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.

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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.

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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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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.

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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

Anna Mancini, Balázs Lengyel, Riccardo Di Clemente, Giulio Cimini

Recent crises like the COVID-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted increasing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here we study dynamics and determinants of firm-level systemic risk in the Hungarian production network from 2015 to 2022. We use as benchmark a heuristic maximum entropy null model that generates an ensemble of production networks at equilibrium, by preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during COVID-19, as those enabling economic exchanges become key players in the economy -- a result which is not reproduced by the null model. Although the empirical systemic risk aligns well with the null value until the onset of the pandemic, it becomes significantly smaller afterwards as the adaptive behavior of firms leads to a more resilient economy. Furthermore, firms' international trade volume (being a subject of disruption) becomes a significant predictor of their systemic risk. However, international links cannot provide an unequivocal explanation for the observed trends, as imports and exports have opposing effects on local systemic risk through the supply and demand channels.

Read the full article at: arxiv.org

Dirk Helbing & Sachit Mahajan 

We critically examine the evolving functionality and challenges of democracies in the age of digital transformation and artificial intelligence (AI). Contrary to notions of democracy as a static governance form, we emphasize the importance of its adaptability, but find that recent technological and institutional shifts have undermined foundational mechanisms such as decentralized decision-making, transparent information flows, and effective self-correction. Drawing from complexity science, political theory, participatory research and computational social science, we analyze how algorithmic control, surveillance capitalism, and power asymmetries have affected core democratic principles. We pay specific attention to structural changes in political representation, civic participation, and how these have affected public trust. We further discuss a set of recent, digitally assisted approaches, ranging from deliberative platforms and participatory budgeting to fair voting systems and co-creation, which can potentially restore the legitimacy of democratic systems and their resilience. By understanding democracies as dynamic, co-evolving systems, we highlight the potential of plu-ralistic design. Aligning technological progress with constitutional principles can meaningfully repair, revive and updgrade democratic systems and institutions.

Read the full article at: www.researchgate.net

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

Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Bert Wang-Chak Chan, Pierre-Yves Oudeyer, Clément Moulin-Frier

Artificial Life (2025) 31 (2): 228–248.

Central to the Artificial Life endeavor is the creation of artificial systems that spontaneously generate properties found in the living world, such as autopoiesis, self-replication, evolution, and open-endedness. Though numerous models and paradigms have been proposed, cellular automata (CA) have taken a very important place in the field, notably because they enable the study of phenomena like self-reproduction and autopoiesis. Continuous CA like Lenia have been shown to produce lifelike patterns reminiscent, from both aesthetic and ontological points of view, of biological organisms we call “creatures.” We propose Flow-Lenia, a mass conservative extension of Lenia. We present experiments demonstrating its effectiveness in generating spatially localized patterns with complex behaviors and show that the update rule parameters can be optimized to generate complex creatures showing behaviors of interest. Furthermore, we show that Flow-Lenia allows us to embed the parameters of the model, defining the properties of the emerging patterns, within its own dynamics, thus allowing for multispecies simulation. Using the evolutionary activity framework and other metrics, we shed light on the emergent evolutionary dynamics taking place in this system.

Read the full article at: direct.mit.edu

Andrea Santoro, Matteo Neri, Simone Poetto, Davide Orsenigo, Matteo Diano, Marilyn Gatica, Giovanni Petri

Traditional models of brain connectivity have primarily focused on pairwise interactions, over-looking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction (HOI) metrics have been proposed, a systematic evaluation of their comparative properties and utility is missing. Here, we present the first large-scale analysis of information-theoretic and topological HOI metrics, applied to both resting-state and task fMRI data from 100 unrelated subjects of the Human Connectome Project. We identify a clear taxonomy of HOI metrics — redundant, synergistic, and topological—, with the latter acting as bridges along the redundancy–synergy continuum. Despite methodological differences, all HOI metrics align with the brain’s overarching unimodal-to-transmodal functional hierarchy. However, certain metrics show specific associations with the neurotransmitter receptor architecture. HOI metrics outperform traditional pairwise models in brain fingerprinting and perform comparably in task decoding, underscoring their value for characterizing individual functional profiles. Finally, multivariate analysis reveals that — among all HOI metrics — topological descriptors are key to linking brain function with behavioral variability, positioning them as valuable tools for linking neural architecture and cognitive function. Overall, our findings establish HOIs as a powerful framework for capturing the brain’s multidimensional dynamics, providing a conceptual map to guide their application across cognitive and clinical neuroscience.

Read the full article at: www.biorxiv.org

Arianna Salazar-Miranda, Zhuangyuan Fan, Michael Baick, Keith N. Hampton, Fabio Duarte, Becky P. Y. Loo, Edward Glaeser, and Carlo Ratti

PNAS 122 (30) e2424662122

Urban public spaces have traditionally served as places for gathering and social connection, shaping the social fabric of cities. This study reveals important shifts in pedestrian behaviors over a 30-y period in four US public spaces. By using AI and computer vision to analyze historical and contemporary video footage, we observe an increase in walking speed and a decrease in time spent lingering, along with fewer group encounters. This trend suggests a growing perception of city streets as corridors for movement rather than spaces for social interaction. These findings highlight a changing urban dynamic, where efficiency increasingly shapes public space usage, potentially impacting social connections and the community-building role of these environments.

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Felix Effenberger, Pedro Carvalho, Igor Dubinin, and Wolf Singer

PNAS 122 (4) e2412830122

Neocortical circuits are characterized by complex oscillatory dynamics. Whether these oscillations serve computations or are an epiphenomenon is still debated. To answer this question, we designed a computational model of a recurrent network that allows control of oscillatory dynamics (harmonic oscillator recurrent network, HORN). When operating in an oscillatory regime, HORNs outperform nonoscillatory recurrent networks in terms of learning speed, noise tolerance, and parameter efficiency. Moreover, they closely replicate the dynamics of neuronal systems, suggesting that biological neural networks are likely to also exploit the unique properties offered by oscillatory dynamics for computing. The interference patterns provided by wave-based responses allow for a holistic representation and highly parallel encoding of both spatial and temporal relations among stimulus features.

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