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Sonia Kéfi, Camille Saade, Eric L. Berlow, Juliano S. Cabral and Emanuel A. Fronhofer Philosophical Transactions of the Royal Society B-Biological Sciences; Vol.: 377; Issue: 1857; Article No.: 20210386 Anthropogenic activities are increasingly affecting ecosystems across the globe. Meanwhile, empirical and theoretical evidence suggest that natural systems can exhibit abrupt collapses in response to incremental increases in the stressors, sometimes with dramatic ecological and economic consequences. These catastrophic shifts are faster and larger than expected from the changes in the stressors and happen once a tipping point is crossed. The primary mechanisms that drive ecosystem responses to perturbations lie in their architecture of relationships, i.e. how species interact with each other and with the physical environment and the spatial structure of the environment. Nonetheless, existing theoretical work on catastrophic shifts has so far largely focused on relatively simple systems that have either few species and/or no spatial structure. This work has laid a critical foundation for understanding how abrupt responses to incremental stressors are possible, but it remains difficult to predict (let alone manage) where or when they are most likely to occur in more complex real-world settings. Here, we discuss how scaling up our investigations of catastrophic shifts from simple to more complex—species rich and spatially structured—systems could contribute to expanding our understanding of how nature works and improve our ability to anticipate the effects of global change on ecological systems. Read the full article at: royalsocietypublishing.org
Alice Patania, Antoine Allard, Jean-Gabriel Young
We study the problem of clustering networks whose nodes have imputed or physical positions in a single dimension, such as prestige hierarchies or the similarity dimension of hyperbolic embeddings. Existing algorithms, such as the critical gap method and other greedy strategies, only offer approximate solutions. Here, we introduce a dynamic programming approach that returns provably optimal solutions in polynomial time -- O(n^2) steps -- for a broad class of clustering objectives. We demonstrate the algorithm through applications to synthetic and empirical networks, and show that it outperforms existing heuristics by a significant margin, with a similar execution time. Read the full article at: arxiv.org
Scientists and philosophers have been attempting to define life for ages. In biology class we were taught to define life through the set of features that we, and every other species on the planet share. Things like movement, respiration, growth, and reproduction. Life is made of cells and has DNA. But does biochemistry constitute the whole picture? As far back as 1970, Carl Sagan didn’t think so. Attempts at defining life, he and many others thought, were too constrained by the characteristics of life as we know it. A single example of extraterrestrial life could change everything. Read the full article at: nautil.us
The Nobel Laureate Giorgio Parisi is interviewed by JPhys Complexity Editor-in-Chief, Ginestra Bianconi, on themes related to the 2021 Nobel Prize in Physics awarded to him for research on complex systems. Read the full article at: iopscience.iop.org
Thomas Schmickl Complexity Volume 2022 | Article ID 9956885
Predictions of emergent phenomena, appearing on the macroscopic layer of a complex system, can fail if they are made by a microscopic model. This study demonstrates and analyses this claim on a well-known complex system, Conway’s Game of Life. Straightforward macroscopic mean-field models are easily capable of predicting such emergent properties after they have been fitted to simulation data in an after-the-fact way. Thus, these predictions are macro-to-macro only. However, a micro-to-macro model significantly fails to predict correctly, as does the obvious mesoscopic modeling approach. This suggests that some macroscopic system properties in a complex dynamic system should be interpreted as examples of phenomena (properties) arising from “strong emergence,” due to the lack of ability to build a consistent micro-to-macro model, that could explain these phenomena in a before-the-fact way. The root cause for this inability to predict this in a micro-to-macro way is identified as the pattern formation process, a phenomenon that is usually classified as being of “weak emergence.” Ultimately, this suggests that it may be in principle impossible to discriminate between such distinct categories of “weak” and “strong” emergence, as phenomena of both types can be part of the very same feedback loop that mainly governs the system’s dynamics. Read the full article at: www.hindawi.com
Arsham Ghavasieh, Manlio De Domenico
Complex network states are characterized by the interplay between system's structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize. Read the full article at: arxiv.org
Josué Ely Molina, Jorge Flores, Carlos Gershenson, Carlos Pineda
A recent increase in data availability has allowed the possibility to perform different statistical linguistic studies. Here we use the Google Books Ngram dataset to analyze word flow among English, French, German, Italian, and Spanish. We study what we define as ``migrant words'', a type of loanwords that do not change their spelling. We quantify migrant words from one language to another for different decades, and notice that most migrant words can be aggregated in semantic fields and associated to historic events. We also study the statistical properties of accumulated migrant words and their rank dynamics. We propose a measure of use of migrant words that could be used as a proxy of cultural influence. Our methodology is not exempt of caveats, but our results are encouraging to promote further studies in this direction. Read the full article at: arxiv.org
Sean Kelty, Raiyan Abdul Baten, Adiba Mahbub Proma, Ehsan Hoque, Johan Bollen, Gourab Ghoshal
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 introduce three information-theoretic measures that quantify novelty, innovation, and impact from scholarly citation networks, and compare the scholarly output of scientists who are either not connected or strongly connected to superstar scientists. We find that while connected scientists do indeed publish more, garner more citations, and produce more diverse content, this comes at a cost of lower innovation and higher redundancy of ideas. Further, once one removes papers co-authored with superstars, the academic output of these connected scientists diminishes. In contrast, authors that produce innovative content without the benefit of collaborations with scientific superstars produce papers that connect a greater diversity of concepts, publish more, and have comparable citation rates, once one controls for 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: arxiv.org
Tomasz Korbak Adaptive Behavior 31(1) The notion of self-organisation plays a major role in enactive cognitive science. In this paper, I review several formal models of self-organisation that various approaches in modern cognitive science rely upon. I then focus on Rosen’s account of self-organisation as closure to efficient cause and his argument that models of systems closed to efficient cause – (M, R) systems – are uncomputable. Despite being sometimes relied on by enactivists this argument is problematic it rests on assumptions unacceptable for enactivists: that living systems can be modelled as time-invariant and material-independent. I then argue that there exists a simple and philosophically appealing reparametrisation of (M, R)–systems that accounts for the temporal dimensions of life but renders Rosen’s argument invalid. Read the full article at: journals.sagepub.com
Joshua Bongard, Michael Levin
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., tendency to oversimplify) and prior technological limitations in favor of a more continuous, gradualist view necessitated by the study of evolution, developmental biology, and intelligent machines. Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing" - the ability of the same substrate to simultaneously compute different things. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of computational materials as reported in the rapidly-growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events, as it has already done at quantum and relativistic scales. Here, we review examples of biological and technological polycomputing, and develop the idea that overloading of different functions on the same hardware is an important design principle that helps understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering. Read the full article at: arxiv.org
Diffusion models generate incredible images by learning to reverse the process that, among other things, causes ink to spread through water. Read the full article at: www.quantamagazine.org
Lucas Böttcher, Mason A. Porter
In many scientific applications, it is common to use binary (i.e., unweighted) edges in the study of networks to examine collections of entities that are either adjacent or not adjacent. Researchers have generalized such binary networks to incorporate edge weights, which allow one to encode node--node interactions with heterogeneous intensities or frequencies (e.g., in transportation networks, supply chains, and social networks). Most such studies have considered real-valued weights, despite the fact that networks with complex weights arise in fields as diverse as quantum information, quantum chemistry, electrodynamics, rheology, and machine learning. Many of the standard approaches from network science that originated in the study of classical systems and are based on real-valued edge weights cannot be applied directly to networks with complex edge weights. In this paper, we examine how standard network-analysis methods fail to capture structural features of networks with complex weights. We then generalize several network measures to the complex domain and show that random-walk centralities provide a useful tool to examine node importances in networks with complex weights. Read the full article at: arxiv.org
Alvaro Diaz-Ruelas Chaos 32, 123136 (2022) The incorporation of stochastic ingredients in models describing phenomena in all disciplines is now a standard in scientific practice. White noise is one of the most important of such stochastic ingredients. Although tools for identifying white and other types of noise exist,1,2 there is a permanent demand for reliable and robust statistical methods for analyzing data in order to distinguish noise and filter it from signals in experiments. Or in hypothesis tests, for assessing the plausibility of the outcome of an experiment being the result of randomness and not a significant, controllable effect. Due to its ubiquity in experiments and its mathematical simplicity, white noise is very often the most convenient stochastic component that adds realism to a dynamic model, commonly regarded as the noise polluting observations. It can be continuous or discrete both in time and in distribution, so it can be applied to many scenarios. It is a stationary and independent and identically distributed process, all relatively simple properties for a stochastic process. Here, we present a combinatorial perspective to study white noise inspired in the concept of ordinal patterns. Read the full article at: aip.scitation.org
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Kishore Vasan, Deisy Gysi, Albert-Laszlo Barabasi
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model of drug discovery. We demonstrate that the model is able to accurately recreate the exploration patterns observed in clinical trials. Most importantly, we show that a network-based search strategy can widen the scope of drug discovery by guiding exploration to novel proteins that are part of under explored regions in the human interactome. Read the full article at: arxiv.org
Chris Kang, Madelynn McElroy, and Nikolaos K. Voulgarakis Entropy 2023, 25(2), 235 Early embryonic development involves forming all specialized cells from a fluid-like mass of identical stem cells. The differentiation process consists of a series of symmetry-breaking events, starting from a high-symmetry state (stem cells) to a low-symmetry state (specialized cells). This scenario closely resembles phase transitions in statistical mechanics. To theoretically study this hypothesis, we model embryonic stem cell (ESC) populations through a coupled Boolean network (BN) model. The interaction is applied using a multilayer Ising model that considers paracrine and autocrine signaling, along with external interventions. It is demonstrated that cell-to-cell variability can be interpreted as a mixture of steady-state probability distributions. Simulations have revealed that such models can undergo a series of first- and second-order phase transitions as a function of the system parameters that describe gene expression noise and interaction strengths. These phase transitions result in spontaneous symmetry-breaking events that generate new types of cells characterized by various steady-state distributions. Coupled BNs have also been shown to self-organize in states that allow spontaneous cell differentiation. Read the full article at: www.mdpi.com
Theodor Cimpeanu, Francisco C Santos, The Anh Han
Humans have developed considerable machinery used at scale to create policies and to distribute incentives, yet we are forever seeking ways in which to improve upon these, our institutions. Especially when funding is limited, it is imperative to optimise spending without sacrificing positive outcomes, a challenge which has often been approached within several areas of social, life and engineering sciences. These studies often neglect the availability of information, cost restraints, or the underlying complex network structures, which define real-world populations. Here, we have extended these models, including the aforementioned concerns, but also tested the robustness of their findings to stochastic social learning paradigms. Akin to real-world decisions on how best to distribute endowments, we study several incentive schemes, which consider information about the overall population, local neighbourhoods, or the level of influence which a cooperative node has in the network, selectively rewarding cooperative behaviour if certain criteria are met. Following a transition towards a more realistic network setting and stochastic behavioural update rule, we found that carelessly promoting cooperators can often lead to their downfall in socially diverse settings. These emergent cyclic patterns not only damage cooperation, but also decimate the budgets of external investors. Our findings highlight the complexity of designing effective and cogent investment policies in socially diverse populations. Read the full article at: arxiv.org
Ginestra Bianconi et al 2023 J. Phys. Complex. 4 010201 The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems. Read the full article at: iopscience.iop.org
Ric Sims and Özlem Yilmaz Adaptive Behavior The tricky question in the plant cognition debate is what theory of cognition should be used to fix the reference of cognitive concepts without skewing the debate too much one way or the other. After all, plants are rather different to animals in many respects: they are not motile, do not possess central nervous systems or even neurons, do not exhibit an invariant morphology, interact with the world in a distributed multi-centred manner, and behave through changes in their physiology. Nonetheless, there is a significant strand in the debate that asserts that plants are indeed cognitive. But what theory of cognition makes sense of this claim without baking in prior zoological assumptions? The aim of this paper is to try out a theory of minimal cognition that makes the claim of plant cognition plausible. It is primarily inspired by the distributed cognition literature and the sensorimotor coordination theory of cognition proposed by van Duijn et al. (2006). We take a cognitive system to be a coordinated set of semi-autonomous processes running over the organism and items in its environment. Coordination is characterised in terms of two functional conditions that ensure that the system generates goal-directed action in the world. The system is stigmergic in the sense that the material results of its actions in the environment are a crucial part of the processes that coordinate further actions. The account possesses a degree of scale invariance and helps unify cognitive explanation across microorganisms, plants and animals. Read the full article at: journals.sagepub.com
Hannah E. Davis, Lisa McCorkell, Julia Moore Vogel & Eric J. Topol
Nature Reviews Microbiology (2023) Long COVID is an often debilitating illness that occurs in at least 10% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. More than 200 symptoms have been identified with impacts on multiple organ systems. At least 65 million individuals worldwide are estimated to have long COVID, with cases increasing daily. Biomedical research has made substantial progress in identifying various pathophysiological changes and risk factors and in characterizing the illness; further, similarities with other viral-onset illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome have laid the groundwork for research in the field. In this Review, we explore the current literature and highlight key findings, the overlap with other conditions, the variable onset of symptoms, long COVID in children and the impact of vaccinations. Although these key findings are critical to understanding long COVID, current diagnostic and treatment options are insufficient, and clinical trials must be prioritized that address leading hypotheses. Additionally, to strengthen long COVID research, future studies must account for biases and SARS-CoV-2 testing issues, build on viral-onset research, be inclusive of marginalized populations and meaningfully engage patients throughout the research process. Read the full article at: www.nature.com
Giorgio Matassi and Pedro Martinez Front. Ecol. Evol., 13 January 2023 Sec. Models in Ecology and Evolution In this review essay, we give a detailed synopsis of the twelve contributions which are collected in a Special Issue in Frontiers Ecology and Evolution, based on the research topic “Current Thoughts on the Brain-Computer Analogy—All Metaphors Are Wrong, But Some Are Useful.” The synopsis is complemented by a graphical summary, a matrix which links articles to selected concepts. As first identified by Turing, all authors in this Special Issue recognize semantics as a crucial concern in the brain-computer analogy debate, and consequently address a number of such issues. What is missing, we believe, is the distinction between metaphor and analogy, which we reevaluate, describe in some detail, and offer a definition for the latter. To enrich the debate, we also deem necessary to develop on the evolutionary theories of the brain, of which we provide an overview. This article closes with thoughts on creativity in Science, for we concur with the stance that metaphors and analogies, and their esthetic impact, are essential to the creative process, be it in Sciences as well as in Arts. Read the full article at: www.frontiersin.org
Arsham Ghavasieh, Manlio De Domenico
Complex network states are characterized by the interplay between system's structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize. Read the full article at: arxiv.org
Koen Siteur, Quan-Xing Liu, Vivi Rottschäfer, Tjisse van der Heide, Max Rietkerk, Arjen Doelman, Christoffer Boström, and Johan van de Koppel PNAS 120 (2) e2202683120 Human-induced environmental changes push ecosystems worldwide toward their limits. Therefore, there is a growing need for indicators to assess the resilience of ecosystems against external changes and disturbances. We highlight a novel class of spatial patterns in ecosystems for which resilience indicators are lacking and introduce a new indicator framework for these ecosystems, akin to the physics of phase separation. Our work suggests that aerial imagery can be used to monitor patchy ecosystems and highlights a link between physics and ecosystem resilience. Read the full article at: www.pnas.org
Randall D.Beer, Ezequiel A.Di Paolo Biosystems
Volume 223, January 2023, 104823 Enaction is an increasingly influential approach to cognition that grew out of Maturana and Varela's earlier work on autopoiesis and the biology of cognition. As with any relatively new scientific discipline, the enactive approach would benefit greatly from a careful analysis of its theoretical foundations. Here we initiate such an analysis for one of the core concepts of enaction, precariousness. Specifically, we consider three types of fragility: systemic, processual and thermodynamic. Using a glider in the Game of Life as a toy model, we illustrate each of these fragilities and examine the relationships between them. We also argue that each type of fragility is characterized by which aspects of a system are hardwired into its definition from the outset and which aspects are emergent and hence vulnerable to disintegration without ongoing maintenance. Read the full article at: www.sciencedirect.com
Cecile Philippe, Yaneer Bar-Yam, Stephane Bilodeau. Carlos Gershenson, Sunil K.Raina, Shu-Ti Chiou, Gunhild A. Nyborg, Matthias F.Schneider The Lancet Regional Health - Europe
Volume 25, February 2023, 100574 After a period where many countries have let the SARS-CoV-2 virus spread more or less freely, individuals and communities are now grappling with the many negative health effects and economic ramifications from high levels of illness over long periods. As evidence of the detrimental long-term effects of the virus mount, it is increasingly clear that the policy vacuum comes at an unacceptable price both in the short and long term; its only justification would be if there was no other alternative that did not come at an even greater cost. Entering the cold season, the number of infections will most likely increase significantly in Europe (≈ one – two order of magnitude in 2021). While the world awaits and hopes for new and more effective vaccines, we need tools in the toolbox that can effectively control transmission of rapidly spreading new variants, especially if more pathogenic. Otherwise, we may face significant disruptions and enormous costs due to repeated waves of illness, with each wave increasing the numbers of workers thrown out of the workforce from long term health effects. Lockdowns, due to their social restrictions and high short-term economic costs, are no longer the best available option. We here point out that mass testing (regular asymptomatic screening of the general population) is an alternative approach that can dramatically reduce cases and quickly restore economic and social activity. Read the full article at: www.sciencedirect.com
Marco Pangallo, Alberto Aleta, R. Maria del Rio Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani, J. Doyne Farmer
The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic. Read the full article at: arxiv.org
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