Papers

Xiangyi Meng, Benjamin Piazza, Csaba Both, Baruch Barzel, Albert-László Barabási

The brain's connectome and the vascular system are examples of physical networks whose tangible nature influences their structure, layout, and ultimately their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organisation. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of length and volume minimisation. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimisation problem. We discover, however, an exact mapping of surface minimisation onto high-dimensional Feynman diagrams in string theory, predicting that with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimisation. Specifically, surface minimisation predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organisation of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which not only are prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi.

Read the full article at: arxiv.org

Dirk Helbing

This review article reflects on emerging societies using a data-driven, cybernetic governance approach. Such an approach implies great opportunities, but also considerable challenges and potential ethical issues, requiring scientific and pubic debate. We will start by discussing the role of the Internet of Things for cyber-physical systems and smart societies. After this, we will introduce converging technologies, which are able to connect information technologies with nano-, bio-, and other technologies. While these technologies are currently less known to the wider public, they can be game changers for societies. Among the possible applications, we will pay particular attention to the "Internet of Bodies" and to nano-neurotechnologies. The former can be used in the context of precision medicine, while the latter may eventually enable interactions with the real world just by thought. Both approaches use digital twins and have enormous opportunities , but the risks of accidental damage or intentional misuse are high. As it turns out, quantum technologies have further interesting implications, which may change emerging cybernetic societies as well. Last but not least we will discuss ethical issues and further challenges of cybernetic societies, leading to a call for action.

Read the full article at: www.researchgate.net

J. A. Scott Kelso

The European Physical Journal Special Topics

This tribute to Hermann Haken, the great theoretical physicist, explores the idea—based on a reconsideration of the experiments that led to the HKB model—that intentions (an emergent ‘mental force’) are hidden~exposed in order parameter fluctuations that arise due to special boundary conditions or rate-independent constraints on the basic coordination dynamics of human brain and behavior.

Read the full article at: link.springer.com

bel Jansma, Erik Hoel

One of the reasons complex systems are complex is because they have multiscale structure. How does this multiscale structure come about? We argue that it reflects an emergent hierarchy of scales that contribute to the system's causal workings. An example is how a computer can be described at the level of its hardware circuitry but also its software. But we show that many systems, even simple ones, have such an emergent hierarchy, built from a small subset of all their possible scales of description. Formally, we extend the theory of causal emergence (2.0) so as to analyze the causal contributions across the full multiscale structure of a system rather than just over a single path that traverses the system's scales. Our methods reveal that systems can be classified as being causally top-heavy or bottom-heavy, or their emergent hierarchies can be highly complex. We argue that this provides a more specific notion of scale-freeness (here, when causation is spread equally across the scales of a system) than the standard network science terminology. More broadly, we provide the mathematical tools to quantify this complexity and provide diverse examples of the taxonomy of emergent hierarchies. Finally, we demonstrate the ability to engineer not just degree of emergence in a system, but how that emergence is distributed across the multiscale structure.

Read the full article at: arxiv.org

R. Maria del Rio-Chanona, Ekkehard Ernst, Rossana Merola, Daniel Samaan, Ole Teutloff

Generative AI is altering work processes, task composition, and organizational design, yet its effects on employment and the macroeconomy remain unresolved. In this review, we synthesize theory and empirical evidence at three levels. First, we trace the evolution from aggregate production frameworks to task- and expertise-based models. Second, we quantitatively review and compare (ex-ante) AI exposure measures of occupations from multiple studies and find convergence towards high-wage jobs. Third, we assemble ex-post evidence of AI's impact on employment from randomized controlled trials (RCTs), field experiments, and digital trace data (e.g., online labor platforms, software repositories), complemented by partial coverage of surveys. Across the reviewed studies, productivity gains are sizable but context-dependent: on the order of 20 to 60 percent in controlled RCTs, and 15 to 30 percent in field experiments. Novice workers tend to benefit more from LLMs in simple tasks. Across complex tasks, evidence is mixed on whether low or high-skilled workers benefit more. Digital trace data show substitution between humans and machines in writing and translation alongside rising demand for AI, with mild evidence of declining demand for novice workers. A more substantial decrease in demand for novice jobs across AI complementary work emerges from recent studies using surveys, platform payment records, or administrative data. Research gaps include the focus on simple tasks in experiments, the limited diversity of LLMs studied, and technology-centric AI exposure measures that overlook adoption dynamics and whether exposure translates into substitution, productivity gains, erode or increase expertise.

Read the full article at: arxiv.org

Eleonora Vitanza , Chiara Mocenni and Pietro De Lellis

In this paper, we introduce a novel Markovian model that describes the impact of egosyntonicity on emotion dynamics. We focus on the dominant current emotion and describe the time evolution of its valence, modelled as a binary variable, where 0 and 1 correspond to negative and positive valences, respectively. In particular, the one-step transition probabilities will depend on the external events happening in daily life, the attention the individual devotes to such events, and the egosyntonicity, modelled as the agreement between the current valence and the internal mood of the individual. A steady-state analysis shows that, depending on the model parameters, four classes of individuals can be identified. Two classes are somewhat expected, corresponding to individuals spending more (less) time in egosyntonicity experiencing positive valences for longer (shorter) times. Surprisingly, two further classes emerge: the self-deluded individuals, where egosyntonicity is associated to a prevalence of negative valences, and the troubled happy individuals, where egodystonicity is associated to positive valences. These findings are aligned with the literature showing that, even if egosyntonicity typically has a positive impact in the short term, it may not always be beneficial in the long run.

Read the full article at: royalsocietypublishing.org

Laurent Hébert-Dufresne, Juniper Lovato, Giulio Burgio, James P. Gleeson, S. Redner, and P. L. Krapivsky

Phys. Rev. Lett. 135, 087401

Models of how things spread often assume that transmission mechanisms are fixed over time. However, social contagions—the spread of ideas, beliefs, innovations—can lose or gain in momentum as they spread: ideas can get reinforced, beliefs strengthened, products refined. We study the impacts of such self-reinforcement mechanisms in cascade dynamics. We use different mathematical modeling techniques to capture the recursive, yet changing nature of the process. We find a critical regime with a range of power-law cascade size distributions with nonuniversal scaling exponents. This regime clashes with classic models, where criticality requires fine-tuning at a precise critical point. Self-reinforced cascades produce critical-like behavior over a wide range of parameters, which may help explain the ubiquity of power-law distributions in empirical social data.

Read the full article at: link.aps.org

Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O. Stanley, Phillip Isola, David Ha

Artificial Life (2025) 31 (3): 368–396.

With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial and error to discover the configurations of lifelike simulations. This article presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called automated search for Artificial Life (ASAL), (a) finds simulations that produce target phenomena, (b) discovers simulations that generate temporally open-ended novelty, and (c) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates, including Boids, Particle Life, the Game of Life, Lenia, and neural cellular automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids life-forms, as well as cellular automata that are open-ended like Conway’s Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.

Read the full article at: direct.mit.edu

Pedro A M Mediano, Fernando E Rosas, Andrea I Luppi, Robin L Carhart-Harris, Daniel Bor , Anil K Seth, and Adam B Barrett

PNAS 122 (39) e2423297122

Complex systems, from the human brain to the global economy, are made of multiple elements that interact dynamically, often giving rise to collective behaviors that are not readily predictable from the “sum of the parts.” To advance our understanding of how this can occur, here we present a mathematical framework to disentangle and quantify different “modes” of information storage, transfer, and integration in complex systems. This framework reveals previously unreported collective behavior phenomena in experimental data across scientific fields, and provides principles to classify and formally relate diverse measures of dynamical complexity and information processing.

Read the full article at: www.pnas.org

GIOVANNI MAURO, NICOLA PEDRESCHI, RENAUD LAMBIOTTE, and LUCA PAPPALARDO

Advances in Complex SystemsVol. 28, No. 06, 2540006 (2025)

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.

Read the full article at: www.worldscientific.com

Qianyang Chen, Nihat Ay, Mikhail Prokopenko

Self-organizing systems consume energy to generate internal order. The concept of thermodynamic efficiency, drawing from statistical physics and information theory, has previously been proposed to characterize a change in control parameter by relating the resulting predictability gain to the required amount of work. However, previous studies have taken a system-centric perspective and considered only single control parameters. Here, we generalize thermodynamic efficiency to multi-parameter settings and derive two observer-centric formulations. The first, an inferential form, relates efficiency to fluctuations of macroscopic observables, interpreting thermodynamic efficiency in terms of how well the system parameters can be inferred from observable macroscopic behaviour. The second, an information-geometric form, expresses efficiency in terms of the Fisher information matrix, interpreting it with respect to how difficult it is to navigate the statistical manifold defined by the control protocol. This observer-centric perspective is contrasted with the existing system-centric view, where efficiency is considered an intrinsic property of the system.

Read the full article at: arxiv.org

Thomas F. Varley, Vaibhav P. Pai, Caitlin Grasso, Jeantine Lunshof, Michael Levin & Josh Bongard

Communicative & Integrative Biology 

Volume 18, 2025 - Issue 1

Understanding how populations of cells collectively coordinate activity to produce the complex structures and behaviors that characterize multicellular organisms, and which coordinated activities, if any, survive processes that reshape cells and tissues into organoids, are fundamental issues in modern biology. Here, we show how techniques from complex systems and multivariate information theory provide a framework for inferring the structure of collective organization in non-neural tissue. Many of these techniques were developed in the context of theoretical neuroscience, where these statistics have been found to be altered during different cognitive, clinical, or behavioral states, and are generally thought to be informative about the underlying dynamics linking biology to cognition. Here, we show that these same patterns of coordinated activity are also present in the aneural tissues of evolutionarily distant biological systems: preparations of embryonic Xenopus laevis tissue (known as “basal Xenobots”). These similarities suggest that such patterns of activity either arose independently in these two systems (epithelial constructs and brains); are epiphenomenological byproducts of other dynamics conserved across vastly different configurations of life; or somehow directly support adaptive behavior across diverse living systems. Finally, these results provide unambiguous support for the hypothesis that, despite their apparent simplicity as collections of non-neural epithelial cells, Xenobots are in fact integrated, complex systems in their own right, with sophisticated internal information structures.

Read the full article at: www.tandfonline.com

Leila Hedayatifar, Alfredo J. Morales, Dominic E. Saadi, Rachel A. Rigg, Olha Buchel, Amir Akhavan, Egemen Sert, Aabir Abubaker Kar, Mehrzad Sasanpour, Irving R. Epstein & Yaneer Bar-Yam 

Scientific Reports volume 15, Article number: 33854 (2025)

Predicting dynamic behaviors is one of the goals of science in general as well as essential to many specific applications of human knowledge to real world systems. Here, we introduce an analytic approach using the sigmoid growth curve to model the dynamics of individual entities within complex systems. Despite the challenges posed by nonlinearity and unpredictability, we demonstrate that sigmoid-like trajectories frequently emerge in systems where entities undergo phases of acceleration and deceleration of growth. Through case studies of (1) customer purchasing behavior and (2) U.S. legislation adoption, we show that these patterns can be identified and used to predict an entity’s ultimate state well in advance of reaching it. This provides valuable insights for business leaders and policymakers. Moreover, our characterization of individual component dynamics offers a framework to reveal the aggregate behavior of the entire system. Moreover, our classification of entity lifepaths contributes to understanding system-level structure by revealing how individual-level dynamics scale to aggregate behaviors. This study offers a practical modeling framework that captures commonly observed growth dynamics in diverse complex systems and supports predictive decision-making.

Read the full article at: www.nature.com

compiled and edited by Ricard Solé, Chris Kempes and Susan Stepney
What is life, and how does it begin? This theme issue explores one of science’s deepest questions: how life can emerge from non-living matter. Researchers from many fields — from physics and chemistry to biology and artificial life — are working to uncover the basic principles that make life possible. Key themes include the role of energy and information in early cells, the plausibility of alternative forms of life, and efforts to recreate life-like systems in the lab. By bringing together diverse perspectives, this issue offers a fresh look at both the limits and possibilities for how life may arise, on Earth and beyond.

Read the full articles at: royalsocietypublishing.org

Petter Holme, Milena Tsvetkova

We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

Read the full article at: arxiv.org

Georgii, Karelin and Nakajima, Kohei and Soto-Astorga, Enrique F. and Carr, Earnest and James, Mark and Froese, Tom

Synergy between stochastic noise and deterministic chaos is a canonical route to unpredictable behavior in nonlinear systems. This letter analyzes the origins and consequences of indeterminism that has recently appeared in leading Large Language Models (LLMs), drawing connections to open-endedness, precariousness, artificial life, and the problem of meaning. Computational indeterminism arises in LLMs from a combination of the non-associative nature of floating-point arithmetic and the arbitrary order of execution in large-scale parallel software-hardware systems. This low-level numerical noise is then amplified by the chaotic dynamics of deep neural networks, producing unpredictable macroscopic behavior. We propose that irrepeatable dynamics in computational processes lend them a mortal nature.
Irrepeatability might be recognized as a potential basis for genuinely novel behavior and agentive artificial intelligence and could be explicitly incorporated into system designs.
The presence of beneficial intrinsic unpredictability can then be used to evaluate when artificial computational systems exhibit lifelike autonomy.

Read the full article at: philsci-archive.pitt.edu

Martin Hendrick, Andrea Rinaldo, and Gabriele Manoli

PNAS 122 (33) e2501224122

Cities can be viewed as living organisms and their metabolism as the set of processes controlling their evolving structure and function. Urban population, transport networks, and all anthropogenic activities have been proposed to mimic body mass, vascular systems, and metabolic rates of living organisms. This analogy is supported by the emergence of seemingly universal scaling laws linking city-scale quantities to population size. However, such scaling relations critically depend on the choices of city boundaries and neglect intraurban variations of urban properties. By capitalizing on today’s availability of high-resolution data, findings emerge on the generality of small-scale covariations in city characteristics and their link to city-wide averages, thus opening broad avenues to understand and design future urban environments.

Read the full article at: www.pnas.org

Sergey Gavrilets and Paul Seabright

PNAS 122 (32) e2504339122

Beliefs about whether the world is a zero-sum or a positive-sum environment vary across individuals and cultures, and affect people’s willingness to work, invest, and cooperate with others. We model interaction between individuals who are biased toward believing the environment is zero-sum, and those biased toward believing it is positive-sum. Beliefs spread through natural and cultural selection if they lead individuals to have higher utilities. If individuals are matched randomly, selection leads to the more accurate beliefs driving out the less accurate. Nonrandom matching and conformity biases can favor the survival of inaccurate beliefs. Cultural authorities can profit from creating enclaves of like-minded individuals whose higher bias drives out the more accurate beliefs of others.

Read the full article at: www.pnas.org

Paul B. Rainey and Michael E. Hochberg

PNAS 122 (37) e2509122122

Artificial intelligence (AI)—broadly defined as the capacity of engineered systems to perform tasks that would require intelligence if done by humans—is increasingly embedded in the infrastructure of human life. From personalized recommendation systems to large-scale decision-making frameworks, AI shapes what humans see, choose, believe, and do (1, 2). Much of the current concern about AI centers on its understanding, safety, and alignment with human values (3–5). But alongside these immediate challenges lies a broader, more speculative, and potentially more profound question: could the deepening interdependence between humans and AI give rise to a new kind of evolutionary individual? We argue that as interdependencies grow, humans and AI could come to function not merely as interacting agents, but as an integrated evolutionary individual subject to selection at the collective level.

Read the full article at: www.pnas.org

IXANDRA ACHITOUV

Advances in Complex SystemsVol. 28, No. 06, 2540005 (2025)

Financial stock returns correlations have been studied in the prism of random matrix theory to distinguish the signal from the “noise”. Eigenvalues of the matrix that are above the rescaled Marchenko–Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis, we use complex network analysis to simulate the “noise” and the “market” component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated “market” random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short-time scale.

Read the full article at: www.worldscientific.com