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Bundling by volume exclusion in non-equilibrium spaghetti

Complexity Digest - Fri, 01/12/2024 - 11:37

I. Bonamassa, B. Ráth, M. Pósfai, M. Abért, D. Keliger, B. Szegedy, J. Kertész, L. Lovász, A.-L. Barabási

In physical networks, like the brain or metamaterials, we often observe local bundles, corresponding to locally aligned link configurations. Here we introduce a minimal model for bundle formation, modeling physical networks as non-equilibrium packings of hard-core 3D elongated links. We show that growth is logarithmic in time, in stark contrast with the algebraic behavior of lower dimensional random packing models. Equally important, we find that this slow kinetics is metastable, allowing us to analytically predict an algebraic growth due to the spontaneous formation of bundles. Our results offer a mechanism for bundle formation resulting purely from volume exclusion, and provide a benchmark for bundling activation and growth during the assembly of physical networks.

Read the full article at: arxiv.org

Fireflies, brain cells, dancers: new synchronisation research shows nature’s perfect timing is all about connections

Complexity Digest - Thu, 01/11/2024 - 13:13

Joseph Lizier

Getting in sync can be exhilarating when you’re dancing in rhythm with other people or clapping along in an audience. Fireflies too know the joy of synchronisation, timing their flashes together to create a larger display to attract mates.

Synchronisation is important at a more basic level in our bodies, too. Our heart cells all beat together (at least when things are going well), and synchronised electrical waves can help coordinate brain regions – but too much synchronisation of brain cells is what happens in an epileptic seizure.

Sync most often emerges spontaneously rather than through following the lead of some central timekeeper. How does this happen? What is it about a system that determines whether sync will emerge, and how strong it will be?

Read the full article at: theconversation.com

Antifragility as a complex system’s response to perturbations, volatility, and time

Complexity Digest - Wed, 01/10/2024 - 13:09

Cristian Axenie, Oliver López-Corona, Michail A. Makridis, Meisam Akbarzadeh, Matteo Saveriano, Alexandru Stancu, Jeffrey West

Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system’s output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems’ antifragility. We frame this review within three scales common to technical systems: intrinsic (input-output nonlinearity), inherited (extrinsic environmental signals), and interventional (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility-adaptiveness-resilience-robustness-antifragility, the principles behind it, and its practical implications.

Read the full article at: arxiv.org

A tiny fraction of all species forms most of nature: Rarity as a sticky state

Complexity Digest - Wed, 01/10/2024 - 08:21

Egbert H. van Nes, Diego G. F. Pujoni, Sudarshan A. Shetty, Gerben Straatsma, Willem M. de Vos, Marten Scheffer

PNAS 121 (2) e2221791120

Data from the human microbiome as well as communities of flies, rodents, fish, trees, plankton, and fungi suggest that consistently a tiny fraction of the species accounts for most of the biomass. We suggest that this may be due to an overlooked phenomenon that we call “stickiness” of rarity. This can arise in groups of species that are equivalent in resource use but differ in their response to stochastic stressors such as weather extremes and disease outbreaks. Stickiness is not absolute though. In our simulations, as well as natural time series from microbial communities, rare species occasionally replace dominant ones that collapse, supporting the insurance theory of biodiversity. Rare species may play an important role as backups stabilizing ecosystem functioning.

Read the full article at: www.pnas.org

Infodynamics, a Review

Complexity Digest - Tue, 01/09/2024 - 15:17

Klaus Jaffe

A review of studies on the interaction of information with the physical world found no fundamental contradiction between the eighth authors promoting Infodynamics. Each one emphasizes different aspects. The fact that energy requires information in order to produce work and that the acquisition of new information requires energy, triggers synergistic chain reactions producing increases of negentropy (increases in Useful Information or decreases in Information Entropy) in living systems. Infodynamics aims to study feasible balances between energy and information using empirical methods. Getting information requires energy and so does separating useful information from noise. Producing energy requires information, but there is no direct proportionality between the energy required to produce the information and the energy unleashed by this information. Energy and information are parts of two separate realms of reality that are intimately entangled but follow different laws of nature. Infodynamics recognizes multiple forms and dimensions of information. Information can be the opposite of thermodynamic entropy (Negentropy), a trigger of Free Energy (Useful or Potentially Useful), a reserve (Redundant Information), Structural, Enformation, Intropy, Entangled, Encrypted Information or Noise. These are overlapping functional properties focusing on different aspects of Information. Studies on information entropy normally quantify only one of these dimensions. The challenge of Infodynamics is to design empirical studies to overcome these limitations. The working of sexual reproduction and its evolution through natural selection and its role in powering the continuous increase in information and energy in living systems might teach us how.

Read the full article at: www.qeios.com

Critical phenomena in complex networks: from scale-free to random networks

Complexity Digest - Tue, 01/09/2024 - 13:06

Alexander Nesterov & Pablo Héctor Mata Villafuerte

The European Physical Journal B Volume 96, article number 143, (2023)

Within the conventional statistical physics framework, we study critical phenomena in configuration network models with hidden variables controlling links between pairs of nodes. We obtain analytical expressions for the average node degree, the expected number of edges in the graph, and the Landau and Helmholtz free energies. We demonstrate that the network’s temperature controls the average node degree in the whole network. We also show that phase transition in an asymptotically sparse network leads to fundamental structural changes in the network topology. Below the critical temperature, the graph is completely disconnected; above the critical temperature, the graph becomes connected, and a giant component appears. Increasing temperature changes the degree distribution from power-degree for lower temperatures to a Poisson-like distribution for high temperatures. Our findings suggest that temperature might be an inalienable property of real networks.

Read the full article at: link.springer.com

SHEEP, a Signed Hamiltonian Eigenvector Embedding for Proximity

Complexity Digest - Mon, 01/08/2024 - 13:02

Shazia’Ayn Babul & Renaud Lambiotte 

Communications Physics volume 7, Article number: 8 (2024

Signed network embedding methods allow for a low-dimensional representation of nodes and primarily focus on partitioning the graph into clusters, hence losing information on continuous node attributes. Here, we introduce a spectral embedding algorithm for understanding proximal relationships between nodes in signed graphs, where edges can take either positive or negative weights. Inspired by a physical model, we construct our embedding as the minimum energy configuration of a Hamiltonian dependent on the distance between nodes and locate the optimal embedding dimension. We show through a series of experiments on synthetic and empirical networks, that our method (SHEEP) can recover continuous node attributes showcasing its main advantages: re-configurability into a computationally efficient eigenvector problem, retrieval of ground state energy which can be used as a statistical test for the presence of strong balance, and measure of node extremism, computed as the distance to the origin in the optimal embedding.

Read the full article at: www.nature.com

Complex Systems Summer School | Santa Fe Institute

Complexity Digest - Mon, 01/08/2024 - 10:04

Complex Systems Summer School (CSSS) offers an intensive four-week introduction to complex behavior in mathematical, physical, living, and social systems. CSSS brings together graduate students, postdoctoral fellows, and professionals to transcend disciplinary boundaries, take intellectual risks, and ask big questions about complex systems. The residential program comprises a series of lectures and workshops devoted to theory and tools, applications-focused seminars, and discussions with faculty and fellow participants. CSSS participants put what they learn from these didactic sessions into practice through group research projects, conducted throughout the program and often extending into manuscripts and longer-term collaborations. CSSS provides an unparalleled opportunity for early-career researchers to expand their professional networks, produce a novel research product, and gain valuable experience working in transdisciplinary teams.

More at: www.santafe.edu

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

Complexity Digest - Sun, 01/07/2024 - 17:02

Bing Yuan, Zhang Jiang, Aobo Lyu, Jiayun Wu, Zhipeng Wang, Mingzhe Yang, Kaiwei Liu, Muyun Mou, Peng Cui

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.

Read the full article at: arxiv.org

Scalable network reconstruction in subquadratic time

Complexity Digest - Sun, 01/07/2024 - 12:58

Tiago P. Peixoto
Network reconstruction consists in determining the unobserved pairwise couplings between N nodes given only observational data on the resulting behavior that is conditioned on those couplings — typically a time-series or independent samples from a graphical model. A major obstacle to the scalability of algorithms proposed for this problem is a seemingly unavoidable quadratic complexity of O(N2), corresponding to the requirement of each possible pairwise coupling being contemplated at least once, despite the fact that most networks of interest are sparse, with a number of non-zero couplings that is only O(N). Here we present a general algorithm applicable to a broad range of reconstruction problems that achieves its result in subquadratic time, with a data-dependent complexity loosely upper bounded by O(N3/2logN), but with a more typical log-linear complexity of O(Nlog2N). Our algorithm relies on a stochastic second neighbor search that produces the best edge candidates with high probability, thus bypassing an exhaustive quadratic search. In practice, our algorithm achieves a performance that is many orders of magnitude faster than the quadratic baseline, allows for easy parallelization, and thus enables the reconstruction of networks with hundreds of thousands and even millions of nodes and edges.

Read the full article at: arxiv.org

Ricard Sole on the Space of Cognitions – Sean Carroll

Complexity Digest - Sat, 01/06/2024 - 12:57

Octopuses, artificial intelligence, and advanced alien civilizations: for many reasons, it’s interesting to contemplate ways of thinking other than whatever it is we humans do. How should we think about the space of all possible cognitions? One aspect is simply the physics of the underlying substrate, the physical stuff that is actually doing the thinking. We are used to brains being solid — squishy, perhaps, but consisting of units in an essentially fixed array. What about liquid brains, where the units can move around? Would an ant colony count? We talk with complexity theorist Ricard Solé about complexity, criticality, and cognition.

Listen at: www.preposterousuniverse.com

The temporal and affective structure of living systems: A thermodynamic perspective

Complexity Digest - Thu, 01/04/2024 - 14:22

Mads J Dengsø

Adaptive Behavior Volume 32, Issue 1

Enactive approaches to cognitive science as well as contemporary accounts from neuroscience have argued that we need to reconceptualize the role of temporality and affectivity in minds. Far from being limited to special faculties, such as emotional mental states and timekeeping, these accounts argue that time and affect both constitute fundamental aspects of minds and cognition. If this is true, how should one conceptualize the relation between these two fundamental aspects? This paper offers a way to conceptualize and clarify the relation between temporality and affectivity when understood in this fundamental sense. In particular, the paper contributes to ongoing discussions of structural temporality and affectivity by combining enactive notions of self-maintenance with a thermodynamically informed view of the organization of living systems. In situating temporality and affectivity by way of their role for the maintenance of thermodynamic non-equilibrium, I will argue that temporality and affectivity should be regarded as two sides of the same coin—that is, two distinct ways of highlighting one and the same process. This process corresponds to the continued differentiation of organism and environment as functional poles of a living system. The temporal and affective structure of living systems may thus be seen as the warp and weft by which living systems maintain themselves in terms of thermodynamic non-equilibrium.

Read the full article at: journals.sagepub.com

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

Complexity Digest - Wed, 01/03/2024 - 14:01

Bing Yuan, Zhang Jiang, Aobo Lyu, Jiayun Wu, Zhipeng Wang, Mingzhe Yang, Kaiwei Liu, Muyun Mou, Peng Cui

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.

Read the full article at: arxiv.org

Coupled dynamics of endemic disease transmission and gradual awareness diffusion in multiplex networks

Complexity Digest - Tue, 01/02/2024 - 15:10

Qingchu Wu, Tarik Hadzibeganovic, and Xiao-Pu Han

Mathematical Models and Methods in Applied Sciences Vol. 33, No. 13, pp. 2785-2821 (2023)

Understanding the interplay between human behavioral phenomena and infectious disease dynamics has been one of the central challenges of mathematical epidemiology. However, socio-cognitive processes critical for the initiation of desired behavioral responses during an outbreak have often been neglected or oversimplified in earlier models. Combining the microscopic Markov chain approach with the law of total probability, we herein institute a mathematical model describing the dynamic interplay between stage-based progression of awareness diffusion and endemic disease transmission in multiplex networks. We analytically derived the epidemic thresholds for both discrete-time and continuous-time versions of our model, and we numerically demonstrated the accuracy of our analytic arguments in capturing the time course and the steady state of the coupled disease-awareness dynamics. We found that our model is exact for arbitrary unclustered multiplex networks, outperforming a widely adopted probability-tree-based method, both in the prediction of the time-evolution of a contagion and in the final epidemic size. Our findings show that informing the unaware individuals about the circulating disease will not be sufficient for the prevention of an outbreak unless the distributed information triggers strong awareness of infection risks with adequate protective measures, and that the immunity of highly-aware individuals can elevate the epidemic threshold, but only if the rate of transition from weak to strong awareness is sufficiently high. Our study thus reveals that awareness diffusion and other behavioral parameters can nontrivially interact when producing their effects on epidemiological dynamics of an infectious disease, suggesting that future public health measures should not ignore this complex behavioral interplay and its influence on contagion transmission in multilayered networked systems.

Read the full article at: www.worldscientific.com

Jumpstart Postdoctoral Opportunity @ FAU’s Center for Complex Systems

Complexity Digest - Tue, 01/02/2024 - 14:45

The Human Brain & Behavior Laboratory (HBBL) located in FAU’s Center for Complex Systems in the Charles E. Schmidt College of Science (Valery Forbes, Dean) seeks an excellent candidate to join our team to investigate the roots of biological agency (see recent article in PNAS). HBBL explores how sentient agency emerges through self-organizing, coordinative processes that span organisms and environments. The postdoctoral fellow will assist in developing next-generation interactive systems for empirical study, conducting experiments with human infants (involving 3D motion capture, EEG, eye tracking) and analyzing data. The post-holder will have freedom and capacity to develop their own scientific ideas through experimental design and preparing manuscripts and grant applications. This position offers opportunity to participate in HBBL’s active collaborations with world-leading teams in Artificial Intelligence analysis (Intelligent Systems Research Centre, Ulster University, The Institute for the Augmented Human, University of Bath), in mathematical modeling using Active Inference (Welcome Centre for Human Neuroimaging, Univ. College London) and Coordination Dynamics frameworks (Institut de Neurosciences des Systèmes, Aix Marseille University). Qualified candidates will possess advanced technical skills in relevant areas such as coding (Matlab, Python, etc.), machine learning, signal processing, mathematical modeling, EEG analysis, motion capture, and electrical engineering/robotics, as well as creativity, curiosity, and a collaborative spirit. HBBL is directed by Glenwood and Martha Creech Eminent Scholar in Science J.A. Scott Kelso. Prof. Kelso founded FAU’s Center for Complex Systems in 1985 with the goal of bringing scientists from different disciplines together in one place to understand the multiscale structure, function, and dynamics of complex biological systems, including human beings and their activities.
Please forward Letter of Interest to Dr Aliza Sloan (asloan2014@fau.edu) indicating qualifications, CV and names and contact information of 2 Referees as soon as possible. The expected salary will follow the current NIH postdoc salary scale plus benefits. The position will be for 2 years, assuming satisfactory progress in year one, and may be extended further depending on funding. Position start date is May to August 2024.

Making sense of chemical space network shows signs of criticality

Complexity Digest - Tue, 01/02/2024 - 13:59

Nicola Amoroso, Nicola Gambacorta, Fabrizio Mastrolorito, Maria Vittoria Togo, Daniela Trisciuzzi, Alfonso Monaco, Ester Pantaleo, Cosimo Damiano Altomare, Fulvio Ciriaco & Orazio Nicolotti 

Scientific Reports volume 13, Article number: 21335 (2023)

Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.

Read the full article at: www.nature.com

Systems Medicine: Physiological Circuits and the Dynamics of Disease, by Uri Alon

Complexity Digest - Sat, 12/23/2023 - 17:45

Why do we get certain diseases, whereas other diseases do not exist?

In this book, Alon, one of the founders of systems biology, builds a foundation for systems medicine.

Starting from basic laws, the book derives why physiological circuits are built the way they are. The circuits have fragilities that explain specific diseases and offer new strategies to treat them.

By the end, the reader will be able to use simple and powerful mathematical models to describe physiological circuits. The book explores, in three parts, hormone circuits, immune circuits, and aging and age-related disease. It culminates in a periodic table of diseases.

Alon writes in a style accessible to a broad range of readers – undergraduates, graduates, or researchers from computational or biological backgrounds. The level of math is friendly and the math can even be bypassed altogether. For instructors and readers who want to go deeper, the book includes dozens of exercises that have been rigorously tested in the classroom

More at: www.taylorfrancis.com

Algorithms for seeding social networks can enhance the adoption of a public health intervention in urban India

Complexity Digest - Sat, 12/23/2023 - 13:43

Marcus Alexander, Laura Forastiere, Swati Gupta, and Nicholas A. Christakis

PNAS

A deep understanding of social networks can be used to create an artificial tipping point, changing population behavior by fostering behavioral cascades. Here, we experimentally test this proposition. We show that network-based targeting substantially increases population-level adoption of new behaviors. In part, this works by driving indirect treatment effects among the nontargeted members of the population (among people who were not initially part of the treatment group but who were affected by treatment of others in their population). The techniques we demonstrate can be easily implemented in global health (and elsewhere), as they do not require knowledge of the whole network. The novel pair-targeting technique explored here is particularly powerful and easy to implement.

Read the full article at: www.pnas.org

Decentralized traffic management of autonomous drones

Complexity Digest - Fri, 12/22/2023 - 17:25

Boldizsár Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz, Gábor Vásárhelyi

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task – filled with conflicts – is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a circular area with a radius of 125 meters.

Read the full article at: arxiv.org

AssemblyCA: A Benchmark of Open-Endedness for Discrete Cellular Automata

Complexity Digest - Fri, 12/22/2023 - 15:15

Keith Yuan Patarroyo, Abhishek Sharma, Sara Walker, Lee Cronin

We introduce AssemblyCA, a framework for utilizing cellular automata(CA) designed to benchmark the potential of open-ended processes. The benchmark quantifies the open-endedness of a system composed of resources, agents interacting with CAs, and a set of generated artifacts. We quantify the amount of open-endedness by taking the generated artifacts or objects and analyzing them using the tools of assembly theory(AT). Assembly theory can be used to identify selection in systems that produce objects that can be decomposable into atomic units, where these objects can exist in high copy numbers. By combining an assembly space measure with the copy number of an object we can quantify the complexity of objects that have a historical contingency. Moreover, this framework allows us to accurately quantify the indefinite generation of novel, diverse, and complex objects, the signature of open-endedness. We benchmark different measures from the assembly space with standard diversity and complexity measures that lack historical contingency. Finally, the open-endedness of three different systems is quantified by performing an undirected exploration in two-dimensional life-like CA, a cultural exploration provided by human experimenters, and an algorithmic exploration by a set of programmed agents.

Read the full article at: openreview.net

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