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.

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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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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.

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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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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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