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Complexity Digest

@cxdig.bsky.social

Networking the complexity community since 1999. Official news channel of the @cssociety.bsky.social Edited by @cgershen.bsky.social

203 Followers  |  137 Following  |  88 Posts  |  Joined: 25.02.2025  |  2.0803

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The Theory of Economic Complexity César A. Hidalgo, Viktor Stojkoski Economic complexity estimates rely on eigenvectors derived from matrices of specialization to explain differences in economic growth, inequality, and sustainability. Yet, despite their widespread use, we still lack a principled theory that can deduce these eigenvectors from first principles and place them in the context of a mechanistic model. Here, we calculate these eigenvectors analytically for a model where the output of an economy in an activity increases with the probability the economy is endowed with the factors required by the activity. We show that the eigenvector known as the Economic Complexity Index or ECI is a monotonic function of the probability that an economy is endowed with a factor, and that in a multi-factor model, it is an estimate of the average endowment across all factors. We then generalize this result to other production functions and to a short-run equilibrium framework with prices, wages, and consumption. We find that our main result does not depend on the introduction of prices or wages, and that the derived wage function is consistent with the convergence of economies with a similar level of complexity. Finally, we use this model to explain the shape of networks of related activities, such as the product space and the research space. These findings solve long standing theoretical puzzles in the economic complexity literature and validate the idea that metrics of economic complexity are estimates of an economy being endowed with multiple factors. Read the full article at: arxiv.org

The Theory of Economic Complexity

27.07.2025 13:46 — 👍 2    🔁 2    💬 0    📌 0
Revisiting Big Data Optimism: Risks of Data-Driven Black Box Algorithms for Society Sachit Mahajan, Dirk Helbing This paper critically examines the growing use of big data algorithms and AI in science, society, and public policy. While these tools are often introduced with the goal of increasing efficiency, the results do not always lead to greater empowerment or fairness for individuals or communities. Persistent issues such as bias, measurement error, and over-reliance on prediction can undermine success and produce outcomes that are neither fair nor transparent, especially when automated decisions replace human judgment. Beyond technical limitations, the widespread use of data-driven methods also shapes the distribution of power, influences public trust, and raises questions about the health of techno-socioeconomic institutions. We argue that the pursuit of optimality cannot succeed without careful evaluation of ethical risks and societal side effects. Responsible innovation demands open standards, ongoing scrutiny, and a focus on human values alongside technical performance. Our goal is to encourage a more balanced approach to big data-one that recognizes both its potentials and its limits, and one that aims for genuine social benefits rather than just efficiency alone. Read the full article at: www.researchgate.net

Revisiting Big Data Optimism: Risks of Data-Driven Black Box Algorithms for Society

26.07.2025 13:27 — 👍 1    🔁 1    💬 0    📌 0
Peer Review and the Diffusion of Ideas Binglu Wang, Zhengnan Ma, Dashun Wang, Brian Uzzi This study examines a fundamental yet overlooked function of peer review: its role in exposing reviewers to new and unexpected ideas. Leveraging a natural experiment involving over half a million peer review invitations covering both accepted and rejected manuscripts, and integrating high-scale bibliographic and editorial records for 37,279 submitting authors, we find that exposure to a manuscript's core ideas significantly influences the future referencing behavior and knowledge of reviewer invitees who decline the review invite. Specifically, declining reviewer invitees who could view concise summaries of the manuscript's core ideas not only increase their citations to the manuscript itself but also demonstrate expanded breadth, depth, diversity, and prominence of citations to the submitting author's broader body of work. Overall, these results suggest peer review substantially influences the spread of scientific knowledge. Ironically, while the massive scale of peer review, entailing millions of reviews annually, often drives policy debates about its costs and burdens, our findings demonstrate that precisely because of this scale, peer review serves as a powerful yet previously unrecognized engine for idea diffusion, which is central to scientific advances and scholarly communication. Read the full article at: arxiv.org

Peer Review and the Diffusion of Ideas

25.07.2025 12:16 — 👍 0    🔁 0    💬 0    📌 0
Participatory Evolution of Artificial Life Systems via Semantic Feedback Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system's potential as a platform for participatory generative design and open-ended evolution. Read the full article at: arxiv.org

Participatory Evolution of Artificial Life Systems via Semantic Feedback

24.07.2025 16:26 — 👍 2    🔁 1    💬 0    📌 0
]Ranking dynamics in movies and music Hyun-Woo Lee, Gerardo Iñiguez, Hang-Hyun Jo, Hye Jin Park Ranking systems are widely used to simplify and interpret complex data across diverse domains, from economic indicators and sports scores to online content popularity. While previous studies including the Zipf's law have focused on the static, aggregated properties of ranks, in recent years researchers have begun to uncover generic features in their temporal dynamics. In this work, we introduce and study a series of system-level indices that quantify the compositional changes in ranking lists over time, and also characterize the temporal ranking trajectories of individual items' ranking dynamics. We apply our method to analyze ranking dynamics of movies from the over-the-top services, including Netflix, as well as that of music items in Spotify charts. We find that newly released movies or music items influence most the system-level compositional changes of ranking lists; the highest ranks of items are strongly correlated with their lifetimes in the lists more than their first and last ranks. Our findings offer a novel lens to understand collective ranking dynamics and provide a basis for comparing fluctuation patterns across various ordered systems. Read the full article at: arxiv.org

Ranking dynamics in movies and music

24.07.2025 13:43 — 👍 0    🔁 0    💬 0    📌 0
Self‐Reconfiguring Modular Robotic Boats Wei Wang, Niklas Hagemann,  Alejandro Gonzalez-Garcia,  Carlo Ratti, Daniela Rus Self-reconfigurable aquatic robots offer promising potential for a wide range of marine applica-tions, including building temporary infrastructure, environmental monitoring, and on-demand transportation. However, achieving autonomous water-based self-reconfiguration, even in two di-mensions on the water surface, remains challenging, due to complex nonlinear hydrodynamics, disturbances from self-motion and neighboring robots, as well as external environmental factors. Here, we present the FloatForm platform, a group of miniature modular robotic boats, capable of self-assembling into physically connected structures, self-reconfiguring, and collectively traveling as larger assemblies via a hybrid coordination framework. Each robot unit is equipped with onboard sensing, motion control, and the ability to coordinate and physically latch with its neigh-bors. We demonstrate the feasibility of parallel self-reconfiguration, where distributed controllers on each robot handle coordination tasks such as aggregating into desired shapes and avoiding col-lisions, while a minimalist central planner oversees the overall success of each task and fixes im-perfections. This work advances the design, control, and coordination of modular robotic systems in aquatic environments, paving the way for flexible, robust and scalable applications on the water. Read the full article at: www.researchsquare.com

Self‐Reconfiguring Modular Robotic Boats

24.07.2025 12:12 — 👍 0    🔁 0    💬 0    📌 0
Communication patterns affect the collective performance of social agents Sandro M. Reia, Dieter Pfoser & Paulo R. A. Campos The European Physical Journal B Volume 98, article number 149, (2025) More often than not, we work in group settings where the communication structure within and between groups governs the flow of information among individuals. This structure can be designed to optimize group performance, enabling individuals to solve tasks in the shortest time or achieve the highest reward. In this paper, we explore the effects of communication patterns on the collective performance of a group of interacting agents. The agents are tasked with performing an action, where the reward depends on their skill in executing that action. At any given time, an agent switching actions has two choices: to learn from the best-performing connected agent (with probability q), or to randomly explore the action space (with probability ). Our findings indicate that decentralized networks enhance collective performance by increasing both the overall group reward and the maximum reward achieved by an individual. Conversely, in more centralized and hierarchical networks, we observe that better connected agents, as reflected by their betweenness centrality, exhibit better performance. Read the full article at: link.springer.com

Communication patterns affect the collective performance of social agents

24.07.2025 11:39 — 👍 0    🔁 0    💬 0    📌 0
Energy and Information Klaus JaffeEnergy and Information The literature contains many contradictory conceptual descriptions of therelation between Energy and Information. Here I argue that Information is not energy.Information is a representation or description of spatiotemporal arrangements (order)of matter and energy, encoded onto a physical substrate. Known substrates includeelectromagnetic waves, material structures, chemical molecules, and neural networks—whether in brains or computers. In quantum mechanics, when information pertainsto elementary particles, the object of study and the substrate encoding the informationcoincide. This leads to view energy and information as the same phenomenon, leadingto counterintuitive and often perplexing interpretations of reality. The proposeddefinition distinguishes between thermodynamic entropy and information entropy,enabling a consilient bridge between quantum and classical mechanics, geneticinformation, human knowledge, personal models of the world, consciousness andempathy. It facilitates the study of different kinds of information—especially the kindthat generates free energy and enables useful work, as studied by infodynamics. Thecentral insight is that energy and information are distinct, irreducible properties of theuniverse. Understanding their interplay requires considering four foundationalelements: spacetime, matter, energy, and information. These clarifications arefundamental for research in natural and artificial intelligence Read the full article at: www.researchgate.net

Energy and Information

23.07.2025 12:09 — 👍 1    🔁 0    💬 0    📌 0
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Diffusion of complex contagions is shaped by a trade-off between reach and reinforcement Allison Wan, Christoph Riedl, and David Lazer PNAS 122 (28) e2422892122 How does social network structure amplify or stifle behavior diffusion? Existing theory suggests that when social reinforcement makes the adoption of behavior more likely, it should spread more—both farther and faster—on clustered networks with redundant ties. Conversely, if adoption does not benefit from social reinforcement, it should spread more on random networks which avoid such redundancies. We develop a model of behavior diffusion with tunable probabilistic adoption and social reinforcement parameters to systematically evaluate the conditions under which clustered networks spread behavior better than random networks. Using simulations and analytical methods, we identify precise boundaries in the parameter space where one network type outperforms the other or they perform equally. We find that, in most cases, random networks spread behavior as far or farther than clustered networks, even when social reinforcement increases adoption. Although we find that probabilistic, socially reinforced behaviors can spread farther on clustered networks in some cases, this is not the dominant pattern. Clustered networks are even less advantageous when individuals remain influential for longer after adopting, have more neighbors, or need more neighbors before social reinforcement takes effect. Under such conditions, clustering tends to help only when adoption is nearly deterministic, which is not representative of socially reinforced behaviors more generally. Clustered networks outperform random networks by a 5% margin in only 22% of the parameter space under its most favorable conditions. This pattern reflects a fundamental trade-off: Random ties enhance reach, while clustered ties enhance social reinforcement. https://www.pnas.org/doi/abs/10.1073/pnas.2422892122

Diffusion of complex contagions is shaped by a trade-off between reach and reinforcement

23.07.2025 09:12 — 👍 1    🔁 0    💬 0    📌 0
Participatory Evolution of Artificial Life Systems via Semantic Feedback Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system's potential as a platform for participatory generative design and open-ended evolution. Read the full article at: arxiv.org

Participatory Evolution of Artificial Life Systems via Semantic Feedback

22.07.2025 17:17 — 👍 2    🔁 1    💬 0    📌 0
A critical phase transition in bee movement dynamics can be modeled using a 2D cellular automata Ivan Shpurov, Tom Froese The collective behavior of numerous animal species, including insects, exhibits scale-free behavior indicative of the critical (second-order) phase transition. Previous research uncovered such phenomena in the behavior of honeybees, most notably the long-range correlations in space and time. Furthermore, it was demonstrated that the bee activity in the hive manifests the hallmarks of the jamming process. We follow up by presenting a discrete model of the system that faithfully replicates some of the key features found in the data - such as the divergence of correlation length and scale-free distribution of jammed clusters. The dependence of the correlation length on the control parameter - density is demonstrated for both the real data and the model. We conclude with a brief discussion on the contribution of the insights provided by the model to our understanding of the insects' collective behavior. Read the full article at: arxiv.org

A critical phase transition in bee movement dynamics can be modeled using a 2D cellular automata

22.07.2025 12:07 — 👍 1    🔁 0    💬 0    📌 0
An AI tool for scafolding complex thinking: challenges and solutions in developing an LLM prompt protocol suite This paper reports an exploratory study examining the interaction between a theoretical framework for Complex Thinking and AI (LLMs), in terms of its potentialities and constraints. The aim was to develop and conduct a preliminary pilot evaluation of a tool comprising a prompt protocol suite for use with an LLM, to scafold Complex Thinking. The tool is designed for use by an individual or group in relation to a given Target System of Interest (i.e., a real-world system, a problem, or a concern), supporting the development of more complex understandings of such systems that can guide more efective and positive actions and decisions. We describe the process of developing a suite of prompt protocols for scafolding particular properties of Complex Thinking and report on the outcomes of a pilot test evaluation with a set of users across diferent domains. Melo, A. T., Renault, L., Caves, L., Garnett, P., Lopes, P. D., Ribeiro, R., & Santos, F. (2025). An AI tool for scaffolding Complex Thinking: Challenges and solutions in developing an LLM prompt protocol suite. Cognition, Technology & Work. https://doi.org/10.1007/s10111-025-00817-6 

An AI tool for scafolding complex thinking: challenges and solutions in developing an LLM prompt protocol suite

19.07.2025 14:09 — 👍 2    🔁 0    💬 0    📌 0
Classifying Emergence in Robot Swarms: An Observer-Dependent Approach Ricardo Vega, Cameron Nowzari Emergence and swarms are widely discussed topics, yet no consensus exists on their formal definitions. This lack of agreement makes it difficult not only for new researchers to grasp these concepts, but also for experts who may use the same terms to mean different things. Many attempts have been made to objectively define 'swarm' or 'emergence,' with recent work highlighting the role of the external observer. Still, several researchers argue that once an observer's vantage point (e.g., scope, resolution, context) is established, the terms can be made objective or measured quantitatively. In this note, we propose a framework to discuss these ideas rigorously by separating externally observable states from latent, unobservable ones. This allows us to compare and contrast existing definitions of swarms and emergence on common ground. We argue that these concepts are ultimately subjective-shaped less by the system itself than by the perception and tacit knowledge of the observer. Specifically, we suggest that a 'swarm' is not defined by its group behavior alone, but by the process generating that behavior. Our broader goal is to support the design and deployment of robotic swarm systems, highlighting the critical distinction between multi-robot systems and true swarms. Read the full article at: arxiv.org

Classifying Emergence in Robot Swarms: An Observer-Dependent Approach

19.07.2025 11:52 — 👍 0    🔁 0    💬 0    📌 0
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The innovation trade-off: how following superstars shapes academic novelty Sean Kelty, Raiyan Abdul Baten, Adiba Mahbub Proma, Ehsan Hoque, Johan Bollen & Gourab Ghoshal Humanities and Social Sciences Communications volume 12, Article number: 926 (2025) Academic success is distributed unequally; a few top scientists receive the bulk of attention, citations, and resources. However, do these “superstars” foster leadership in scientific innovation? We employ a series of information-theoretic measures that quantify novelty, innovation, and impact from scholarly citation networks, and compare the academic output of scientists in the American Physical Society corpus with varying levels of connections to superstar scientists. The strength of connection is based on the frequency of citations to superstar papers, which is also related to the frequency of collaboration. We find that while strongly-connected scientists publish more, garner more citations, and produce moderately more diverse content, this comes at a cost of lower innovation, less disruption, and higher redundancy of ideas. Further, once one removes papers co-authored with superstars, the academic output of these strongly connected scientists greatly diminishes. In contrast, authors who publish at the same rate without the benefit of collaborations with scientific superstars produce papers that are more innovative, more disruptive, and have comparable citation rates, once one controls for the transferred prestige of superstars. On balance, our results indicate that academia pays a price by focusing attention and resources on superstars. Read the full article at: www.nature.com

The innovation trade-off: how following superstars shapes academic novelty

19.07.2025 10:07 — 👍 1    🔁 2    💬 0    📌 0
Life Finds A Way: Emergence of Cooperative Structures in Adaptive Threshold Networks Sean P. Maley, Carlos Gershenson, Stuart A. Kauffman There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution of life. Using a simple model, we investigate how varying bias toward cooperation versus antagonism shapes network dynamics, revealing that higher-order organization emerges even amid pervasive antagonistic interactions. In general, we observe that a quantitative increase in the number of elements in a system leads to a qualitative transition. We present a random threshold-directed network model that integrates node-specific traits with dynamic edge formation and node removal, simulating arbitrary levels of cooperation and competition. In our framework, intrinsic node values determine directed links through various threshold rules. Our model generates a multi-digraph with signed edges (reflecting support/antagonism, labeled ``help''/``harm''), which ultimately yields two parallel yet interdependent threshold graphs. Incorporating temporal growth and node turnover in our approach allows exploration of the evolution, adaptation, and potential collapse of communities and reveals phase transitions in both connectivity and resilience. Our findings extend classical random threshold and Erdős-Rényi models, offering new insights into adaptive systems in biological and economic contexts, with emphasis on the application to Collective Affordance Sets. This framework should also be useful for making predictions that will be tested by ongoing experiments of microbial communities in soil. Read the full article at: arxiv.org

Life Finds A Way: Emergence of Cooperative Structures in Adaptive Threshold Networks

19.07.2025 05:35 — 👍 1    🔁 0    💬 0    📌 0
Tendencies toward triadic closure: Field experimental evidence Mohsen Mosleh,, Dean Eckles, and David G. Rand PNAS 122 (27) e2404590122 Empirical social networks are characterized by a high degree of triadic closure (i.e., transitivity, clustering): network neighbors of the same individual are also likely to be directly connected. It is unknown to what degree this results from dispositions to form such ties (i.e., to close open triangles) per se versus other processes such as homophily and more opportunities for exposure. These mechanisms are difficult to disentangle in many settings. On social media, however, they can be decomposed - and platforms frequently make decisions that depend on these distinct processes. Here, using a field experiment on social media, we randomize the existing network structure that a user faces when they are followed by a target account that we control. We then examine whether the user reciprocates this tie formation. Being randomly assigned to have an existing tie to an account that follows the target user increases tie formation by 35%. Through multiple control conditions, we attribute this effect specifically to a minimal cue that indicates the presence of a potential mutual follower. Theory suggests that triadic closure should be especially likely in open triads of strong ties, and accordingly we find larger effects when the subject has interacted more with the existing follower. These results indicate a substantial role for tendencies toward triadic closure, but one that is substantially smaller than what might be inferred from prior observational studies. Platforms and others may rely on these tendencies in encouraging tie formation, with broader implications for network structure and information diffusion in online networks Read the full article at: www.pnas.org

Tendencies toward triadic closure: Field experimental evidence

18.07.2025 14:07 — 👍 0    🔁 0    💬 0    📌 0
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Applied Antifragility in Natural Systems: From Principles to Applications Cristian Axenie , Roman Bauer , Oliver López Corona , Jeffrey West As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. Such principles are already well suited for describing behaviors in natural systems but also in approaching therapy designs and eco-system modelling and eco-system analysis. The purpose of this book is to build a foundational knowledge base by applying antifragile system design, analysis, and development in natural systems, including biomedicine, neuroscience, and ecology as main fields. We are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop systems that behave beyond robust in the face of uncertainty when characterizing and intervening in biomedical and ecological (eco)systems. The book introduces the framework of applied antifragility and possible paths to build systems that gain from uncertainty. We draw from the body of literature on natural systems (e.g. cancer therapy, antibiotics, neuroscience, and agricultural pest management) in an attempt to unify the scales of antifragility in one framework. The work of the Applied Antifragility Group in oncology, neuroscience, and ecology led by the authors provides a good overview on the current research status. Read the full article at: link.springer.com

Applied Antifragility in Natural Systems: From Principles to Applications

17.07.2025 15:59 — 👍 0    🔁 0    💬 0    📌 0
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Applied Antifragility in Technical Systems: From Principles to Applications Cristian Axenie , Meisam Akbarzadeh , Michail A. Makridis , Matteo Saveriano , Alexandru Stancu The book purpose is to build a foundational knowledge base by applying antifragile system design, analysis, and development in technical systems, with a focus on traffic engineering, robotics, and control engineering. The authors are interested in formalizing principles and an apparatus that turns the basic concept of antifragility into a tool for designing and building closed-loop technical systems that behave beyond robust in the face of uncertainty. As coined in the book of Nassim Taleb, antifragility is a property of a system to gain from uncertainty, randomness, and volatility, opposite to what fragility would incur. An antifragile system’s response to external perturbations is beyond robust, such that small stressors can strengthen the future response of the system by adding a strong anticipation component. The work of the Applied Antifragility Group in traffic control and robotics, led by the authors, provides a good overview on the current research status. Read the full article at: link.springer.com

Applied Antifragility in Technical Systems: From Principles to Applications

17.07.2025 13:57 — 👍 0    🔁 0    💬 0    📌 0
Top rank statistics for Brownian reshuffling Zdzislaw Burda, Mario Kieburg Phys. Rev. E 112, 014114 We study the dynamical aspects of the top rank statistics of particles, performing Brownian motions on a half-line, which are ranked by their distance from the origin. For this purpose, we introduce an observable Ω⁡(𝑡) which we call the overlap ratio. The average overlap ratio is equal to the probability that a particle that is on the top-𝑛 list at some time will also be on the top-𝑛 list after time 𝑡. The overlap ratio is a local observable which is concentrated at the top of the ranking and does not require the full ranking of all particles. In practice, the overlap ratio is easy to measure. We derive an analytical formula for the average overlap ratio for a system of 𝑁 particles in the stationary state that undergo independent Brownian motion on the positive real half-axis with a reflecting wall at the origin and a drift towards the wall. In particular, we show that for 𝑁→∞, the overlap ratio takes a rather simple form ⟨Ω⁡(𝑡)⟩=erfc⁡(𝑎⁢√𝑡) for 𝑛≫1 with some scaling parameter 𝑎>0. This result is a very good approximation even for moderate sizes of the top-𝑛 list such as 𝑛=10. Moreover, we observe in numerical studies that the overlap ratio exhibits universal behavior in many dynamical systems including geometric Brownian motion, Brownian motion with asymptotically linear drift, the Bouchaud-Mézard wealth distribution model, and Kesten processes. We conjecture the universality to hold for a broad class of one-dimensional stochastic processes. Read the full article at: link.aps.org

Top rank statistics for Brownian reshuffling

17.07.2025 11:54 — 👍 0    🔁 0    💬 0    📌 0
Collective cooperative intelligence W. Barfuss, J. Flack, C.S. Gokhale, L. Hammond, C. Hilbe, E. Hughes, J.Z. Leibo, T. Lenaerts, N. Leonard, S. Levin, U. Madhushani Sehwag, A. McAvoy, J.M. Meylahn, & F.P. Santos PNAS 122 (25) e2319948121 Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior—in which intelligent actors in complex environments jointly improve their well-being—remains poorly understood. Complex systems science (CSS) provides a rich understanding of collective phenomena, the evolution of cooperation, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context—largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be computationally expensive and challenging to interpret. In this perspective, we propose that bridging CSS and MARL affords new directions forward. Both fields can complement each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent collective phenomena. We see this approach as providing the necessary foundations for a proper science of collective, cooperative intelligence. We highlight work that is already heading in this direction and discuss concrete steps for future research. Read the full article at: www.pnas.org

Collective cooperative intelligence

29.06.2025 15:10 — 👍 3    🔁 1    💬 0    📌 0
Why collective behaviours self-organize to criticality: a primer on information-theoretic and thermodynamic utility measures Qianyang Chen and Mikhail Prokopenko Roy. Soc. Open Science Collective behaviours are frequently observed to self-organize to criticality. Existing proposals to explain these phenomena are fragmented across disciplines and only partially answer the question. This primer compares the underlying, intrinsic, utilities that may explain the self-organization of collective behaviours near criticality. We focus on information-driven approaches (predictive information, empowerment and active inference), as well as an approach incorporating both information theory and thermodynamics (thermodynamic efficiency). By interpreting the Ising model as a perception-action loop, we compare how different intrinsic utilities shape collective behaviour and analyse the distinct characteristics that arise when each is optimized. In particular, we highlight that thermodynamic efficiency—measuring the ratio of predictability gained by the system to its energy costs—reaches its maximum at the critical regime. Finally, we propose the Principle of Super-efficiency, suggesting that collective behaviours self-organize to the critical regime where optimal efficiency is achieved with respect to the entropy reduction relative to the thermodynamic costs. Read the full article at: royalsocietypublishing.org

Why collective behaviours self-organize to criticality: a primer on information-theoretic and thermodynamic utility measures

29.06.2025 11:31 — 👍 4    🔁 1    💬 0    📌 1
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Probabilistic alignment of multiple networks Teresa Lázaro, Roger Guimerà & Marta Sales-Pardo  Nature Communications volume 16, Article number: 3949 (2025) The network alignment problem appears in many areas of science and involves finding the optimal mapping between nodes in two or more networks, so as to identify corresponding entities across networks. We propose a probabilistic approach to the problem of network alignment, as well as the corresponding inference algorithms. Unlike heuristic approaches, our approach is transparent in that all model assumptions are explicit; therefore, it is susceptible of being extended and fine tuned by incorporating contextual information that is relevant to a given alignment problem. Also in contrast to current approaches, our method does not yield a single alignment, but rather the whole posterior distribution over alignments. We show that using the whole posterior leads to correct matching of nodes, even in situations where the single most plausible alignment mismatches them. Our approach opens the door to a whole new family of network alignment algorithms, and to their application to problems for which existing methods are perhaps inappropriate. Read the full article at: www.nature.com

Probabilistic alignment of multiple networks

28.06.2025 15:12 — 👍 0    🔁 0    💬 0    📌 0
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Unifying Systems : Information, Feedback, and Self-Organization, by Aarne Mämmelä Interdisciplinary systems thinking is complementary but does not replace conventional disciplinary analytical thinking. The book is valuable for researchers, their advisors, and other thinkers interested in deep knowledge of science. Interdisciplinary systems thinking is valuable for three reasons: The goal of all science is a unified view of the world; we cannot solve the significant problems of our time without interdisciplinary collaboration; and general theories of systems and system archetypes support the solution to those problems. System archetypes are generic system models that have stood the test of time. As specialists within a discipline, we must be able to communicate between disciplines. Interdisciplinary generalists can offer us reliable visions and relevant research problems. The goal of interdisciplinary research is to find unified solutions to those problems. The book provides a lot of information from over a thousand sources in a structured manner to help the reader. The book includes a comprehensive chronology, vocabulary, and bibliography. The author has been a research professor in information engineering for over 25 years. During his career, he became interested in systems thinking, which is closely related to the philosophy and history of science. More at: link.springer.com

Unifying Systems : Information, Feedback, and Self-Organization

27.06.2025 19:28 — 👍 4    🔁 1    💬 0    📌 0
Matrix-Weighted Networks for Modeling Multidimensional Dynamics: Theoretical Foundations and Applications to Network Coherence Yu Tian, Sadamori Kojaku, Hiroki Sayama, and Renaud Lambiotte Phys. Rev. Lett. 134, 237401 Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to nontrivial steady states that generalize the notions of communities and structural balance in traditional networks. Read the full article at: link.aps.org

Matrix-Weighted Networks for Modeling Multidimensional Dynamics: Theoretical Foundations and Applications to Network Coherence

27.06.2025 16:30 — 👍 3    🔁 1    💬 0    📌 0
Nitrogen-fixing organelle in a marine alga TYLER H. COALE, et al. SCIENCE 11 Apr 2024 Vol 384, Issue 6692 pp. 217-222 Symbiotic interactions were key to the evolution of chloroplast and mitochondria organelles, which mediate carbon and energy metabolism in eukaryotes. Biological nitrogen fixation, the reduction of abundant atmospheric nitrogen gas (N2) to biologically available ammonia, is a key metabolic process performed exclusively by prokaryotes. Candidatus Atelocyanobacterium thalassa, or UCYN-A, is a metabolically streamlined N2-fixing cyanobacterium previously reported to be an endosymbiont of a marine unicellular alga. Here we show that UCYN-A has been tightly integrated into algal cell architecture and organellar division and that it imports proteins encoded by the algal genome. These are characteristics of organelles and show that UCYN-A has evolved beyond endosymbiosis and functions as an early evolutionary stage N2-fixing organelle, or “nitroplast.” Read the full article at: www.science.org

Nitrogen-fixing organelle in a marine alga

27.06.2025 15:13 — 👍 0    🔁 0    💬 0    📌 0
Large Language Models and Emergence: A Complex Systems Perspective David C. Krakauer, John W. Krakauer, Melanie Mitchell Emergence is a concept in complexity science that describes how many-body systems manifest novel higher-level properties, properties that can be described by replacing high-dimensional mechanisms with lower-dimensional effective variables and theories. This is captured by the idea "more is different". Intelligence is a consummate emergent property manifesting increasingly efficient -- cheaper and faster -- uses of emergent capabilities to solve problems. This is captured by the idea "less is more". In this paper, we first examine claims that Large Language Models exhibit emergent capabilities, reviewing several approaches to quantifying emergence, and secondly ask whether LLMs possess emergent intelligence. Read the full article at: arxiv.org

Large Language Models and Emergence: A Complex Systems Perspective

27.06.2025 11:29 — 👍 2    🔁 1    💬 0    📌 0
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A continental scale analysis reveals widespread root bimodality Mingzhen Lu, Sili Wang, Avni Malhotra, Shersingh Joseph Tumber-Dávila, Samantha Weintraub-Leff, M. Luke McCormack, Xingchen Tony Wang & Robert B. Jackson Nature Communications volume 16, Article number: 5281 (2025) An improved understanding of root vertical distribution is crucial for assessing plant-soil-atmosphere interactions and their influence on the land carbon sink. Here, we analyze a continental-scale dataset of fine roots reaching 2 meters depth, spanning from Alaskan tundra to Puerto Rican forests. Contrary to the expectation that fine root abundance decays exponentially with depth, we found root bimodality at ~20% of 44 sites, with secondary biomass peaks often below 1 m. Root bimodality was more likely in areas with low total fine root biomass and was more frequent in shrublands than grasslands. Notably, secondary peaks coincided with high soil nitrogen content at depth. Our analyses suggest that deep soil nutrients tend to be underexploited, while root bimodality offers plants a mechanism to tap into deep soil resources. Our findings add to the growing recognition that deep soil dynamics are systematically overlooked, and calls for more research attention to this deep frontier in the face of global environmental change. Read the full article at: www.nature.com

A continental scale analysis reveals widespread root bimodality

26.06.2025 22:07 — 👍 0    🔁 0    💬 0    📌 0
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Compendium of Urban Complexity, edited by Diego Rybski This book brings together key findings, insights, and theories at the intersection of two disciplines – city science and complex systems. It features a curated collection of chapters contributed by emerging scholars conducting cutting-edge research in complexity science, interdisciplinary physics, and quantitative geography. The compendium is tailored to a thematically diverse audience, spanning quantitative fields such as statistical and mathematical physics, as well as socially-focused domains such as geography and urban planning. By integrating novel methods and insights from physics, economics, and geography, this book aims at an interdisciplinary spectrum of graduate students and academic researchers studying cities as complex systems. More at: link.springer.com

Compendium of Urban Complexity

26.06.2025 19:06 — 👍 0    🔁 0    💬 0    📌 0
International Conference on Complex Systems Modeling, Analysis & Applications [IC2SMA2 2026], 13 - 14 February, Pune Lavasa, India IC2SMA2 2026 aims to create a new international venue that can unite scholars, practitioners and students from diverse fields to address various real-world challenges and opportunities using methodologies of complex systems modeling and analysis. The conference will showcase cutting-edge modeling/analysis methods, interdisciplinary applications, and innovative solutions, fostering collaboration and sparking new ideas. Its 2026 edition will have a particular focus on the applications to education and society. By integrating insights from systems science, mathematics, computer science, engineering, economics, social sciences, psychology, healthcare, education, and many others, we seek to advance understanding and application in these crucial areas. Join us to explore how multidisciplinary approaches can drive improvements in our society! Organized in Hybrid Mode by CHRIST University, Pune Lavasa, India & Binghamton University, State University of New York, USA More at: ic2sma2.christuniversity.in

International Conference on Complex Systems Modeling, Analysis & Applications [IC2SMA2 2026], 13 - 14 February, Pune Lavasa, India

26.06.2025 16:15 — 👍 1    🔁 1    💬 0    📌 0
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ANTS 2026: 15th International Conference on Swarm Intelligence, June 8-10, 2026. Darmstadt, Germany Since its inception in 1998, ANTS has been a highly selective, single-track meeting that provided a forum for discussing advances in the field of swarm intelligence. It solicits submissions presenting significant, original research from researchers and practitioners of any area related to swarm intelligence. Swarm intelligence is an interdisciplinary and rapidly evolving field, rooted in the study of self-organizing processes in both natural and artificial systems. Researchers from disciplines ranging from ethology to statistical physics have developed models that explain collective phenomena, such as decision-making in social insect colonies and collective movements in human crowds. Swarm-inspired algorithms and methods have proven effective in solving complex optimization problems and creating multi-robot and networked systems of unparalleled resilience, adaptability and scalability. Applications of swarm intelligence continue to grow and become increasingly critical for addressing societal challenges such as environmental sustainability, food security, health, and global conflicts. The 2026 edition’s theme is "reaching beyond - swarm intelligence across systems, disciplines, and communities". The meeting seeks to encourage new perspectives, help bridge traditional boundaries and enable open debate on what could be ambitious, exploratory, and groundbreaking endeavors to embark on. More at: ants2026.org

ANTS 2026: 15th International Conference on Swarm Intelligence, June 8-10, 2026. Darmstadt, Germany

26.06.2025 15:02 — 👍 0    🔁 0    💬 0    📌 0

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