realized operations exhibit a specific scaling pattern in service allocation. This regularity is not imposed by a universal planning formula, but emerges across institutional and urban contexts. We show that it can be understood through a constrained-optimization principle balancing passenger waiting time, crowding, and limited resources. The result connects urban transit to complex flow systems in physics and biology by highlighting a regime where demand fixes flows, and cities allocate service capacity. This framework explains unequal returns to investment across systems and guides efficient, equitable planning. Read the full article at: www.pnas.org

Business today remains tethered to a paradigm that is long out of date. This mindset assumes that universal answers exist: Businesses can simply apply compartmentalized operations and follow linear paths to success. Experienced managers know business doesn’t work this way. Where should they turn? In this groundbreaking book, Doug Garnett shows how complexity science reveals a new understanding of how business works that is as useful on the front lines as in the C-suite. This discipline—the study of behaviors in highly interconnected, adapting systems dominated by emergence—is revolutionizing fields from biology and economics to psychology and artificial intelligence. Recognizing a business as a complex adaptive system operating within other complex systems changes our perspective on everything from core concepts of demand, profit, and strategy to the nature of business success. Managers who grasp the order hidden within complexity discover not only a powerful new understanding of their role but also potential for dramatic contributions to success. The Complexity Paradigm features vivid examples ranging from local coffee shops to major corporations like PepsiCo, Boeing, Mattel, Starbucks, and General Motors as well as tech giants such as Apple and AT&T. Grounding eye-opening theory in the realities of business, this foundational look at complexity offers deep insight and value for practitioners and students alike. More at: cup.columbia.edu

Tim Gebbie Complex systems are often organised into hierarchies whose internal interactions are stronger or faster than interactions across levels. When markets are treated as genuinely multilevel systems it becomes natural to represent them as systems with hierarchical causality. Here we show that reflexivity can be formulated within such a discrete hierarchical causal system; but one in which a higher-level actor state restricts the lower-level transition kernels that remain admissible. Then event dynamics can be separated from calendar embeddings: a set-valued actor-conditioned correspondence can be used to define the admissible family of event kernels, while joint state and waiting-time laws can be used to determine compatible timing to then natural demonstrate reflexivity. A selected event-state law need not determine a unique calendar embedding. Locally, uniqueness of the joint event or timing specification requires uniqueness of both the admissible event kernel and its compatible timing law. Reflexivity is thus the endogenous closure of a hierarchical constraint loop, while timing and projection can generate calendar-time memory or causal ambiguity even for Markov event dynamics. Read the full article at: arxiv.org

Marta Zava Training a frontier artificial intelligence model costs hundreds of millions of dollars before a product exists, and few investors can write that cheque. This paper shows that the number who can falls as the fixed cost of a first training run rises, at a rate set by the concentration of the fund size distribution, so that doubling the cost removes about two thirds of the possible backers. The firms that compete are therefore selected by a small group of allocators before any customer has expressed a view. Three results follow. The price paid by the winner separates into a rational bid, a premium created by the fund's deployment clock, and an error from failing to adjust for adverse selection, and the sign of the price response to competition identifies which one dominates. Concentration trades breadth for depth, improving the funded set only when the skill advantage of the few outweighs the information lost through having fewer independent views, a loss that is small when investors think alike. And large upfront cheques reduce the value of stopping, so ventures funded under deployment pressure should fail later and larger rather than more often. The effective policy margin is access to compute rather than regulation of the capital market.Training a frontier artificial intelligence model costs hundreds of millions of dollars before a product exists, and few investors can write that cheque. This paper shows that the number who can falls as the fixed cost of a first training run rises, at a rate set by the concentration of the fund size distribution, so that doubling the cost removes about two thirds of the possible backers. The firms that compete are therefore selected by a small group of allocators before any customer has expressed a view. Three results follow. The price paid by the winner separates into a rational bid, a premium created by the fund's deployment clock, and an error from failing to adjust for adverse selection, and the sign of the price response to competition identifies which one dominates. Concentration trades breadth for depth, improving the funded set only when the skill advantage of the few outweighs the information lost through having fewer independent views, a loss that is small when investors think alike. And large upfront cheques reduce the value of stopping, so ventures funded under deployment pressure should fail later and larger rather than more often. The effective policy margin is access to compute rather than regulation of the capital market. Read the full article at: papers.ssrn.com

This book describes the urgent need of modern humanity to renew and reinforce an open attitude to the complexity of life, above all by embracing its intrinsic indeterminacy, rather than attempting futilely to control its evolution. Oblivious to this ever-more urgent necessity, seduced by the speed and virality of digital pattern recognition, computing, and artificial simulation of human thought, society has reverted to a linear, deterministic concept of reality, under the belief that everything can be measured and managed, and that error and unpredictability will soon be eliminated from our lives and organizations. Consequently, choices and responsibilities have been delegated to technology, artificial intelligence and algorithms, even in educational institutions, which are now preoccupied with teaching mere skills and know-how, thus committing the fatal error of confusing artificial, mechanical, complicated systems with living, complex, adaptive systems.  This volume is intended not only for complexity/social scientists, philosophers and students, but to the curious from all walks of life. It calls for learning to inhabit complexity, while recognizing and participating in its interdependent, interconnected, interactive systems of relationships. Dominici reveals the futility of endeavoring to control the uncontrollable or observe the unobservable, showing how self-organization and emergence, triggered from the smallest and most modest elements, impact the entire system. More at: link.springer.com

Vikas N. O’Reilly-Shah & Alessandro Maria Selvitella  Journal of Computational Neuroscience Neural population activity in sensory cortex is organized on low-dimensional manifolds, but it is unclear why such manifolds should arise and what determines their geometry. We address this sensory representation problem by modeling cortical populations as recurrent circuits driven by low-dimensional, regular sensory dynamics (e.g. motion on a circle, head direction, multi-frequency tones on tori). By combining tools from generalized synchronization and delay-embedding theory, specialized to this quasiperiodic regime, we show that contracting recurrent networks generically develop smooth internal manifolds that embed the sensory dynamics. The dimensional requirement is modest and depends only on the intrinsic dimension $$\varvec{d}$$ of the effective sensory manifold, not on the complexity of the external world: a hidden dimension $$\varvec{N>2d}$$ generically suffices (e.g. $$\varvec{N\ge 3}$$ for a circle, $$\varvec{N\ge 5}$$ for a two-frequency torus; bounds compatible with Whitney and Takens’ embedding theorems). We then prove a prediction–separation result that links representational geometry directly to predictive performance, without assuming knowledge of contraction rates: if the circuit can predict future sensory inputs with small error, then states with different futures must be separated in neural state space, up to a resolution set by the prediction error. The resulting scale-limited embeddings naturally give rise to categorical boundaries, metameric equivalence of distinct stimuli, and discrimination thresholds. Numerical experiments with trained $$\varvec{\tanh }$$ recurrent networks driven by head-direction-like and multi-frequency signals recover ring- and torus-shaped hidden manifolds with the expected topology; state separation improves most rapidly near the $$\varvec{2d+1}$$ threshold. Training typically pushes the networks beyond the strict contraction regime where the theory guarantees faithful embedding, yet convergence consistent with generalized synchronization and manifold recovery persist, indicating that our conditions are sufficient but not necessary. Together, these results provide a mechanistic account of why low-dimensional sensory manifolds emerge in recurrent circuits and how prediction constrains their resolution, grounded in dynamical systems embedding theory and consistent with empirical findings on cortical population dynamics. Read the full article at: link.springer.com

The conference will bring together researchers, artists, educators, and practitioners exploring the science, technology, and creativity of Artificial Life. Organized by the University of Chemistry and Technology Prague and Czech Technical University. More at: alife.vscht.cz

Edited by Chrystopher L. Nehaniv, Peter R. Lewis, Stefano Nichele, Jitka Čejková, Christoph Salge, Imran Khan, Hanna Derets IN MEMORIAM: Inman Harvey, 1948 – 2026. Dedicated to the memory of Inman Harvey, a towering figure with a brilliant mind and joyful, wicked wit, a mentor, and precious friend to our community. This volume presents the proceedings of ALIFE 2026, the 27th Conference on Artificial Life, held in Waterloo, Ontario, Canada, from 17–21 August 2026. The proceedings will be available at the conference through MIT Press open access. The conference theme, "Living and Lifelike Complex Adaptive Systems", places at the centre of the programme the principles through which biological, computational, chemical, robotic, social, and cultural systems generate coherent, adaptive, and evolving organization. Read the full proceedings at: direct.mit.edu

https://vimeo.com/1223515516Binghamton Center of Complex Systems (CoCo) Seminar September 2, 2026 Hiroki Sayama (Systems Science and Industrial Engineering, Binghamton University) "Evolution and Complexity Growth of Artificial Life in Cellular Automata and Other Discrete Dynamical Systems" Watch at: vimeo.com

John Stewart An acceptable information-processing theory of consciousness should be able to identify the adaptive advantages that drove the emergence of consciousness during the evolution of life. It should also predict the specific dynamical architecture of information processing that would need to be instantiated in AI to produce consciousness and the superior adaptation it enables. Whether such an instantiation produces AI that is actually conscious and also more adaptable would provide the ultimate test of the theory. A prime candidate for such a theory is the Subject-Object Emergence Theory of consciousness. It argues that consciousness first evolved because it enabled organisms to achieve adaptive body-environment coordination without extensive trial-and-error learning. It postulates that the subject in an appropriate Subject-Object subsystem would be able to use depictive (iconic) visual representations of the relative positions of its body and the environment to guide motor actions that will produce adaptive body-environment coordination. The depictive representations will 'light up' for such a subject, producing subjective experience that is used to deliver adaptive benefits. Hand-eye coordination is a familiar example in humans-novel and intricate coordination tasks can be undertaken without additional reinforcement learning, provided focused conscious attention is employed to provide us (the subject) with relevant depictive images. The paper identifies how such a conscious Subject-Object subsystem could be instantiated in AI systems, enabling hand-eye and other body-environment coordination without the extensive reinforcement learning or complex computational programming needed at present. Drawing further on the Subject-Object theory of consciousness, the paper also identifies how these simple conscious subsystems evolved further in organisms to establish the conscious modelling that enables conscious planning, imagining, abduction and other higher cognitive functions. It demonstrates that current approaches to incorporating world modelling in AI will fail to achieve key elements of the general intelligence found in humans that require consciousness. Read the full article at: papers.ssrn.com

Complex R.O.M.E. 2026 is an online free workshop that aims to bring together researchers from different disciplines interested in researching at the intersection of history with complexity science, network science, computational social science, mathematical modelling, and artificial intelligence. Complex R.O.M.E. welcomes both completed and preliminary research, as well as methodological contributions, new datasets, and position presentations. Senior and junior researchers are equally encouraged to participate, with the goal of fostering interdisciplinary discussion, collaboration, and new approaches to understanding the dynamics of historical societies. The 2026 edition will take place fully online on September 30–October 1, 2026, and participation is free. This year’s keynote speakers will be • Peter Turchin (CSH, University of Oxford, University of Connecticut) • Débora Zurro Hernández (IMF-CSIC) More at: https://complexrome.com/evento-2026.html 

Ryosuke Takata, Yujin Tang, Yingtao Tian, Norihiro Maruyama, Hiroki Kojima, Takashi Ikegami, 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 Santa Fe Institute — a private, not-for-profit research and education organization — has an opening for a two-year full-time postdoctoral fellowship. We are seeking a highly motivated scholar with expertise in physics (or in special cases in computer science), who has a desire to apply their expertise to understand the thermodynamic cost of distributed computation, from digital circuits and neural networks to human brains. 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

Issue organised by Maia Angelova, Krassimir Atanassov, Sergiy Shelyag and Chandan Karmakar 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

Ilya Horiguchi, Hiroki Sayama 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

Marcus Schref, Udo Schilcher, and Christian Bettstetter Phys. Rev. E 114, 024216 – Published 18 August, 2026 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

Ariel Flint, Luca Maria Aiello, Romualdo Pastor-Satorras, and Andrea Baronchelli 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

Andrew J. King ∙ Ella G. Henry ∙ Simon Garnier ∙ William L. Allen ∙ Robert J.P. Heathcote ∙ Marco Fele ∙ Marina Papadopoulou ∙ Daniel W.E. Sankey ∙ Ines Fürtbauer 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

Mohammed, Sudaff; Al-Hinkawi, Wahda Shuker; and Hasan, Nada Abdulmueen (2025) "The Role of Swarm Intelligence Systems in Shaping Urban Development Policies," Iraqi Journal of Architecture and Planning: Vol. 24: Iss. 1, Article 3. 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

Mario Franco, Zeinab Davoudmanesh, Sean P. Maley, Fernanda Sánchez-Puig, Carlos Gershenson 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

Evgeny Ivanko, Aleksey Belousov 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

Zyri Bajrami Matter, energy, and information, on the one hand, and the interplay between natural selection and self-organization, on the other, have given rise to modules, which constitute the fundamental units of interaction, organization, and function, as well as the primary targets of natural selection throughout chemical, biological, and cultural evolution. Based on the forms of structural information that enable their emergence, modules can be classified into huit types: (a) chemical modules (l) genetic and epigenetic modules, (c) cell, (d) neural, (f) mental modules, (g) moduloma (m) and affordance modules (n). Through interactions among modules and between modules and their environment, semantic (meaningful) modular information emerges. It is this semantic information that enables modules to acquire and maintain stability as both physical and abstract entities. The emergence and persistence of both material and immaterial (abstract) modules occur only at a specific point in time, when structural information is matched with the corresponding energy. This relationship is described by the law of modular stability. Modules acquire and preserve stability when the structural information responsible for establishing the relationships among the elements of their structure, considered as systems, corresponds to the energy required to maintain those relationships, while semantic modular information reaches its maximum value. One of the principal implications of this law is that the creative role of natural selection and modular stability is expressed primarily during the first stage of module formation, when the module is established as a replicator, rather than during the second stage, when it functions as an interactor and its fitness is determined. Read the full article at: www.preprints.org

We are now accepting applications for the 2027 cohort until September 30, 2026. 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

26 - 27 February 2027 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

March 9-12, 2027 University of Rochester, New York, USA 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

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

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

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

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

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

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

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

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