Mohsen Mosleh, Jennifer Allen, and David G. Rand When analyzing over 10 million posts across 7 social media platforms, we find stark differences across platforms in the political lean and quality of news shared, as well as qualitatively different patterns of engagement. While lower-quality news domains are shared more on right-leaning platforms, and news from a platform’s dominant political orientation receives more engagement, we nonetheless find that a given user's lower-quality news posts consistently attract more user engagement than their higher-quality content—even on left-leaning platforms. This pattern holds even though we account for all user-level variation in engagement, and even on platforms without complex algorithms. These findings highlight the importance of examining cross-platform variation and offer insights into political echo chambers and the spread of misinformation. Read the full article at: www.pnas.org

José Ignacio Arroyo, Amahury J. Lopez-Diaz, Alejandro Maass, Carlos Gershenson, Pablo Marquet, Geoffrey West, Christopher P. Kempes Understanding the extent to which genetic correlations change in response to environmental factors, such as temperature, is a poorly explored question, despite the importance of understanding how different processes will change with climate warming. Despite correlations between thermal performance traits having been reported in the literature for a few taxa and performance tasks, such as population growth rate, a comprehensive global analysis of the entire tree of life and multiple performance tasks remains an open challenge. To advance in this open question, we compile a database of 1,300 thermal response curves, encompassing 38 variable types related to individuals’ performance (including per capita population growth rate, photosynthetic rate, among others) and 1,125 different species, ranging from viruses to mammals, encompassing all major lineages of the tree of life. Our analysis reveals that among all possible relationships between traits and optimal performance, four traits form a line with a high goodness-of-fit, while the remaining traits exhibit a polygonal pattern, either a triangle or a tetrahedron. We derive a thermodynamic framework that explains the relationships described by a curve or line (as opposed to a surface or polygon), highlighting the linear relationship between maximum and minimum temperatures, as well as between maximum and optimum temperatures. We also discuss other generic trait evolution models, which could account for the other significant sublinear relationships, as well as the more general model, Pareto optimality theory, which could account for relationships in the form of lines or polygons. Our theoretical framework and empirical evidence suggest that, based on a single data point (e.g., minimum temperature), all critical temperature limits and maximum performance boundaries can be predicted using the estimated parameter from this study. Our results reveal universal scaling relationships in thermal performance, which could be useful for predicting changes in performance under scenarios of climate warming. Read the full article at: www.biorxiv.org See Also: A database of biological thermal performances

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

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

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

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

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

Chao Duan & Adilson E. Motter Nature Communications volume 16, Article number: 7242 (2025) Averting catastrophic global warming requires decisive action to decarbonize key sectors. Vehicle electrification, alongside renewable energy integration, is a long-term strategy toward zero carbon emissions. However, transitioning to fully renewable electricity may take decades—during which electric vehicles may still rely on carbon-intensive electricity. We analyze the critical role of the transmission network in enabling or constraining emissions reduction from U.S. vehicle electrification. Our models reveal that the available transmission capacity severely limits potential CO2 emissions reduction. With adequate transmission, full electrification could nearly eliminate vehicle operational CO2 emissions once renewable generation reaches the existing nonrenewable capacity. In contrast, the current grid would support only a fraction of that benefit. Achieving the full emissions reduction potential of vehicle electrification during this transition will require a moderate but targeted increase in transmission capacity. Our findings underscore the pressing need to enhance transmission infrastructure to unlock the climate benefits of large-scale electrification and renewable integration. Read the full article at: www.nature.com

P.G. Tello,  S. Kauffman BioSystems Volume 258, December 2025, 105618 This work revisits the Maxwell Demon paradigm to explore its implications for evolutionary dynamics from an information-theoretic perspective. By removing the Demon as an intentional agent, we reinterpret the emergence of order as a natural outcome of physical laws combined with stochastic processes. Using models inspired by information theory, such as binary and Z-channels, we show how random fluctuations (e.g., stochastic resonance) can decrease entropy, generate mutual information, and induce non-ergodicity. These dynamics highlight the role of memory and correlation as emergent features of purely physical interactions without recourse to purposeful agency. In this framework, evolutionary exaptations, rather than sole adaptations, emerge as key drivers of biological evolution. Finally, we connect our analysis with recent contributions on agency and memory, underscoring the relevance of informational concepts for understanding the purposeless yet structured dynamics of evolutionary processes. Read the full article at: www.sciencedirect.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

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

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

Christoph Riedl When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We introduce an information-theoretic framework to test -- in a purely data-driven way -- whether multi-agent systems show signs of higher-order structure. This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems, localize it, and distinguish spurious temporal coupling from performance-relevant cross-agent synergy. We implement both a practical criterion and an emergence capacity criterion operationalized as partial information decomposition of time-delayed mutual information (TDMI). We apply our framework to experiments using a simple guessing game without direct agent communication and only minimal group-level feedback with three randomized interventions. Groups in the control condition exhibit strong temporal synergy but only little coordinated alignment across agents. Assigning a persona to each agent introduces stable identity-linked differentiation. Combining personas with an instruction to ``think about what other agents might do'' shows identity-linked differentiation and goal-directed complementarity across agents. Taken together, our framework establishes that multi-agent LLM systems can be steered with prompt design from mere aggregates to higher-order collectives. Our results are robust across emergence measures and entropy estimators, and not explained by coordination-free baselines or temporal dynamics alone. Without attributing human-like cognition to the agents, the patterns of interaction we observe mirror well-established principles of collective intelligence in human groups: effective performance requires both alignment on shared objectives and complementary contributions across members. https://arxiv.org/abs/2510.05174 

Jaemin Lee, David Lazer, Christoph Riedl Sociological Science Complex contagion rests on the idea that individuals are more likely to adopt a behavior if they experience social reinforcement from multiple sources. We develop a test for complex contagion, conceptualized as social reinforcement, and then use it to examine whether empirical data from a country-scale randomized controlled viral marketing field experiment show evidence of complex contagion. The experiment uses a peer encouragement design in which individuals were randomly exposed to either one or two friends who were encouraged to share a coupon for a mobile data product. Using three different analytical methods to address the empirical challenges of causal identification, we provide strong support for complex contagion: the contagion process cannot be understood as independent cascades but rather as a process in which signals from multiple sources amplify each other through synergistic interdependence. We also find social network embeddedness is an important structural moderator that shapes the effectiveness of social reinforcement. https://sociologicalscience.com/articles-v12-28-685/ 

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

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

https://www.youtube.com/watch?app=desktop&v=dQC7AIT91_gTheoretical computer science has over the years sought more and more refined answers to the question of which mathematical truths are knowable by finite beings like ourselves, bounded in time and space and subject to physical laws. I'll tell a story that starts with Godel's Incompleteness Theorem and Turing's discovery of uncomputability. I'll then introduce the spectacular Busy Beaver function, which grows faster than any computable function. Work by me and Yedidia, along with recent improvements by O'Rear, Riebel, and others, has shown that the value of BB(549) is independent of the axioms of set theory; on the other end, an international collaboration proved last year that BB(5) = 47,176,870. I'll speculate on whether BB(6) will ever be known, by us or our AI successors. I'll next discuss the P!=NP conjecture and what it does and doesn't mean for the limits of machine intelligence. As my own specialty is quantum computing, I'll summarize what we know about how scalable quantum computers, assuming we get them, will expand the boundary of what's mathematically knowable. I'll end by talking about hypothetical models even beyond quantum computers, which might expand the boundary of knowability still further, if one is able (for example) to jump into a black hole, create a closed timelike curve, or project oneself onto the holographic boundary of the universe. Watch at: www.youtube.com

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

Christoph Riedl, Ben Weidmann We introduce a novel Bayesian Item Response Theory framework to quantify human–AI synergy, separating individual and collaborative ability while controlling for task difficulty in interactive settings. Unlike standard static benchmarks, our approach models human–AI performance as a joint process, capturing both user-specific factors and moment-to-moment fluctuations. We validate the framework by applying it to human–AI benchmark data (n=667) and find significant synergy. We demonstrate that collaboration ability is distinct from individual problem-solving ability. Users better able to infer and adapt to others’ perspectives achieve superior collaborative performance with AI–but not when working alone. Moreover, moment-to-moment fluctuations in perspective taking influence AI response quality, highlighting the role of dynamic user factors in collaboration. By introducing a principled framework to analyze data from human-AI collaboration, interactive benchmarks can better complement current single-task benchmarks and crowd-assessment methods. This work informs the design and training of language models that transcend static prompt benchmarks to achieve adaptive, socially aware collaboration with diverse and dynamic human partners. https://osf.io/preprints/psyarxiv/vbkmt_v1 

Watch at: vimeo.com

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

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

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

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

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