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

This book offers a unique perspective on the evolution of scientific theories through the lens of their changing complexity. To explore this non-trivial connection, the author draws on well-known historical cases from the philosophy of science tradition to test the central theses of the work. At the same time, the book develops a conceptual framework in which the debates on emergence and complexity play a central role. The opening chapter provides the historical background of emergence, examining both classical and contemporary perspectives, highlighting diverse viewpoints and their contributions to the current discussion. The second chapter turns to the foundations of complexity science, detailing its key methodologies and emphasizing the role of information in describing and modeling systems. Building on this foundation, the book introduces a novel quantitative definition of emergent properties, grounded in the concept of parametric model complexity. It discusses how slight variations in control parameters can generate universal features and explores the implications of these dynamics for our understanding of systemic behavior. Finally, the author shows how this framework illuminates critical aspects of scientific practice, ranging from the criteria guiding theory choice to the relationship between technological innovation and the risk of the appearance of anomalies. By combining historical analysis, conceptual innovation, and formal modeling, the book presents a compelling vision of how complexity and emergence can be predictive indicators of theoretical transformation, recognizing the moments when our current models have reached their limits. More at: link.springer.com

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

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

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

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

Philip LaPorte, Shiyi Wang, Lenz Pracher, Saptarshi Pal, Martin Nowak In biology, there is often a tension between what is good for the individual and what is good for the population (1–6). Cooperation benefits the community, while defection tempts the individual to garner short term gains. The theory of repeated games specifies that there is a continuum of Nash equilibria which ranges from fully defective to fully cooperative (7,8). The mechanism of direct reciprocity, which relies on repeated interactions, therefore only stipulates that evolution of cooperation is possible, but whether or not cooperation can be established, and for which parameters, depends on the details of the underlying process of mutation and selection (9–18). Many well known evolutionary processes achieve cooperation only in restricted settings. In the case of the donation game (5,6), for example, high benefit to-cost ratios are often needed for selection to favor cooperation (19–22). Here we study a universe of two-player cooperative dilemmas (23), which includes the prisoner’s dilemma (24–27), snowdrift (28–30), stag-hunt (31) and harmony game. Upon those games we apply a universe of evolutionary processes. Among those processes we find a continuous set which has the feature that it achieves maximum payoff for all cooperative dilemmas under direct reciprocity. This set is characterized by a surprisingly simple property which we call parity: competing strategies are evaluated symmetrically. Read the full article at: www.researchsquare.com

Manuel de J. Luevano-Robledo, Alejandro Puga-Candelas Physica D: Nonlinear Phenomena Volume 482, November 2025, 134844 In this work, several random Boolean networks (RBNs) are generated and analyzed based on two fundamental features: their time evolution diagrams and their transition diagrams. For this purpose, we estimate randomness using three measures, among which Algorithmic Complexity stands out because it can (a) reveal transitions towards the chaotic regime more distinctly, and (b) disclose the algorithmic contribution of certain states to the transition diagrams, including their relationship with the order they occupy in the temporal evolution of the respective RBN. Results from both types of analysis illustrate the potential of Algorithmic Complexity and Perturbation Analysis for Boolean networks, paving the way for possible applications in modeling biological regulatory networks. Read the full article at: www.sciencedirect.com

The 2026 Conference on Complex Systems will be held in Binghamton, New York, USA. Binghamton has a long history of creativity and innovation. The Greater Binghamton area has historically been called "the Valley of Opportunity" and attracted many immigrants (especially from Europe) since the mid-1800s, which has made the region's rich and diverse culture and demographics. It is the birthplace of IBM (International Business Machines), Link Flight Simulator, McIntosh Laboratory, and a number of other pioneering ventures, fostering a spirit of ingenuity that continues to thrive. Located at the confluence of the Susquehanna river (one of the oldest rivers in the world) and the Chenango river, the region boasts stunning natural beauty and offers ample opportunities for outdoor recreation, from hiking and biking to fishing and kayaking. In particular, in mid-October when CCS 2026 is held, the Binghamton area will showcase its renowned and breathtaking fall foliage. Furthermore, a vibrant arts and culture scene flourishes here, with numerous galleries, theaters, and music venues providing enriching experiences. CCS 2026 will be held primarily in person on the main campus at Binghamton University, with an online participation option via Zoom. Read the full article at: ccs2026.github.io

Honeybees are renowned for their skills in building intricate and adaptive combs that display notable variation in cell size. However, the extent of their adaptability in constructing honeycombs with varied cell sizes has not been thoroughly investigated. We use 3D-printing and X-ray microscopy to quantify honeybees’ capacity in adjusting the comb to different initial conditions. Our findings suggest three distinct comb construction modes in response to foundations with varying sizes of 3D-printed cells. For smaller foundations, bees occasionally merge adjacent cells to compensate for the reduced space. However, for larger cell sizes, the hive uses adaptive strategies such as tilting for foundations with cells up to twice the reference size and layering for cells that are three times larger than the reference cell. Our findings shed light on honeybees adaptive comb construction abilities, significant for the biology of self-organized collective behavior, as well as for bio-inspired engineered systems. Gharooni-Fard G, Kavaraganahalli Prasanna C, Peleg O, López Jiménez F (2025) Honeybees adapt to a range of comb cell sizes by merging, tilting, and layering their construction. PLoS Biol 23(8): e3003253. Read the full article at: journals.plos.org

Andrew W. Corcoran, Andrew M. Haun, Reinder Dorman, Giulio Tononi, Karl J. Friston, Cyriel M. A. Pennartz, TWCF: INTREPID Consortium As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences -- as well as their predictive and explanatory power -- becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Information Theory, Neurorepresentationalism, and Active Inference. We begin by presenting the core claims of each theory, before comparing them in terms of (1) the phenomena they seek to explain, (2) the sorts of explanations they avail, and (3) the methodological strategies they endorse. We then consider some of the inherent challenges of theory testing, and how adversarial collaboration addresses some of these difficulties. More specifically, we outline the key hypotheses that will be tested in this adversarial collaboration, and exemplify how contrasting empirical predictions may pertain to core and auxiliary components of each theory. Finally, we discuss how the data harvested across disparate experiments (and their replicates) may be formally integrated to provide a quantitative measure of the evidential support accrued under each theory. We suggest this approach to theory comparison may afford a useful metric for tracking the amount of scientific progress being made in consciousness research. Read the full article at: arxiv.org

The Spanish Society of Complex Systems (CS³ Spain) was founded in 2022, during the International Conference on Complex Systems held in Palma de Mallorca, with the aim of strengthening the community of complex systems researchers in our country. Since then, the Spanish Chapter has held three meetings: in Santander (2023), in Barcelona (2024) and in Madrid (2025), consolidating itself as a reference space for the exchange of ideas and interdisciplinary collaboration. The 4th Meeting of the Spanish Society of Complex Systems will take place in Seville, from January 21 to 23, 2026, at the emblematic Uruguay Pavilion, headquarters of the University of Seville. More at: cs3.es

In cellular automata, simple rules create elaborate structures. Now researchers can start with the structures and reverse-engineer the rules. Read the full article at: www.quantamagazine.org

Engineering Swarms for Cyber-Physical Systems covers the whole design cycle for applying swarm intelligence in Cyber-Physical Systems (CPS) and guides readers through modeling, design, simulation, and final deployment of swarm systems. The book provides a one-stop-shop covering all relevant aspects for engineering swarm systems. Following a concise introduction part on swarm intelligence and the potential of swarm systems, the book explains modeling methods for swarm systems embodied in the interplay of physical swarm agents. Examples from several domains including robotics, manufacturing, and search and rescue applications are given. In addition, swarm robotics is further covered by an analysis of available platforms, computation models and applications. It also treats design methods for cyber-physical swarm applications including swarm modeling approaches for CPSs and classical implementations of behaviors as well as approaches based on machine-learning. A chapter on simulation covers simulation requirements and addresses the dichotomy between abstract and detailed physical simulation models. A special feature of the chapters is the hands-on character by providing programming examples with the different engineering aspects whenever possible, thus allowing for fast translation of concepts to actual implementation. Overall, the book is meant to give a creative researcher or engineer the inspiration, theoretical background, and practical knowledge to build swarm systems of CPSs. It also serves as a text for students in science and engineering. Read the full article at: www.routledge.com

https://www.youtube.com/live/uhK9ElOZ7n4Venki Ramakrishnan 30th Ulam Lecture Night 2 The knowledge of aging and death has driven human culture, including our religions, ever since we became aware of our mortality. For much of our existence there was not much we could do about it. But over the past few decades, biology has made major advances in our understanding of the causes of aging, opening for the first time the possibility of intervening in the process. At the same time, the combination of longer lives and reduced fertility rates means that many societies are faced with an aging population. This has led to large investments in aging research from governments and private industry funded largely by tech billionaires, resulting in both real advances and a large amount of hype. In this talk, Venki Ramakrishnan will discuss some of the key findings about why and how we age and die and prospects for the future. He will also explore the possible consequences of societies with extremely long-lived populations. Watch at: www.youtube.com

https://www.youtube.com/live/xJ5BFBbakSkVenki Ramakrishnan 30th Ulam Lecture Night 1 Ramakrishnan will provide a history of molecular visualization, as well as take us through his work at the MRC Laboratory of Molecular Biology in Cambridge, England, where his team determined the atomic structure of the 30S ribosomal subunit and its complexes with ligands and antibiotics. Everyone is familiar with DNA, but by itself, DNA is just an inert blueprint for life. It is the ribosome — an enormous molecular machine made up of a million atoms — that makes DNA come to life, turning our genetic code into proteins and therefore into us. He will talk about the ribosome (the "Gene Machine"), and how his team learned about its structure. He will also share some recent developments, including the development of cryoEM — a powerful technique used to determine the structure of three-dimensional structure of biological molecules at near-atomic resolution. Watch at: www.youtube.com

The Evolving Landscape of Complex Systems is a curated special collection in npj Complexity inspired by themes explored at the Conference on Complex Systems 2025 (CCS25). This collection consolidates emerging advances in theory, methodologies, and applications across the multifaceted area of complexity science. It seeks contributions that span the full spectrum - from novel computational frameworks and multiscale analyses to domain-adaptive models and novel complexity science applications - reflecting the discipline’s rapid evolution. This collection invites novel research that explores: Conceptual foundations and theory: advancements in network science, emergent dynamics, agent-based modelling, nonlinear systems, and adaptive behaviours, providing refined lenses for interpreting complex phenomena. Cross-scale integration and robustness: studies elucidating how micro-level interactions scale up to macro-level patterns, resilience, and adaptation in systems spanning biological, social, technological, and ecological networks. Computational innovation: cutting-edge analytical and computational methods - ranging from data-driven approaches and AI-augmented modelling to novel simulations and multilevel inference - that enhance the understanding and manipulation of complex systems. Interdisciplinary and application-oriented research: compelling case studies where complexity science addresses urgent global challenges - such as pandemics, misinformation, climate change, socioeconomic inequality, inclusivity and diversity, and governance - demonstrating adaptability and societal relevance. Submissions are welcomed from all researchers working in complexity science, regardless of conference participation. More at: www.nature.com