Complexity Digest (@cxdig) Bsky

Wildlife trade drives animal-to-human pathogen transmission over 40 years JÉRÔME M. W. GIPPET, COLIN J. CARLSON, TRISTAN KLAFTENBERGER, MATTÉO SCHWEIZER, EVAN A. ESKEW, MEREDITH L. GORE, AND CLEO BERTELSMEIER SCIENCE 9 Apr 2026 Vol 392, Issue 6794 pp. 178-182 The wildlife trade affects a quarter of terrestrial vertebrates and creates opportunities for cross-species pathogen transmission, but its precise role in shaping animal-human pathogen exchange remains unclear. In our analysis of 40 years of global wildlife trade data, we show that traded mammals are 1.5-fold as likely to share pathogens with humans as nontraded mammals, and that illegal and live-animal trade further exacerbate pathogen sharing. Time spent in trade predicts the number of zoonotic pathogens that a wildlife species hosts. On average, a species shares an additional pathogen with humans for every 10 years it is traded. Read the full article at: www.science.org

Wildlife trade drives animal-to-human pathogen transmission over 40 years

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On Importance Sampling and Multilinear Extensions for Approximating Shapley Values with Applications to Explainable Artificial Intelligence Tim Pollmann and Jochen Staudacher Complexities 2026, 2(1), 7 Shapley values are the most widely used point-valued solution concept for cooperative games and have recently garnered attention for their applicability in explainable machine learning. Due to the complexity of Shapley value computation, users mostly resort to Monte Carlo approximations for large problems. We take a detailed look at an approximation method grounded in multilinear extensions proposed in 2021 under the name “Owen sampling”. We point out why Owen sampling is biased and propose unbiased alternatives based on combining multilinear extensions with stratified sampling and importance sampling. Finally, we discuss empirical results of the presented algorithms for various cooperative games, including real-world explainability scenarios. Read the full article at: www.mdpi.com

On Importance Sampling and Multilinear Extensions for Approximating Shapley Values with Applications to Explainable Artificial Intelligence

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

Human mobility in the metaverse mirrors patterns in the physical world | Scientific Reports

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Twelfth International Conference on Guided Self-Organization (GSO-2026) "Information Processing in Complex Systems" The 12th International Conference on Guided Self-Organization takes place during October 14-15, 2026 in Binghamton, NY (USA), during The 2026 Conference on Complex Systems (CCS 2026) . GSO-2026 is organized by The State University of New York at Binghamton and The International Association for Guided Self-Organization (TIA-GSO). Research Aims and Topics GSO "aims to regulate self-organization for specific purposes, so that a dynamical system may reach specific attractors or outcomes. The regulation constrains a self-organizing process within a complex system by restricting local interactions between the system components, rather than following an explicit control mechanism or a global design blueprint." Information processing in complex self-organizing systems involves the storage, transfer, and modification of information through the interactions of components within the system. Unlike traditional computers, which process digital information in a centralized manner, complex systems like biological organisms or social networks process information in decentralized, distributed, and often analog ways. The study of information processing in complex systems seeks to define a set of universal properties that can describe the dynamics of diverse systems, from brain networks to financial markets, using a common language. Understanding information processing in complex systems is fundamental to designing self-organizing systems, engineering collective behavior and developing energetically efficient models of computation. Modern approaches use frameworks from fields such as information theory, dynamical systems, and machine learning to model how systems ranging from economies to ant colonies process information. The GSO-2026 conference will bring together invited experts and researchers in unconventional computation, swarm intelligence, open-ended evolution, and complex adaptive systems. Special topics of interest include: synthetic and systems biology, agent-based modeling, evolutionary and adaptive computation, socio- and bio-inspired algorithms, swarm robotics, physics of self-organizing behavior, information-driven self-organization, and self-organizing cyber-physical systems. More at: www.guided-self.org

Twelfth International Conference on Guided Self-Organization (GSO-2026)

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Call for Abstracts: CCS 2026: The 2026 Conference on Complex Systems @ Binghamton, NY, USA Abstract submission deadline: May 1, 2026 We call for submissions of abstracts for oral and poster presentations on a wide variety of complex systems research. Relevant topics include (but are not limited to):Theoretical foundations of complex systemsNonlinear dynamics and chaosSystems theory, information theory, and systems scienceGame theory, decision theory, and socio-economical applicationsSelf-organization, pattern formation, and collective behaviorStructure and dynamics of complex networksSustainability and adaptability of complex systemsBio-inspired systems, machine learning, and evolutionary computationData-driven approaches to complex systemsApplications to the humanities, art, and literatureHistorical and philosophical aspects of complex systemsComplex systems and educationMore at: ccs26.cssociety.org

Call for Abstracts: CCS 2026: The 2026 Conference on Complex Systems @ Binghamton, NY, USA

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The degree of fine-tuning in our universe - and others Adams, Fred C. Both the fundamental constants that describe the laws of physics and the cosmological parameters that determine the properties of our universe must fall within a range of values in order for the cosmos to develop astrophysical structures and ultimately support life. This paper reviews the current constraints on these quantities. The discussion starts with an assessment of the parameters that are allowed to vary. The standard model of particle physics contains both coupling constants (α ,αs ,αw) and particle masses (mu ,md ,me) , and the allowed ranges of these parameters are discussed first. We then consider cosmological parameters, including the total energy density of the universe (Ω) , the contribution from vacuum energy (ρΛ) , the baryon-to-photon ratio (η) , the dark matter contribution (δ) , and the amplitude of primordial density fluctuations (Q) . These quantities are constrained by the requirements that the universe lives for a sufficiently long time, emerges from the epoch of Big Bang Nucleosynthesis with an acceptable chemical composition, and can successfully produce large scale structures such as galaxies. On smaller scales, stars and planets must be able to form and function. The stars must be sufficiently long-lived, have high enough surface temperatures, and have smaller masses than their host galaxies. The planets must be massive enough to hold onto an atmosphere, yet small enough to remain non-degenerate, and contain enough particles to support a biosphere of sufficient complexity. These requirements place constraints on the gravitational structure constant (αG) , the fine structure constant (α) , and composite parameters (C⋆) that specify nuclear reaction rates. We then consider specific instances of possible fine-tuning in stellar nucleosynthesis, including the triple alpha reaction that produces carbon, the case of unstable deuterium, and the possibility of stable diprotons. For all of the issues outlined above, viable universes exist over a range of parameter space, which is delineated herein. Finally, for universes with significantly different parameters, new types of astrophysical processes can generate energy and thereby support habitability. Read the full article at: ui.adsabs.harvard.edu

The degree of fine-tuning in our universe - and others - via @ricardsole.bsky.social

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3D Imaging of Honeybee Swarm Assembly and Disassembly Danielle L. Chase, Daniel Zhu, Mahi Kathait, Henry Robertson, Jash Shah, Sully Harrer, Gary Nave, Nolan R. Bonnie, Orit Peleg When honeybee colonies reproduce by fission, several thousand bees and their queen depart the parental nest and temporarily form a dense cluster on a tree branch or other surface while searching for a new nest site. Once the new nest site is selected, the swarm disassembles and flies toward it. How honeybees transition rapidly between dispersed flight and an aggregated cluster remains an open question. Here, we develop an experimental system and three-dimensional imaging pipeline to track individual flying bees together with the evolving morphology of the swarm during formation and dissolution. We report results from a representative swarming event. During assembly, swarms rapidly form low-density clusters before undergoing a slower contraction to a more dense steady state configuration. In contrast, disassembly occurs significantly faster than assembly and is characterized by strongly divergent flight, with bees departing the swarm in all directions. Overall, this method is able to demonstrate the coupled flight and morphological dynamics that underlie honeybee swarm assembly. Because the system is relatively low-cost and low-power, it is readily adaptable for three-dimensional imaging of other biological collectives in naturalistic environments. Read the full article at: www.biorxiv.org

3D Imaging of Honeybee Swarm Assembly and Disassembly | bioRxiv

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

Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender by Steven D Shaw, Gideon Nave :: SSRN

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Directional information transfer between interacting Brownian particles Tenta Tani We theoretically investigate how information flows when two particles interact with each other. Understanding the physical mechanisms of directional information flow is crucial for advancing information thermodynamics and stochastic computing. However, the fundamental connection between mechanical motion and causal information transfer remains elusive. To focus only on essential effects of physical dynamics, we examine two interacting Brownian particles confined in a one-dimensional potential. By simulating their Langevin dynamics, we quantify the causal information exchange using transfer entropy. We demonstrate that a mass asymmetry inherently breaks the symmetry of information flow, inducing a net directional transfer from the heavier to the lighter particle. Physically, the heavier particle, possessing larger inertia and higher active information storage, retains the memory of its trajectory longer against thermal fluctuations, thereby acting as a source of information. We analytically clarify that this net transfer is governed by a competition between the difference in memory capacity and the predictability of the particle trajectories. Furthermore, we reveal that the net information flow scales logarithmically with the mass ratio. These findings provide essential insights into the physical significance of transfer entropy and the nature of information flow in general physical systems. Read the full article at: arxiv.org

[2603.10475] Directional information transfer between interacting Brownian particles

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Social Influence and the Logic of Collective Action, by Sergey Gavrilets Collective action has been a fundamental aspect of human societies throughout history, from building irrigation systems and defenses in Neolithic times to coordinated disaster relief and scientific collaborations today. In this book, Sergey Gavrilets explains when and why groups of people cooperate, presenting a quantitative framework that unifies game theory with models of social influence, cognition, and individual and cultural variation. He shows how humans’ deep susceptibility to social influence—grounded in evolutionary need to cooperate and learn from peers, reinforced by deference to parents and elders, and extended to cultural, religious, and political leaders—shapes norms, beliefs, and collective outcomes. Integrating previously separate literatures, Gavrilets introduces explicit dynamics for norms and beliefs, quantifies the effects of individual and cultural differences, and tests predictions across societies. Drawing on formal, data-based mathematical modeling supported by behavioral experiments and studies of online behavior, he concludes that successful collective action depends on six interacting forces: material payoffs, personal norms and attitudes, social influence, cognition, evolving social norms and beliefs about others, and individual and cultural differences. Lasting cultural change, he argues, depends on norms and institutions that shape behavior through persuasion, nudging, and enforcement. Gavrilets translates this theory into practical, testable strategies for policy and design, including targeted messaging, dynamic norms, and culturally sensitive approaches, and connects it to broader theories of behavior change. More at: press.princeton.edu

Social Influence and the Logic of Collective Action, by Sergey Gavrilets

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From description to design: Automated engineering of complex systems with desirable emergent properties Thomas F. Varley, Josh Bongard The study of complex systems has produced a huge library of different descriptive statistics that scientists can use to describe the various emergent patterns that characterize complex systems. The problem of engineering systems to display those patterns from first principles is a much harder one, however, as a hallmark of complexity is that macro-scale emergent properties are often difficult to predict from micro-scale features. Here, we propose a general optimization-based pipeline to automate the difficult problem of engineering emergent features by re-purposing descriptive statistics as loss functions, and letting a gradient descent optimizer do the hard work of designing the relevant micro-scale features and interactions. Using Kuramoto systems of coupled oscillators as a test bed, we show that our approach can reliably produce systems with non-trivial global properties, including higher-order synergistic information, multi-attractor metastability, and meso-scale structures such as modules and integrated information. We further show that this pipeline can also account for and accommodate constraints on the system properties, such as the costs of connections, or topological restrictions. This work is a step forward on the path moving complex systems science from a field predicated largely on description and post-hoc storytelling towards one capable of engineering real-world systems with desirable emergent meso-scale and macro-scale properties. Read the full article at: arxiv.org

[2603.15631] From description to design: Automated engineering of complex systems with desirable emergent properties

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Jordan Scharnhorst: Entropy, Coarse-graining, and the 2nd Law of Thermodynamics Binghamton Center of Complex Systems (CoCo) Extra Seminar March 24, 2026Watch at: vimeo.com

Jordan Scharnhorst: Entropy, Coarse-graining, and the 2nd Law of Thermodynamics

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

Antifragility: A Cross-Cutting Concept for Understanding Ecological Responses to Variability

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Call for Papers for the 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐋𝐢𝐟𝐞 𝐟𝐨𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 special session at ALIFE Conference 2026 More information about the session and how to submit: https://alifeforscience.github.io

Call for Papers for the 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐋𝐢𝐟𝐞 𝐟𝐨𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 special session at ALIFE Conference 2026

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Scaling laws for function diversity and specialization across socioeconomic and biological complex systems Vicky Chuqiao Yang, James Holehouse, Hyejin Youn, José Ignacio Arroyo, Sidney Redner, Geoffrey B. West, and Christopher P. Kempes PNAS 123 (7) e2509729123 Diversification and specialization are central to complex adaptive systems, yet overarching principles across domains remain elusive. We introduce a general theory that unifies diversity and specialization across disparate systems, including microbes, federal agencies, companies, universities, and cities, characterized by two key parameters. We show from extensive data that function diversity scales with system size as a sublinear power law-resembling Heaps’ law-in all but cities, where it is logarithmic. Our theory explains both behaviors and suggests that function creation depends on system goals and structure: federal agencies tend to ensure functional coverage; cities slow new function growth as old ones expand, and cells occupy an intermediate position. Once functions are introduced, their growth follows a remarkably universal pattern across all systems. Read the full article at: www.pnas.org

Scaling laws for function diversity and specialization across socioeconomic and biological complex systems

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AI agents are ‘aeroplanes for the mind’: five ways to ensure that scientists are responsible pilots Dashun Wang As artificial-intelligence systems take on more of the scientific workflow, the central goal should not be complete automation, but designing platforms that preserve creativity, responsibility and surprise. Read the full article at: www.nature.com

AI agents are ‘aeroplanes for the mind’: five ways to ensure that scientists are responsible pilots

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What is emergence, after all? Abbas K Rizi PNAS Nexus, Volume 5, Issue 2, February 2026, pgag010, The term emergence is increasingly used across scientific disciplines to describe phenomena that arise from interactions among a system's components but cannot be readily inferred by examining those components in isolation. While often invoked to explain higher-level behaviors—such as flocking, synchronization, or collective intelligence—the term is frequently used without precision, sometimes giving rise to ambiguity or even mystique. In this perspective paper, I clarify the scientific meaning of emergence as a measurable and physically grounded phenomenon. Through concrete examples—such as temperature, magnetism, and herd immunity in social networks—I review how collective behavior can arise from local interactions that are constrained by global boundaries. By refining the concept of emergence, it is possible to gain a clearer and more grounded understanding of complex systems. My goal is to show that emergence, when properly framed, offers not mysticism, but rather insight. Read the full article at: academic.oup.com

What is emergence, after all? | PNAS Nexus | Oxford Academic

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The Economy as an Evolving Complex System IV The contemporary global economy exhibits unprecedented structural complexity—characterized by nonlinear dynamics, adaptive behaviors, and emergent properties. Understanding these phenomena requires theoretical frameworks capable of addressing complexity, path dependence, and evolutionary processes. Complexity economics has developed to address such intellectual challenges. Originating in a seminal 1987 Santa Fe Institute workshop and first described in The Economy as an Evolving Complex System (1988), this approach fundamentally reconceptualizes economic systems as complex adaptive systems. Subsequent volumes (1997, 2005) progressively developed this framework, offering new insights into finance, technological innovation, and social interactions. Like each of its predecessors, this fourth volume is the product of an interdisciplinary workshop hosted at the Santa Fe Institute. It represents the latest synthesis, reflecting theoretical advances and methodological developments achieved over nearly four decades. Drawing on contributions from leading scholars worldwide, the chapters span foundational questions to policy applications—from agent-based modeling and network theory to macroeconomic dynamics, innovation systems, sustainability transitions, and inequality. The result demonstrates complexity economics' capacity to generate novel insights into phenomena that remain puzzling within traditional frameworks: financial instability, technological disruption, climate economics, and institutional change. This volume positions complexity economics as an essential analytical framework for understanding twenty-first-century economic realities. More at: www.sfipress.org

The Economy as an Evolving Complex System IV

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On the equivalence between nonlinear graph-based dynamics and linear dynamics on higher-order networks Lucas Lacasa In network science, collective dynamics of complex systems are typically modelled as (nonlinear, often including many-body) vertex-level update rules evolving over a graph interaction structure. In recent years, frameworks that explicitly model such higher-order interactions in the interaction backbone (i.e. hypergraphs) have been advanced, somehow shifting the imputation of the effective nonlinearity from the dynamics to the interaction structure. In this work we discuss such structural--dynamical representation duality, and investigate how and when a nonlinear dynamics defined on the vertex set of a graph allows an equivalent representation in terms of a linear dynamics defined on the state space of a sufficiently richer, higher-order interaction structure. Using Carleman linearisation arguments, we show that finite polynomial dynamics defined in the |V| vertices of a graph admit an exact representation as linear dynamics on the state space of an hb-graph of order |V|, a combinatorial structure that extends hypergraphs by allowing vertex multiplicity, where the specific shape of the nonlinearity indicates whether the hb-graph is either finite or infinite (in terms of the number of hb-edges). For more general analytic nonlinearities, exact linear representation always require an hb-graph of infinite size, and its finite-size truncation provides an approximate representation of the original nonlinear graph-based dynamics. Read the full article at: arxiv.org

[2602.21727] On the equivalence between nonlinear graph-based dynamics and linear dynamics on higher-order networks

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Metrology of Complexity and Implications for the Study of the Emergence of Life Sara Imari Walker One of the longest standing open problems in science is how life arises from non-living matter. If it is possible to measure this transition in the lab, then it might be possible to understand the physical mechanisms by which the emergence of life occurs, which so far have evaded scientific understanding. A significant hurdle is the lack of standards or a framework for cross comparison across different experimental contexts and planetary environments. In this essay, I review current challenges in experimental approaches to origin of life chemistry, focusing on those associated with quantifying experimental selectivity versus de novo generation of molecular complexity, and I highlight new methods using molecular assembly theory to measure molecular complexity. This metrology-centered approach can enable rigorous testing of hypotheses about the cascade of major transitions in molecular order marking the emergence of life, while potentially bridging traditional divides between metabolism-first and genetics-first scenarios. Grounding the study of life's origins in measurable complexity has significant implications for the search for life beyond Earth, suggesting paths toward theory-driven detection of biological complexity in diverse planetary contexts. As the field moves forward, standardized measurements of molecular complexity may help unify currently disparate approaches to understanding how matter transforms to life. Much remains to be done in this exciting frontier. Read the full article at: arxiv.org

[2602.18203] Metrology of Complexity and Implications for the Study of the Emergence of Life

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IS ALL THAT GLITTERS A NETWORK? SEARCHING FOR THE BOUNDARIES OF THE NETWORK APPROACH ONERVA KORHONEN Advances in Complex Systems Vol. 28, No. 08, 2530001 (2025) Network analysis has become a powerful tool in various fields. However, the increasing popularity comes with potential problems. Unfamiliarity with the characteristics of the systems under investigation complicates network model construction and interpretation of analysis outcomes. While these issues require special attention in studies that apply the increasingly complex higher-order connectivity models, similar problems are associated with all, even the most simple, network models. Alongside technical issues, network scientists face a philosophical question: can the network approach discover the fundamental nature of a system, on the one hand, and produce useful information, on the other hand. In this perspective, I review the potential problems of the network approach and propose two solutions to address them: active evaluation of the potential and limitations of the network framework before applying a network model and a transition toward an interdisciplinary research practice to interpret analysis outcomes in their right context. Read the full article at: www.worldscientific.com

IS ALL THAT GLITTERS A NETWORK? SEARCHING FOR THE BOUNDARIES OF THE NETWORK APPROACH | Advances in Complex Systems

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

State-Expanding Systems: A Constraint-Limited Theory of Novelty Growth

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Stochastic–dissipative least-action framework for self-organizing biological systems, Part I: Variational rationale and Lyapunov-type behavior How and why do complex chemical and biological systems self-organize into ordered states far from thermodynamic equilibrium? Despite advances in thermodynamics, kinetics, and information theory, a unifying principle that links organization and efficiency across scales has remained elusive. In open systems, productive-event trajectories are conditioned on starting at a source and ending at a sink. This work proposes a stochastic–dissipative least-action triad framework in which (i) a path-ensemble weighting biases trajectories by their action cost, (ii) feedback processes sharpen this distribution, and (iii) the ensemble evolves toward a least-average-action attractor, decreasing during self-organization and increasing during decay. A parametric cross-scale metric—Average Action Efficiency (AAE)—is defined, which is inversely proportional to the average action per productive event. Under reinforcing feedback, identities derived from the exponential-family path measure show that the average action decreases and AAE rises monotonically. In future extensions, this formulation could help bridge quantum, classical, and biological regimes while remaining computationally tractable, because its empirical version relies on aggregate energetic and timing data rather than enumerating individual trajectories. AAE reaches a local maximum at a non-equilibrium steady state under fixed operational context, consistent with the present formulation, and connections to thermodynamic and informational measures are made. A companion article (Part II) details empirical estimation strategies and applications (Georgiev, 2025a). Georgi Yordanov Georgiev BioSystems Volume 262, April 2026, 105647 Read the full article at: www.sciencedirect.com See Also: Part II: Empirical estimation, Average Action Efficiency, and applications to ATP synthase

Stochastic–dissipative least-action framework for self-organizing biological systems, Part I: Variational rationale and Lyapunov-type behavior - ScienceDirect

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BeComplex 2026 - Belgrade School on Complex Systems 21-27 June 2026 at Petnica Science Center. Most of the everyday phenomena we see around us can be categorized as "complex." Such systems consist of many strongly interacting parts and yet, despite this, they exhibit a certain emergent qualitative unity which endows them with a distinct being, separate, although not independent, from that of their constituent elements. These complex systems thus possess a kind of "simplicity" as well, which makes them intelligible and allows them to be studied in their own right. The sheer diversity of complex phenomena—from magnets to climate to the economy to the human brain—prevents them from being investigated under a single theoretical framework. Still, studies such as those of Lorenz and Mandelbrot in the 1970s began to reveal a surprisingly large number of common motifs across these systems, including transitions to chaos, fractal structures, pattern formation, and more. The search for common features of complex systems still remains open. However, most efforts today are focused on understanding particular phenomena. The "Belgrade School of Complex Systems," organized by the Faculty of Physics at the University of Belgrade (http://www.ff.bg.ac.rs/Engleski/index_eng.html), is an attempt to bring together experts from around the world working on various fields that fall under the broad category of complex systems in order to encourage the exchange of knowledge and promote collaboration between like-minded researchers that may be working in seemingly disparate fields. More at: becomplex.net

BeComplex 2026 - Belgrade School on Complex Systems

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Evolving self-organisation workshop @ GECCO 2026 We are thrilled to be returning to GECCO for a second edition of the Evolving Self-organisation workshop and are now accepting submissions! Website: https://evolving-self-organisation-workshop.github.io/gecco-2026/ Submission deadline: March 27 Where: GECCO 2026 is a hybrid conference, with its physical venue located in San José, Costa Rica. When: the conference dates are July 13-17, workshops traditionally happen during the first two days with exact date announced later The organizing committee ------------------------------------------------------------------- Alex Mordvintsev (Google Research, Zurich) Eleni Nisioti (IT University of Copenhagen) Eyvind Niklasson (Google Research, Zurich) Ettore Randazzo (Google Research, Zurich) Mayalen Etcheverry (Google Research, Zurich) Marcello Barylli (IT University of Copenhagen) Milton Montero (IT University of Copenhagen) Sebastian RIsi (IT University of Copenhagen)

Evolving self-organisation workshop @ GECCO 2026

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Bacterial sensors poised at criticality | Nature Physics Junhua Yuan Nature Physics (2026) Spontaneous switching between active and inactive states in bacterial chemosensory arrays is shown to operate near a critical point. Through biologically controlled disorder, cells balance high signal gain with fast response. Read the full article at: www.nature.com

Bacterial sensors poised at criticality | Nature Physics

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Optimizing economic complexity Viktor Stojkoski, César A. Hidalgo Research Policy Volume 55, Issue 4, May 2026, 105454 Efforts to apply economic complexity to identify diversification opportunities often rely on diagrams comparing the relatedness and complexity of products, technologies, or industries. Yet, the use of these diagrams, is not based on empirical or theoretical evidence supporting some notion of optimality. Here, we introduce an optimization-based framework that identifies diversification opportunities by minimizing a cost function capturing the constraints imposed by an economy's pattern of specialization. We show that the resulting portfolios often differ from those implied by relatedness–complexity diagrams, providing a target-oriented optimization layer to the economic complexity toolkit. Read the full article at: www.sciencedirect.com

Optimizing economic complexity - ScienceDirect

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A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness Erik Hoel Scientific theories of consciousness should be falsifiable and non-trivial. Recent research has given us formal tools to analyze these requirements of falsifiability and non-triviality for theories of consciousness. Surprisingly, many contemporary theories of consciousness fail to pass this bar, including theories based on causal structure but also (as I demonstrate) theories based on function. Herein, I show these requirements of falsifiability and non-triviality especially constrain the potential consciousness of contemporary Large Language Models (LLMs) because of their proximity to systems that are equivalent to LLMs in terms of input/output function; yet, for these functionally equivalent systems, there cannot be any falsifiable and non-trivial theory of consciousness that judges them conscious. This forms the basis of a disproof of contemporary LLM consciousness. I then show a positive result, which is that theories of consciousness based on (or requiring) continual learning do satisfy the stringent formal constraints for a theory of consciousness in humans. Intriguingly, this work supports a hypothesis: If continual learning is linked to consciousness in humans, the current limitations of LLMs (which do not continually learn) are intimately tied to their lack of consciousness. Read the full article at: arxiv.org

[2512.12802] A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness

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Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton Ivan Shpurov and Tom Froese Phys. Rev. E 113, 024405 The collective behavior of numerous animal species, including insects, exhibits scale-free behavior indicative of the critical (second-order) phase transition. Previous research uncovered such phenomena in the behavior of honeybees, most notably the long-range correlations in space and time. Furthermore, it was demonstrated that the bee activity in the hive manifests the hallmarks of the jamming process. We follow up by presenting a discrete model of the system that faithfully replicates some of the key features found in the data, such as the divergence of correlation length and scale-free distribution of jammed clusters. The dependence of the correlation length on the control parameter, density, is demonstrated for both the real data and the model. We conclude with a brief discussion on the contribution of the insights provided by the model to our understanding of the insects' collective behavior. Read the full article at: link.aps.org

Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton | Phys. Rev. E

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Calls for the 2026 CSS Emerging Researcher, Junior, and Senior Scientific Awards The Complex Systems Society announces the 2026 edition of the CSS Scientific Awards. The Emerging Researcher Award recognizes promising researchers in Complex Systems within 3 years of their PhD defense. The Junior Scientific Award is aimed at recognizing excellent scientific record of young researchers within 10 years of their PhD defense. The Senior Scientific Award will recognize outstanding contributions of Complex Systems scholars at any stage of their careers. Deadline: April 30th, 2026. See https://cssociety.org/community/awards for the list of previous awardees. More at: cssociety.org

Calls for the 2026 CSS Emerging Researcher, Junior, and Senior Scientific Awards

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