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Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani Artificial Intelligence Volume 339, February 2025, 104244 Read the full article at: www.sciencedirect.com 
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Takahiro Yabe, Bernardo García Bulle Bueno, Morgan R. Frank, Alex Pentland & Esteban Moro Disruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks. Read the full article at: www.nature.com
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Hiroki Sayama Hash Chemistry, a minimalistic artificial chemistry model of open-ended evolution, has recently been extended to non-spatial and cellular versions. The non-spatial version successfully demonstrated continuous adaptation and unbounded growth of complexity of self-replicating entities, but it did not simulate multiscale ecological interactions among the entities. On the contrary, the cellular version explicitly represented multiscale spatial ecological interactions among evolving patterns, yet it failed to show meaningful adaptive evolution or complexity growth. It remains an open question whether it is possible to create a similar minimalistic evolutionary system that can exhibit all of those desired properties at once within a computationally efficient framework. Here we propose an improved version called Structural Cellular Hash Chemistry (SCHC). In SCHC, individual identities of evolving patterns are explicitly represented and processed as the connected components of the nearest neighbor graph of active cells. The neighborhood connections are established by connecting active cells with other active cells in their Moore neighborhoods in a 2D cellular grid. Evolutionary dynamics in SCHC are simulated via pairwise competitions of two randomly selected patterns, following the approach used in the non-spatial Hash Chemistry. SCHC's computational cost was significantly less than the original and non-spatial versions. Numerical simulations showed that these model modifications achieved spontaneous movement, self-replication and unbounded growth of complexity of spatial evolving patterns, which were clearly visible in space in a highly intuitive manner. Detailed analysis of simulation results showed that there were spatial ecological interactions among self-replicating patterns and their diversity was also substantially promoted in SCHC, neither of which was present in the non-spatial version. Read the full article at: arxiv.org
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Alireza Zaeemzadeh, Giulio Tononi Information theory, introduced by Shannon, has been extremely successful and influential as a mathematical theory of communication. Shannon's notion of information does not consider the meaning of the messages being communicated but only their probability. Even so, computational approaches regularly appeal to "information processing" to study how meaning is encoded and decoded in natural and artificial systems. Here, we contrast Shannon information theory with integrated information theory (IIT), which was developed to account for the presence and properties of consciousness. IIT considers meaning as integrated information and characterizes it as a structure, rather than as a message or code. In principle, IIT's axioms and postulates allow one to "unfold" a cause-effect structure from a substrate in a state, a structure that fully defines the intrinsic meaning of an experience and its contents. It follows that, for the communication of meaning, the cause-effect structures of sender and receiver must be similar. Read the full article at: arxiv.org
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Sandro M. Reia, P. Suresh C. Rao, Marc Barthelemy & Satish V. Ukkusuri Recent studies show that rare and extreme domestic migration flows influence both population growth and the rise and fall of cities in urbanized countries such as the USA, Canada, the UK and France. This study examines the relationship between domestic net flows (inflows minus outflows) and city rank volatility across countries over time. We find that approximately 95% of cities, representing up to 99% of a country’s population, exhibit rescaled net flows that conform to normal distributions, while about 5% experience migration shocks. Small cities are more susceptible to these shocks, often caused by net flows from larger, nearby cities, while in France, large cities also experience shocks from smaller ones. We also show that domestic migration is an important component of population growth in small cities, thus explaining their rank volatility, and that the rank stability of large cities is supported by international migration and natural increase. Read the full article at: www.nature.com
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Alexander F. Siegenfeld, Asier Piñeiro Orioli, Robin Na, Blake Elias, Yaneer Bar-Yam Although pandemics are often studied as if populations are well-mixed, disease transmission networks exhibit a multi-scale structure stretching from the individual all the way up to the entire globe. The COVID-19 pandemic has led to an intense debate about whether interventions should prioritize public health or the economy, leading to a surge of studies analyzing the health and economic costs of various response strategies. Here we show that describing disease transmission in a self-similar (fractal) manner across multiple geographic scales allows for the design of multi-scale containment measures that substantially reduce both these costs. We characterize response strategies using multi-scale reproduction numbers -- a generalization of the basic reproduction number R0 -- that describe pandemic spread at multiple levels of scale and provide robust upper bounds on disease transmission. Stable elimination is guaranteed if there exists a scale such that the reproduction number among regions of that scale is less than 1, even if the basic reproduction number R0 is greater than 1. We support our theoretical results using simulations of a heterogeneous SIS model for disease spread in the United States constructed using county-level commuting, air travel, and population data. Read the full article at: arxiv.org
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Gordana Dodig-Crnkovic The development of naturalistic approaches to complexity of life continues a lineage of thought from Prigogine’s thermodynamics to contemporary complexity science. The paper highlights the central themes of self-organization, emergence, and the interplay between physical, informational, and biological processes. Prigogine’s concept of dissipative structures and irreversibility provided a foundation for understanding complexity in physical systems, which later expanded into biology through Kauffman’s models of creativity and evolution. Margulis's endosymbiosis theory illuminate the cooperative dynamics underpinning life’s complexity, while Walker's work integrates thermodynamics and information theory to bridge the gap between chemistry and biology through multiscale interactions and adaptive dynamics. By synthesizing these perspectives, this article situates life as an emergent phenomenon shaped by interactions across scales, proposing a unified framework for understanding complexity in the natural world. Read the full article at: www.preprints.org
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Alexis Pietak, Michael Levin Regulatory networks such as gene regulatory networks (GRNs) are critically important for efforts in biomedicine and synthetic biology. They have classically been viewed as mechanistic, “clockwork-like” systems, assumed to require direct changes to network topology via genetic modification to effect significant, stable changes in their output functions. This perspective limits therapeutic approaches, suggesting a need for alternative conceptual framing. Here we show how regulatory networks can behave as analog computational agents to perform sophisticated information processing, driven by patterns of stimulus inputs, without a change in network topology. We introduce and develop a new conceptual and computational framework for working with regulatory networks called the Regulatory Network Machine (RNM). Given a regulatory network model, our RNM framework enables the construction of detailed maps that embody the “software-like” nature of a regulatory network, providing easy identification of the specific interventions necessary to achieve desired outcomes. We demonstrate the use of our RNM framework to gain insights into important biological examples including yeast osmoadaptation, PI3K/AKT/mTor cross-signaling cascades, and embryonic stem cell differentiation. Importantly, we show how system-level outcomes can be induced in a biological system without requiring genetic rewiring. Our RNM approach also elucidates system factors that support the innate computational capabilities of regulatory networks, and ascertains the interventions that provide the most control for the least amount of effort. Ultimately, we hope to use insights gained from our RNM framework to expand the horizons of biomedicine, providing an effective avenue to move beyond “single-factor, single treatment” and “one-constant-dose” biomedical paradigms. Read the full article at: www.researchgate.net
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Nicolò Gozzi, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Marco Ajelli, Alessandro Vespignani, Nicola Perra Epidemics Volume 49, December 2024, 100805 The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models’ preliminary assessment about Omicron’s transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises. Read the full article at: www.sciencedirect.com
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Sergey Gavrilets, Denis Tverskoi, Nianyi Wang, Xiaomin Wang, Juan Ozaita, Boyu Zhang, Angel Sánchez, and Giulia Andrighetto Evolutionary Human Sciences , Volume 6 , 2024 , e50 Understanding and predicting human cooperative behaviour and belief dynamics remains a major challenge both from the scientific and practical perspectives. Because of the complexity and multiplicity of material, social and cognitive factors involved, both empirical and theoretical work tends to focus only on some snippets of the puzzle. Recently, a mathematical theory has been proposed that integrates material, social and cognitive aspects of behaviour and beliefs dynamics to explain how people make decisions in social dilemmas within heterogeneous groups. Here we apply this theory in two countries, China and Spain, through four long-term behavioural experiments utilising the Common Pool Resources game and the Collective Risk game. Our results show that material considerations carry the smallest weight in decision-making, while personal norms tend to be the most important factor. Empirical and normative expectations have intermediate weight in decision-making. Cognitive dissonance, social projection, logic constraints and cultural background play important roles in both decision-making and beliefs dynamics. At the individual level, we observe differences in the weights that people assign to factors involved in the decision-making and belief updating process. We identify different types of prosociality and rule-following associated with cultural differences, various channels for the effects of messaging, and culturally dependent interactions between sensitivity to messaging and conformity. Our results can put policy and information design on firmer ground, highlighting the need for interventions tailored to the situation at hand and to individual characteristics. Overall, this work demonstrates the theoretical and practical power of the theory in providing a more comprehensive understanding of human behaviour and beliefs. Read the full article at: www.cambridge.org
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Keith Farnsworth Confusion over the terms ‘information’ and ‘causation’ in theoretical biology is a problem. Most of it results from misinterpreting cybernetic systems, or even worse, statistical metrics, for physical information phenomena. Over the past several years, our understanding of causation has developed to recognise it as the constraint on the action of physical forces by the spatiotemporal configuration of matter (or energy fields). That configuration has been identified with physically embodied information. This work begins by clarifying that. It then proceeds to demonstrate biologically relevant implications. First, by revealing the physical organisation that underlies synergistic information (an influential idea, especially in neuroscience). Then by applying a rigorous account of multi-level causation to positional information (in multicellular development) and ecological community structure. The approach presented reveals underlying physical structuring in cybernetic systems and clearly delineates the limits to their physical embodiment - e.g. showing how ecological communities can only be entities separate from their component parts in rather special circumstances. It also provides a clear argument for upward and downward causation, unveiling the mechanisms for both Read the full article at: www.researchgate.net
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Arke Vogell, Udo Schilcher, Jorge F. Schmidt, and Christian Bettstetter Phys. Rev. E 110, 054214 Coupled oscillator systems can lead to states in which synchrony and chaos coexist. These states are called “chimera states.” The mechanism that explains the occurrence of chimera states is not well understood, especially in pulse-coupled oscillators. We study a variation of a pulse-coupled oscillator model that has been shown to produce chimera states, demonstrate that it reproduces several of the expected chimera properties, like the formation of multiple heads and the ability to control the natural drift that Kuramoto's chimera states experience in a ring, and explain how chimera states emerge. Our contribution is defining the model, analyzing the mechanism leading to chimera states, and comparing it with examples from the field of Kuramoto oscillators. Read the full article at: link.aps.org
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Jairo F. Gudiño , Umberto Grandi and César Hidalgo Roy Soc Phil Trans A Volume 382I ssue 2285 We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens’ preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil’s 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject’s individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a ‘bundle rule’, which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation. Read the full article at: royalsocietypublishing.org
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Aleksandar Haber, Ferenc Molnar, Adilson E. Motter Chaos 34, 123166 (2024) In the classical control of network systems, the control actions on a node are determined as a function of the states of all nodes in the network. Motivated by applications where the global state cannot be reconstructed in real time due to limitations in the collection, communication, and processing of data, here we introduce a control approach in which the control actions can be computed as a function of the states of the nodes within a limited state information neighborhood. The trade-off between the control performance and the size of this neighborhood is primarily determined by the condition number of the controllability Gramian. Our theoretical results are supported by simulations on regular and random networks and are further illustrated by an application to the control of power-grid synchronization. We demonstrate that for well-conditioned Gramians, there is no significant loss of control performance as the size of the state information neighborhood is reduced, allowing efficient control of large networks using only local information. Read the full article at: pubs.aip.org
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Vicky Chuqiao Yang, Jacob J Jackson, Christopher P Kempes Cities exhibit consistent returns to scale in economic outputs, and urban scaling analysis is widely adopted to uncover common mechanisms in cities’ socioeconomic productivity. Leading theories view cities as closed systems, with returns to scale arising from intra-city social interactions. Here, we argue that the interactions between cities, particularly via shared organizations such as firms, significantly influence a city’s economic output. By examining global data on city connectivity through multinational firms alongside urban scaling Gross Domestic Product (GDP) statistics from the United States, EU, and China, we establish that global connectivity notably enhances GDP, while controlling for population. After accounting for global connectivity, the effect of population on GDP is no longer distinguishable from linear. To differentiate between local and global mechanisms, we analyzed homicide case data, anticipating dominant local effects. As expected, inter-city connectivity showed no significant impact. Our research highlights that inter-city effects affect some urban outputs more than others. This empirical analysis lays the groundwork for incorporating inter-city organizational connections into urban scaling theories and could inform future model development. Read the full article at: academic.oup.com
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Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami & Takenori Tomaru Integrated information theory (IIT) assesses the degree of consciousness in living organisms from an information-theoretic perspective. This theory can be generalised to other systems, including those exhibiting criticality. In this study, we applied IIT to the collective behaviour of Plecoglossus altivelis and observed that the group integrity (Φ) was maximised at the critical state. Multiple levels of criticality were identified within the group, existing as distinct subgroups. Moreover, these fragmented critical subgroups coexisted alongside the overall criticality of the group. The distribution of high-criticality subgroups was heterogeneous across both time and space. Notably, core fish in the high-criticality subgroups were less affected by internal and external stimuli compared to those in low-criticality subgroups. These findings are consistent with previous interpretations of critical phenomena and offer a new perspective on the dynamics of an empirical critical state. Read the full article at: www.nature.com
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Binglu Wang, Ching Jin, Chaoming Song, Johannes Bjelland, Brian Uzzi, Dashun Wang Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle. Read the full article at: arxiv.org
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James DiFrisco, Richard Gawne Journal of Evolutionary Biology, voae153 This paper evaluates recent work purporting to show that the “agency” of organisms is an important phenomenon for evolutionary biology to study. Biological agency is understood as the capacity for goal-directed, self-determining activity—a capacity that is present in all organisms irrespective of their complexity and whether or not they have a nervous system. Proponents of the “agency perspective” on biological systems have claimed that agency is not explainable by physiological or developmental mechanisms, or by adaptation via natural selection. We show that this idea is theoretically unsound and unsupported by current biology. There is no empirical evidence that the agency perspective has the potential to advance experimental research in the life sciences. Instead, the phenomena that the agency perspective purports to make sense of are better explained using the well-established idea that complex multiscale feedback mechanisms evolve through natural selection. Read the full article at: academic.oup.com
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Sadamori Kojaku, Filippo Radicchi, Yong-Yeol Ahn & Santo Fortunato Nature Communications volume 15, Article number: 9446 (2024) Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space. Read the full article at: www.nature.com
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Fabian Dvorak, Urs Fischbacher, Katrin Schmelz The Economic Journal, ueae085, We provide systematic insights on strategic conformist—as well as anti-conformist—behaviour in situations where people are evaluated, i.e., where an individual has to be selected for reward (e.g., promotion) or punishment (e.g., layoffs). To affect the probability of being selected, people may attempt to fit in or stand out in order to affect the chances of being noticed or liked by the evaluator. We investigate such strategic incentives for conformity or anti-conformity experimentally in three different domains: facts, taste and creativity. To distinguish conformity and anti-conformity from independence, we introduce a new experimental design that allows us to predict participants’ independent choices based on transitivity. We find that the prospect of punishment increases conformity, while the prospect of reward reduces it. Anti-conformity emerges in the prospect of reward, but only under specific circumstances. Similarity-based selection (i.e., homophily) is much more important for the evaluators’ decisions than salience. We also employ a theoretical approach to illustrate strategic key mechanisms of our experimental setting. Read the full article at: academic.oup.com
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Abigail Devereaux, Roger Koppl Economists model knowledge use and acquisition as a cause-and-effect calculus associating observations made by a decision-maker about their world with possible underlying causes. Knowledge models are well-established for static contexts, but not for contexts of innovative and unbounded change. We develop a representation of knowledge use and acquisition in open-ended evolutionary systems and demonstrate its primary results, including that observers embedded in open-ended evolutionary systems can agree to disagree and that their ability to theorize about their systems is fundamentally local and constrained to their frame of reference what we call frame relativity. The results of our framework formalize local knowledge use, the many-selves interpretation of reasoning through time, and motivate the emergence of nonlogical modes of reasoning like institutional and aesthetic codes. Read the full article at: arxiv.org
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Matthew R. DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, and Filippo Menczer PNAS 121 (50) e2322823121 This study explores how large language models (LLMs) used for fact-checking affect the perception and dissemination of political news headlines. Despite the growing adoption of AI and tests of its ability to counter online misinformation, little is known about how people respond to LLM-driven fact-checking. This experiment reveals that even LLMs that accurately identify false headlines do not necessarily enhance users’ abilities to discern headline accuracy or promote accurate news sharing. LLM fact checks can actually reduce belief in true news wrongly labeled as false and increase belief in dubious headlines when the AI is unsure about an article’s veracity. These findings underscore the need for research on AI fact-checking’s unintended consequences, informing policies to enhance information integrity in the digital age.Read the full article in PNAS: doi:10.1073/pnas.2322823121
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Guillermo Restrepo Theory in Biosciences Volume 143, pages 237–251, (2024) In an effort to expand the domain of mathematical chemistry and inspire research beyond the realms of graph theory and quantum chemistry, we explore five mathematical chemistry spaces and their interconnectedness. These spaces comprise the chemical space, which encompasses substances and reactions; the space of reaction conditions, spanning the physical and chemical aspects involved in chemical reactions; the space of reaction grammars, which encapsulates the rules for creating and breaking chemical bonds; the space of substance properties, covering all documented measurements regarding substances; and the space of substance representations, composed of the various ontologies for characterising substances. Read the full article at: link.springer.com
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KAROLINE WIESNER, JYOTSNA PURI, and ANDREAS REUMANN Advances in Complex SystemsVol. 27, No. 06, 2440004 (2024) As governments and multilateral institutions launch projects and programs to support climate change mitigation and adaptation, the challenge lies in determining their effectiveness. The high complexity of climate-change programs often makes it difficult to determine their effectiveness through standard monitoring and evaluation procedures. ‘complexity-aware monitoring’ is a qualitative approach to monitoring, recently introduced by international development programs. This increasing awareness of complexity in the evaluation sector opens up a window of opportunity for complexity science to support climate change mitigation and adaptation programs. This paper’s contribution is a hands-on methodology for live monitoring and evaluation of development programs. The methodology is rooted in existing literature on social–ecological systems, as pioneered by Ostrom, and in quantitative methods from complexity science. To illustrate the methodology, an existing climate mitigation project in Madagascar, funded, monitored and evaluated by the Green Climate Fund, is discussed. Read the full article at: www.worldscientific.com
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Philippe Collard Artificial Life (2024) 30 (2): 171–192. This article deals with individuals moving in procession in real and artificial societies. A procession is a minimal form of society in which individual behavior is to go in a given direction and the organization is structured by the knowledge of the one ahead. This simple form of grouping is common in the living world, and, among humans, procession is a very circumscribed social activity whose origins are certainly very remote. This type of organization falls under microsociology, where the focus is on the study of direct interactions between individuals within small groups. In this article, we focus on the particular case of pine tree processionary caterpillars (Thaumetopoea pityocampa). In the first part, we propose a formal definition of the concept of procession and compare field experiments conducted by entomologists with agent-based simulations to study real caterpillars’ processionaries as they are. In the second part, we explore the life of caterpillars as they could be. First, by extending the model beyond reality, we can explain why real processionary caterpillars behave as they do. Then we report on field experiments on the behavior of real caterpillars artificially forced to follow a circular procession; these experiments confirm that each caterpillar can either be the leader of the procession or follow the one in front of it. In the third part, by allowing variations in the speed of movement on an artificial circular procession, computational simulations allow us to observe the emergence of unexpected mobile spatial structures built from regular polygonal shapes where chaotic movements and well-ordered forms are intimately linked. This confirms once again that simple rules can have complex consequences. Read the full article at: direct.mit.edu
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