L. K. Eraso-Hernandez, A. P. Riascos

A system is called antifragile when damage acts as a constructive element improving the performance of a global function. In this letter, we analyze the emergence of antifragility in the movement of random walkers on networks with modular structures or communities. The random walker hops considering the capacity of transport of each link, whereas the links are susceptible to random damage that accumulates over time. We show that in networks with communities and high modularity, the localization of damage in specific groups of nodes leads to a global antifragile response of the system improving the capacity of stochastic transport to more easily reach the nodes of a network. Our findings give evidence of the mechanisms behind antifragile response in complex systems and pave the way for their quantitative exploration in different fields.

Read the full article at: arxiv.org

Vivek Jadhav, Roberto Pasqua, Christophe Zanon, Matthieu Roy, Gilles Tredan, Richard Bon, Vishwesha Guttal, Guy Theraulaz

Across taxa, group-living organisms exhibit collective escape responses to stimuli varying from mild stress to predatory pressures. How exactly does information flow among group members leading to a collective escape remains an open question. Here we study the collective responses of a flock of sheep to a shepherd dog in a driving task between well-defined target points. We collected high-resolution spatio-temporal data from 14 sheep and the dog, using Ultra Wide Band tags attached to each individual. Through the time delay analysis of velocity correlations, we identify a hierarchy among sheep in terms of directional influence. Notably, the average spatial position of a sheep along the front-back axis of the group's velocity strongly correlates with its impact on the collective movement. Our findings demonstrate that, counter-intuitively, directional information on shorter time scales propagates from the front of the group towards the rear, and that the dog exhibits adaptive movement adjustments in response to the flock's dynamics. Furthermore, we show that a simple shepherding model can capture key features of the collective response of the sheep flocks. In conclusion, our study reveals novel insights on how directional information propagates in escaping animal groups.

Read the full article at: www.biorxiv.org

Marta Ewa Lech, Sune Lehmann, Jonas L. Juul

Since the creation of the Billboard Hot 100 music chart in 1958, the chart has been a window into the music consumption of Americans. Which songs succeed on the chart is decided by consumption volumes, which can be affected by consumer music taste, and other factors such as advertisement budgets, airplay time, the specifics of ranking algorithms, and more. Since its introduction, the chart has documented music consumerism through eras of globalization, economic growth, and the emergence of new technologies for music listening. In recent years, musicians and other hitmakers have voiced their worry that the music world is changing: Many claim that it is getting harder to make a hit but until now, the claims have not been backed using chart data. Here we show that the dynamics of the Billboard Hot 100 chart have changed significantly since the chart's founding in 1958, and in particular in the past 15 years. Whereas most songs spend less time on the chart now than songs did in the past, we show that top-1 songs have tripled their chart lifetime since the 1960s, the highest-ranked songs maintain their positions for far longer than previously, and the lowest-ranked songs are replaced more frequently than ever. At the same time, who occupies the chart has also changed over the years: In recent years, fewer new artists make it into the chart and more positions are occupied by established hit makers. Finally, investigating how song chart trajectories have changed over time, we show that historical song trajectories cluster into clear trajectory archetypes characteristic of the time period they were part of. The results are interesting in the context of collective attention: Whereas recent studies have documented that other cultural products such as books, news, and movies fade in popularity quicker in recent years, music hits seem to last longer now than in the past.

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Claudio Gnoli

Volume 29, pages 543–551, (2024)

Emergence can be described as a relationship between entities at different levels of organization, that looks especially puzzling at the transitions between the major levels of matter, life, cognition and culture. Indeed, each major level is dependent on the lower one not just for its constituents, but in some more formal way. A passage by François Jacob suggests that all such evolutionary transitions are associated with the appearance of some form of memory–genetic, neural or linguistic respectively. This implies that they have an informational nature. Based on this idea, we propose a general model of informational systems understood as combinations of modules taken from a limited inventory. Some informational systems are “semantic” models, that is reproduce features of their environment. Among these, some are also “informed”, that is have a pattern derived from a memory subsystem. The levels and components of informed systems can be listed to provide a general framework for knowledge organization, of relevance in both philosophical ontology and applied information services.

Read the full article at: link.springer.com

Tiago P. Peixoto

A fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting, and produces an inferred network with a statistically justifiable number of edges. The status quo in this context is based on L1 regularization combined with cross-validation. However, besides its high computational cost, this commonplace approach unnecessarily ties the promotion of sparsity with weight "shrinkage". This combination forces a trade-off between the bias introduced by shrinkage and the network sparsity, which often results in substantial overfitting even after cross-validation. In this work, we propose an alternative nonparametric regularization scheme based on hierarchical Bayesian inference and weight quantization, which does not rely on weight shrinkage to promote sparsity. Our approach follows the minimum description length (MDL) principle, and uncovers the weight distribution that allows for the most compression of the data, thus avoiding overfitting without requiring cross-validation. The latter property renders our approach substantially faster to employ, as it requires a single fit to the complete data. As a result, we have a principled and efficient inference scheme that can be used with a large variety of generative models, without requiring the number of edges to be known in advance. We also demonstrate that our scheme yields systematically increased accuracy in the reconstruction of both artificial and empirical networks. We highlight the use of our method with the reconstruction of interaction networks between microbial communities from large-scale abundance samples involving in the order of 104 to 105 species, and demonstrate how the inferred model can be used to predict the outcome of interventions in the system.

Read the full article at: arxiv.org

Andreagiovanni Reina, Thierry Njougouo, Elio Tuci, and Timoteo Carletti
Phys. Rev. E 109, 054307

To succeed in their objectives, groups of individuals must be able to make quick and accurate collective decisions on the best option among a set of alternatives with different qualities. Group-living animals aim to do that all the time. Plants and fungi are thought to do so too. Swarms of autonomous robots can also be programed to make best-of-n decisions for solving tasks collaboratively. Ultimately, humans critically need it and so many times they should be better at it! Thanks to their mathematical tractability, simple models like the voter model and the local majority rule model have proven useful to describe the dynamics of such collective decision-making processes. To reach a consensus, individuals change their opinion by interacting with neighbors in their social network. At least among animals and robots, options with a better quality are exchanged more often and therefore spread faster than lower-quality options, leading to the collective selection of the best option. With our work, we study the impact of individuals making errors in pooling others' opinions caused, for example, by the need to reduce the cognitive load. Our analysis is grounded on the introduction of a model that generalizes the two existing models (local majority rule and voter model), showing a speed-accuracy trade-off regulated by the cognitive effort of individuals. We also investigate the impact of the interaction network topology on the collective dynamics. To do so, we extend our model and, by using the heterogeneous mean-field approach, we show the presence of another speed-accuracy trade-off regulated by network connectivity. An interesting result is that reduced network connectivity corresponds to an increase in collective decision accuracy.

Read the full article at: link.aps.org

Hiroki Sayama

There is an increasing level of interest in open-endedness in the recent literature of Artificial Life and Artificial Intelligence. We previously proposed the cardinality leap of possibility spaces as a promising mechanism to facilitate open-endedness in artificial evolutionary systems, and demonstrated its effectiveness using Hash Chemistry, an artificial chemistry model that used a hash function as a universal fitness evaluator. However, the spatial nature of Hash Chemistry came with extensive computational costs involved in its simulation, and the particle density limit imposed to prevent explosion of computational costs prevented unbounded growth in complexity of higher-order entities. To address these limitations, here we propose a simpler non-spatial variant of Hash Chemistry in which spatial proximity of particles are represented explicitly in the form of multisets. This model modification achieved a significant reduction of computational costs in simulating the model. Results of numerical simulations showed much more significant unbounded growth in both maximal and average sizes of replicating higher-order entities than the original model, demonstrating the effectiveness of this non-spatial model as a minimalistic example of open-ended evolutionary systems.

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Laurent Hébert-Dufresne, Antoine Allard, Joshua Garland, Elizabeth A. Hobson & Luis Zaman 
npj Complexity volume 1, Article number: 4 (2024)

Complexity science studies systems where large numbers of components or subsystems, at times of a different nature, combine to produce surprising emergent phenomena apparent at multiple scales. It is these phenomena, hidden behind the often deceptively simple rules that govern individual components, that best define complex systems. Since these behaviors of interest arise from interactions between parts, complex systems are not counterparts to simple systems but rather to separable ones. Their study therefore often requires a collaborative approach to science, studying a problem across scales and disciplinary domains. However, this approach introduces challenges into the ways collaborations function across traditionally-siloed disciplines, and in the publication of complexity science, which often does not fall cleanly into disciplinary journals. In this editorial, we provide our view of the current state of complex systems research and explain how this new journal will fill an important niche for researchers working on these ideas.

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Stefano Nolfi

Adaptive Behavior

Large Language Models (LLMs) are capable of displaying a wide range of abilities that are not directly connected with the task for which they are trained: predicting the next words of human-written texts. In this article, I review recent research investigating the cognitive abilities developed by LLMs and their relation to human cognition. I discuss the nature of the indirect process that leads to the acquisition of these cognitive abilities, their relation to other indirect processes, and the implications for the acquisition of integrated abilities. Moreover, I propose the factors that enable the development of abilities that are related only very indirectly to the proximal objective of the training task. Finally, I discuss whether the full set of capabilities that LLMs could possibly develop is predictable.

Read the full article at: journals.sagepub.com

Andrea Musso and Dirk Helbing

Royal Society Open Science

May 2024 Volume 11Issue 5

Socio-diversity, the variety of human opinions, ideas, behaviours and styles, has profound implications for social systems. While it fuels innovation, productivity and collective intelligence, it can also complicate communication and erode trust. So what mechanisms can influence it? This paper studies how fundamental characteristics of social networks can support or hinder socio-diversity. It employs models of cultural evolution, mathematical analysis and numerical simulations. We find that pronounced inequalities in the distribution of connections obstruct socio-diversity. By contrast, the prevalence of close-knit communities, a scarcity of long-range connections, and a significant tie density tend to promote it. These results open new perspectives for understanding how to change social networks to sustain more socio-diversity and, thereby, societal innovation, collective intelligence and productivity.

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Iacopo Iacopini, Jennifer R. Foote, Nina H. Fefferman, Elizabeth P. Derryberry and Matthew J. Silk

Phil Trans Roy Soc B

08 July 2024 Volume 379Issue 1905

Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalization. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual’s behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g. hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: (i) alter predictions made about the outcome of vocally coordinated group departures; (ii) generate different patterns of song synchronization from models that only include dyadic interactions; and (iii) inform models of cultural evolution of vocal communication. Together, our examples highlight the potential power of higher-order networks to study animal vocal communication. We then build on our case studies to identify key challenges in applying higher-order network approaches in this context and outline important research questions that these techniques could help answer.

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Klaus Jaffe

Information and Energy are related. The Second Law of Thermodynamics applies to changes in energy and heat, but it does not apply to information dynamics. Advances in Infodynamics have made it clear that Total Information contains Useful Information and Noise, both of which may be gained or lost in irreversible processes. Increases in Free Energy of open systems require more Useful Information, reducing or increasing Thermodynamic Entropy. Empirical data show that the more Free Energy is created, the more Useful Information is required; and the more Useful Information is produced the more Free Energy is spent. The Energy – Information relationship underlies all processes where novel structures, forms and systems emerge. Although science cannot predict the structure of information that will produce Free Energy, engineers have been successful in finding Useful Information that increases Free Energy. Here I explore the fate of information in irreversible processes and its relation with the Second Law of Thermodynamics.

Read the full article at: www.qeios.com

Manlio De Domenico, Luca Allegri, Guido Caldarelli, Valeria d'Andrea, Barbara Di Camillo, Luis M. Rocha, Jordan Rozum, Riccardo Sbarbati, Francesco Zambelli

The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.

Read the full article at: arxiv.org

Louie Slocombe and Sara Imari Walker

​ACS Cent. Sci. 2024

In a scientific era focused on big data, it is easy to lose sight of the critical role of metrology─the science of measurement─in advancing fundamental science. However, most major scientific advances have been driven by progress in what we measure and how we measure it. An example is the invention of temperature, (1) where before it, we could say one thing was hotter than another but without a standardized, empirical measure we could not say how much hotter. This is not unlike the current state in discussing complexity in chemistry, (2,3) where we can say molecules are complex but lack an empirically validated standardization to confirm that one is more complex than another. In this issue of ACS Central Science, (4) a set of experiments by Leroy Cronin and co-workers conducted at the University of Glasgow aim to change this by providing a new kind of measurement with a well-defined scale, a significant step toward a metrology of complexity in chemistry. Although the concept of quantifying molecular complexity is not new itself, (3) the team leveraged principles from the recently developed theory of molecular assembly (MA) and related ideas (5) to define a rigorous concept of a scale for complexity, connected to a theory for how evolution builds complex molecules. (6,7) They show how the complexity of molecules on this scale can be inferred from standard laboratory spectroscopic techniques, including nuclear magnetic resonance (NMR), infrared (IR) spectroscopy, and tandem mass spectrometry (MS/MS). The robust validation of the inferred complexity across a multimodal suite of techniques instills confidence in the objectivity of the complexity scale proposed and the reliability of its resultant measurement.

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Roberto Casadei, Lukas Esterle, Rose Gamble, Paul Harvey, and Elizabeth F. Wanner

Front. Robot. AI, 06 May 2024

Cyber-physical collectives (CPCs) are systems consisting of groups of interactive computational devices situated in physical space. Their emergence is fostered by recent techno-scientific trends like the Internet of Things (IoT), cyber-physical systems (CPSs), pervasive computing, and swarm robotics. Such systems feature networks of devices that are capable of computation and communication with other devices, as well as sensing, actuation, and physical interaction with their environment. This distributed sensing, processing, and action enables them to address spatially situated problems and provide environment-wide services through their collective intelligence (CI) in a wide range of domains including smart homes, buildings, factories, cities, forests, oceans, and so on. However, the inherent complexity of such systems in terms of heterogeneity, scale, non-linear interaction, and emergent behaviour calls for scientific and engineering ideas, methods, and tools (cf. Wirsing et al. (2023); Dorigo et al. (2021); Brambilla et al. (2013); Casadei (2023a; b)). This Research Topic gathers contributions related to understanding and engineering cyber-physical collectives.

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Abramson, J., Adler, J., Dunger, J. et al.

Nature (2024).

The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. In this paper, we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates significantly improved accuracy over many previous specialised tools: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than nucleic-acid-specific predictors, and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer v2.3. Together these results show that high accuracy modelling across biomolecular space is possible within a single unified deep learning framework.

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Rafael Prieto-Curiel, Gian Maria Campedelli

El Salvador, once one of the most violent countries in the world, has, in recent years, experienced a huge drop in homicides. The massive reduction is the result of Nayib Bukele's anti-gang policies, which brought widespread domestic and international popularity to the President and its government. Other countries suffering high levels of violence are praising Bukele's actions, electing El Salvador as a model to be followed despite the blatant violations of human, civil and political rights suffered by its citizens. While concurring that this aspect represents the most concerning facet of El Salvador's strategy, we reflect on whether other countries should follow Bukele's policies, elaborating on issues that have been largely overlooked. First, the policy scalability, adaptability and external validity. Second, the long-term vision of the prison population and the demographic and economic costs. As a result of our reflections, we conclude that other countries should not follow El Salvador's strategy: beyond the likely erosion of citizens' rights, the exportation of the policy may entail an array of additional unbearable costs, making Latin American democracies weaker.

Read the full article at: papers.ssrn.com

Adam Rupe, James P. Crutchfield

Physics Reports

Volume 1071, 13 June 2024, Pages 1-47

After more than a century of concerted effort, physics still lacks basic principles of spontaneous self-organization. To appreciate why, we first state the problem, outline historical approaches, and survey the present state of the physics of self-organization. This frames the particular challenges arising from mathematical intractability and the resulting need for computational approaches, as well as those arising from a chronic failure to define structure. Then, an overview of two modern mathematical formulations of organization—intrinsic computation and evolution operators—lays out a way to overcome these challenges. Additionally, we show how intrinsic computation and evolution operators combine to produce a general framework showing physical consistency between emergent behaviors and their underlying physics. This statistical mechanics of emergence provides a theoretical foundation for data-driven approaches to organization necessitated by analytic intractability. Taken all together, the result is a constructive path towards principles of organization that builds on the mathematical identification of structure.

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Carlos Gershenson

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I'll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming --- or accepting --- their limits.

Read the full article at: www.preprints.org