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.

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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

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

Alexander F. Siegenfeld and Yaneer Bar-Yam

Entropy 2025, 27(8), 835

Ashby’s law of requisite variety allows a comparison of systems with their environments, providing a necessary (but not sufficient) condition for system efficacy: A system must possess at least as much complexity as any set of environmental behaviors that require distinct responses from the system. However, to account for the dependence of a system’s complexity on the level of detail—or scale—of its description, a multi-scale generalization of Ashby’s law is needed. We define a class of complexity profiles (complexity as a function of scale) that is the first, to our knowledge, to exhibit a multi-scale law of requisite variety. This formalism provides a characterization of multi-scale complexity and generalizes the law of requisite variety’s single constraint on system behaviors to a class of multi-scale constraints. We show that these complexity profiles satisfy a sum rule, which reflects a tradeoff between smaller- and larger-scale degrees of freedom, and we extend our results to subdivided systems and systems with a continuum of components.

Read the full article at: www.mdpi.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

George Blackburne, Alberto Liardi, Jeremy I Skipper, Pedro A. M. Mediano, and Fernando Rosas

The relationship between subjectivity and its substrate can be understood via the notion of compatibilist emergence, accounting for the plurality of ways nature can host new dynamical laws at higher scales. We discuss how the autopoietic character of life may require and promote the kind of emergence necessary, albeit not sufficient, for consciousness.

Read the full article at: osf.io

Giorgio Tripodi, Xiang Zheng, Yifan Qian, Dakota Murray, Benjamin F. Jones, Chaoqun Ni, and Dashun Wang

PNAS 122 (30) e2500322122

Tenure is a defining feature of the US academic system with significant implications for research productivity and creative search. Yet the impact of tenure on faculty research trajectories remains poorly understood. We analyze the careers of 12,000 US faculty across 15 disciplines to reveal key patterns, pre- and post-tenure. Publication rates rise sharply during the tenure-track, peaking just before tenure. However, post-tenure trajectories diverge: Researchers in lab-based fields sustain high output, while those in non-lab-based fields typically exhibit a decline. After tenure, faculty produce more novel works, though fewer highly cited papers. These findings highlight tenure’s pivotal role in shaping scientific careers, offering insights into the interplay between academic incentives, creativity, and impact while informing debates about the academic system.

Read the full article at: www.pnas.org

Will Deter, Hiroki Sayama

Complexity

Agent-based models of residential segregation have been of persistent interest to various research communities since their origin with James Sakoda and popularization by Thomas Schelling. Frequently, these models have sought to elucidate the extent to which the collective dynamics of individual preferences may cause segregation to emerge. This open question has sustained relevance in U.S. jurisprudence. Previous investigation that incorporated heterogeneity of behaviors (preferences) showed reductions in segregation. Meanwhile, previous investigation that incorporated heterogeneity of social network topologies showed no significant impact to observed segregation levels. In the present study, we examined the effects of the concurrent presence of both behavioral and topological heterogeneities in network segregation models. Simulations were conducted using both homogeneous and heterogeneous preference models on 2D lattices with varied levels of densification to create topological heterogeneities (i.e., clusters and hubs). Results show a richer variety of outcomes, including novel differences in resultant segregation levels and hub composition. Notably, with concurrent increased representations of heterogeneous preferences and heterogeneous topologies, reduced levels of segregation emerge. Simultaneously, we observe a novel dynamic of segregation between tolerance levels as highly tolerant nodes take residence in dense areas and push intolerant nodes to sparse areas mimicking the urban–rural divide.

Read the full article at: onlinelibrary.wiley.com

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.

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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.

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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.

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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

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.

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Samson Abramsky, Wolfgang Banzhaf, Leo S. D. Caves, Michael Levin, Penousal Machado, Charles Ofria, Susan Stepney, Roger White

Living systems exhibit a range of fundamental characteristics: they are active, self-referential, self-modifying systems. This paper explores how these characteristics create challenges for conventional scientific approaches and why they require new theoretical and formal frameworks. We introduce a distinction between 'natural time', the continuing present of physical processes, and 'representational time', with its framework of past, present and future that emerges with life itself. Representational time enables memory, learning and prediction, functions of living systems essential for their survival. Through examples from evolution, embryogenesis and metamorphosis we show how living systems navigate the apparent contradictions arising from self-reference as natural time unwinds self-referential loops into developmental spirals. Conventional mathematical and computational formalisms struggle to model self-referential and self-modifying systems without running into paradox. We identify promising new directions for modelling self-referential systems, including domain theory, co-algebra, genetic programming, and self-modifying algorithms. There are broad implications for biology, cognitive science and social sciences, because self-reference and self-modification are not problems to be avoided but core features of living systems that must be modelled to understand life's open-ended creativity.

Read the full article at: arxiv.org

Julia Schüler, Maik Bieleke, Matti T. J. Heino, Natalia Balague Serre, Angel Chater, Markus Gruber, Martina Kanning, Daniel A. Keim, Daniela Mier, Maria Moreno-Villanueva, Fridtjof Nussbeck, Jens C. Pruessner, Termeh Shafie, and Michael Schwenk

The rising of physical inactivity and its associated health and economic burdens persist de-spite decades of interdisciplinary research aimed at promoting physical activity (PA). This Per-spective takes a complex systems view on PA, proposing that at least two layers of complexity should be taken into account: 1) interactions between various physiological, psychological, social, and environmental systems and 2) their dynamic interactions across time. To address this complexity, all stages of the research process—from theory and measurement to study design, analysis, and interventions—must be aligned with a complex systems perspective. This alignment requires intensive interdisciplinary collaboration and an integration of basic and applied research beyond current research practices to create transdisciplinary solutions. We offer actionable insights that bridge the gap between abstract theoretical approaches (e.g., complex systems and attractor landscape frameworks of behavior change) and practical PA research, thereby laying a foundation for more effective behavior change interventions.

Read the full article at: osf.io