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Self-organized multistability in the forest fire model

Thu, 07/29/2021 - 15:04

Diego Rybski, Van Butsic, and Jan W. Kantelhardt

Phys. Rev. E 104, L012201 – Published 29 July 2021

The forest fire model in statistical physics represents a paradigm for systems close to but not completely at criticality. For large tree growth probabilities p we identify periodic attractors, where the tree density ρ oscillates between discrete values. For lower p this self-organized multistability persists with incrementing numbers of states. Even at low p the system remains quasiperiodic with a frequency ≈p on the way to chaos. In addition, the power-spectrum shows 1/f^2 scaling (Brownian noise) at the low frequencies f, which turns into white noise for very long simulation times.

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Global Disaster Coming? Earth’s ‘Vital Signs’ are Worsening Rapidly as Humanity’s Impact Deepens

Thu, 07/29/2021 - 13:19

The global economy’s business-as-usual approach to climate change has seen Earth’s “vital signs” deteriorate to record levels, an influential group of scientists said Wednesday, warning that several climate tipping points were now imminent. The researchers, part of a group of more than 14,000 scientists who have signed on to an initiative declaring a worldwide climate emergency, said that governments had consistently failed to address the root cause of climate change: “the overexploitation of the Earth”.

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Modelling and measuring open-endedness

Thu, 07/29/2021 - 12:43

Susan Stepney

Generating open-ended (OE) systems is a major and as yet unachieved goal of ALife research. Here I discuss aspects of defining, modelling, and measuring OE. I apply a simple model of OE to itself, thereby expanding the concept, to demonstrate how truly open and vast open-endedness is.

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Heterogeneity-stabilized homogeneous states in driven media

Tue, 07/27/2021 - 16:46

Z.G. Nicolaou, D.J. Case, E.B. van der Wee, M.M. Driscoll, and A.E. Motter ,
Nature Communications 12, 4486 (2021).
Understanding the relationship between symmetry breaking, system properties, and instabilities has been a problem of longstanding scientific interest. Symmetry-breaking instabilities underlie the formation of important patterns in driven systems, but there are many instances in which such instabilities are undesirable. Using parametric resonance as a model process, here we show that a range of states that would be destabilized by symmetry-breaking instabilities can be preserved and stabilized by the introduction of suitable system asymmetry. Because symmetric states are spatially homogeneous and asymmetric systems are spatially heterogeneous, we refer to this effect as heterogeneity-stabilized homogeneity. We illustrate this effect theoretically using driven pendulum array models and demonstrate it experimentally using Faraday wave instabilities. Our results have potential implications for the mitigation of instabilities in engineered systems and the emergence of homogeneous states in natural systems with inherent heterogeneities.

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Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence

Tue, 07/27/2021 - 12:42

Moritz U.G. Kraemer, et al.

Science  22 Jul 2021:
DOI: 10.1126/science.abj0113

Understanding the causes and consequences of the emergence of SARS-CoV-2 variants of concern is crucial to pandemic control yet difficult to achieve, as they arise in the context of variable human behavior and immunity. We investigate the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based PCR data. We identify a multi-stage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7’s increased intrinsic transmissibility. We further explore how B.1.1.7 spread was shaped by non-pharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.

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A Statistical Model of Word Rank Evolution

Tue, 07/27/2021 - 10:51

Alex John Quijano, Rick Dale, Suzanne Sindi
The availability of large linguistic data sets enables data-driven approaches to study linguistic change. This work explores the word rank dynamics of eight languages by investigating the Google Books corpus unigram frequency data set. We observed the rank changes of the unigrams from 1900 to 2008 and compared it to a Wright-Fisher inspired model that we developed for our analysis. The model simulates a neutral evolutionary process with the restriction of having no disappearing words. This work explains the mathematical framework of the model – written as a Markov Chain with multinomial transition probabilities – to show how frequencies of words change in time. From our observations in the data and our model, word rank stability shows two types of characteristics: (1) the increase/decrease in ranks are monotonic, or (2) the average rank stays the same. Based on our model, high-ranked words tend to be more stable while low-ranked words tend to be more volatile. Some words change in ranks in two ways: (a) by an accumulation of small increasing/decreasing rank changes in time and (b) by shocks of increase/decrease in ranks. Most of the stopwords and Swadesh words are observed to be stable in ranks across eight languages. These signatures suggest unigram frequencies in all languages have changed in a manner inconsistent with a purely neutral evolutionary process.

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Handbook of Cities and Networks

Mon, 07/26/2021 - 14:01

Edited by Zachary P. Neal and Céline Rozenblat

This Handbook of Cities and Networks provides a cutting-edge overview of research on how economic, social and transportation networks affect processes both in and between cities. Exploring the ways in which cities connect and intertwine, it offers a varied set of collaborations, highlighting different theoretical, historical and methodological perspectives.

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ALIFE 2021: The 2021 Conference on Artificial Life

Mon, 07/26/2021 - 11:07

Jitka Čejková, Silvia Holler, Lisa Soros, Olaf Witkowski (Eds)

MIT Press

The theme of ALIFE 2021 conference is ”Robots: The century past and the century ahead”, because we celebrate the centenary of Čapek’s R.U.R. and the worldwide-used word “robot”, which comes from this play. The conference was originally scheduled to be held in Prague, the city where the play had its official world premiere in 1921. However, because of the covid-19 pandemic and its repercussions, ALIFE 2021 conference is virtual.

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Engineering self-organized criticality in living cells

Sun, 07/25/2021 - 10:37

Blai Vidiella, Antoni Guillamon, Josep Sardanyés, Victor Maull, Jordi Pla, Nuria Conde & Ricard Solé 

Nature Communications volume 12, Article number: 4415 (2021)

Complex dynamical fluctuations, from intracellular noise, brain dynamics or computer traffic display bursting dynamics consistent with a critical state between order and disorder. Living close to the critical point has adaptive advantages and it has been conjectured that evolution could select these critical states. Is this the case of living cells? A system can poise itself close to the critical point by means of the so-called self-organized criticality (SOC). In this paper we present an engineered gene network displaying SOC behaviour. This is achieved by exploiting the saturation of the proteolytic degradation machinery in E. coli cells by means of a negative feedback loop that reduces congestion. Our critical motif is built from a two-gene circuit, where SOC can be successfully implemented. The potential implications for both cellular dynamics and behaviour are discussed.

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Historical language records reveal a surge of cognitive distortions in recent decades

Fri, 07/23/2021 - 20:56

Johan Bollen, Marijn ten Thij, Fritz Breithaupt, Alexander T. J. Barron, Lauren A. Rutter, Lorenzo Lorenzo-Luaces, and Marten Scheffer

PNAS July 27, 2021 118 (30) e2102061118;

Can entire societies become more or less depressed over time? Here, we look for the historical traces of cognitive distortions, thinking patterns that are strongly associated with internalizing disorders such as depression and anxiety, in millions of books published over the course of the last two centuries in English, Spanish, and German. We find a pronounced “hockey stick” pattern: Over the past two decades the textual analogs of cognitive distortions surged well above historical levels, including those of World War I and II, after declining or stabilizing for most of the 20th century. Our results point to the possibility that recent socioeconomic changes, new technology, and social media are associated with a surge of cognitive distortions.

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Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

Thu, 07/22/2021 - 12:43

Jordan C. Rozum, Jorge Gómez Tejeda Zañudo, Xiao Gan, Dávid Deritei and Réka Albert
Science Advances  16 Jul 2021:
Vol. 7, no. 29, eabf8124
DOI: 10.1126/sciadv.abf8124

We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system’s relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.

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Nowcasting transmission and suppression of the Delta variant of SARS-CoV-2 in Australia

Wed, 07/21/2021 - 10:49

Sheryl L. Chang, Oliver M. Cliff, Mikhail Prokopenko
As of July 2021, there is a continuing outbreak of the B.1.617.2 (Delta) variant of SARS-CoV-2 in Sydney, Australia. The outbreak is of major concern as the Delta variant is estimated to have twice the reproductive number to previous variants that circulated in Australia in 2020, which is worsened by low levels of acquired immunity in the population. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, in terms of both mitigation (case isolation, home quarantine) and suppression (school closures, social distancing). Our nowcasting modelling indicated that the level of social distancing currently attained in Sydney is inadequate for the outbreak control. A counter-factual analysis suggested that if 80% of agents comply with social distancing, then at least a month is needed for the new daily cases to reduce from their peak to below ten. A small reduction in social distancing compliance to 70% lengthens this period to over two months.

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Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter

Wed, 07/21/2021 - 09:29

Thayer Alshaabi, Jane L. Adams, Michael V. Arnold, Joshua R. Minot, David R. Dewhurst, Andrew J. Reagan, Christopher M. Danforth, and Peter Sheridan Dodds

Science Advances  16 Jul 2021:

Vol. 7, no. 29, eabe6534
DOI: 10.1126/sciadv.abe6534

In real time, Twitter strongly imprints world events, popular culture, and the day-to-day, recording an ever-growing compendium of language change. Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularity and spreading through retweets. Here, we describe Storywrangler, an ongoing curation of over 100 billion tweets containing 1 trillion 1-grams from 2008 to 2021. For each day, we break tweets into 1-, 2-, and 3-grams across 100+ languages, generating frequencies for words, hashtags, handles, numerals, symbols, and emojis. We make the dataset available through an interactive time series viewer and as downloadable time series and daily distributions. Although Storywrangler leverages Twitter data, our method of tracking dynamic changes in n-grams can be extended to any temporally evolving corpus. Illustrating the instrument’s potential, we present example use cases including social amplification, the sociotechnical dynamics of famous individuals, box office success, and social unrest.

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Structure of the Region-Technology Network as a Driver for Technological Innovation

Tue, 07/20/2021 - 15:16

Dion R. J. O’Neale, Shaun C. Hendy, and Demival Vasques Filho

Front. Big Data, 14 July 2021

Agglomeration and spillovers are key phenomena of technological innovation, driving regional economic growth. Here, we investigate these phenomena through technological outputs of over 4,000 regions spanning 42 countries, by analyzing more than 30 years of patent data (approximately 2.7 million patents) from the European Patent Office. We construct a bipartite network—based on revealed comparative advantage—linking geographic regions with areas of technology and compare its properties to those of artificial networks using a series of randomization strategies, to uncover the patterns of regional diversity and technological ubiquity. Our results show that the technological outputs of regions create nested patterns similar to those of ecological networks. These patterns suggest that regions need to dominate various technologies first (those allegedly less sophisticated), creating a diverse knowledge base, before subsequently developing less ubiquitous (and perhaps more sophisticated) technologies as a consequence of complementary knowledge that facilitates innovation. Finally, we create a map—the Patent Space Network—showing the interactions between technologies according to their regional presence. This network reveals how technology across industries co-appear to form several explicit clusters, which may aid future works on predicting technological innovation due to agglomeration and spillovers.

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The Crisis of Democracy in the Age of Cities conference

Tue, 07/20/2021 - 10:29

Tel Aviv University’s City Center is proud to invite you to the Crisis of Democracy in the Age of Cities,
​an international online conference.
The conference will last 3 consecutive days, from August 31st to September 2nd.
The Aim of the conference is to examine the links between the crisis of democracy with its tension between “non-democratic liberalism’ vs “non-liberal democracy’ and, the 21st century as the age of cities, in which the various properties of cities and urbanism dominate life. This, at the background of Industry 4.0, the Anthropocene, globalization and the COVID-19 pandemic.

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Melanie Mitchell Trains AI to Think With Analogies

Mon, 07/19/2021 - 15:18

Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.

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The fundamental theorem of natural selection

Mon, 07/19/2021 - 12:50

John Baez

Suppose we have n different types of self-replicating entity, with the population P_i of the ith type changing at a rate equal to P_i times the fitness f_i of that type. Suppose the fitness f_i is any continuous function of all the populations P_1, \dots, P_n. Let p_i be the fraction of replicators that are of the ith type. Then p = (p_1, \dots, p_n) is a time-dependent probability distribution, and we prove that its speed as measured by the Fisher information metric equals the variance in fitness. In rough terms, this says that the speed at which information is updated through natural selection equals the variance in fitness. This result can be seen as a modified version of Fisher’s fundamental theorem of natural selection. We compare it to Fisher’s original result as interpreted by Price, Ewens and Edwards.

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A network model of labor market dynamics

Sat, 07/17/2021 - 20:14

Oxford Mathematicians and Economists Maria del Rio-Chanona, Penny Mealy, Mariano Beguerisse-Díaz, François Lafond, and J. Doyne Farmer discuss their network model of labor market dynamics.

“Mathematics has explained many physical, chemical, and biological phenomena, but can it explain how the economy works? It is challenging because the economy is highly diverse, and ever-changing, with both short term fluctuations – it goes through recession and recovery periods – and long-term structural change – innovation transforms the scope and diversity of what we do.

Take the labor market, for example. Figure 1 shows what we call the occupational mobility network (1) – each node is an occupation, and the links show how likely it is that a worker in an occupation moves to another occupation. Clearly, there are many different occupations, and some occupational transitions are more likely than others. How can we model the dynamics of the labor market while taking this into account (click figure to enlarge)?

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Collective decision-making in living and artificial systems: editorial

Sat, 07/17/2021 - 12:56

Special issue on “Collective decision-making in living and artificial systems”
Swarm Intelligence, volume 15, issue 1–2 (2021)
Edited by A. Reina, E. Ferrante & G. Valentini

Collective decision-making is a fundamental cognitive process required for group coordination. Typically, this process requires individuals in a group to either reach a consensus on one of several available options or to distribute their workforce over different tasks. Similar collective decision-making processes can be found in a large number of systems, motivating a vast modeling effort across scientific disciplines. It can be observed across scales in a variety of animal groups, from unicellular organisms, to social insects, fish schools, and groups of mammals. In the social sciences, scientific domains such as econophysics and sociophysics emerged to investigate collective decisions in humans, deepening our understanding of the dynamics of economies and social policies. Neuroscientists also look at brains as a collection of neurons that, through numerous interactions, lead to rational decisions. Studies of collective decision-making in nature inspired the engineering of decentralized cyber-physical systems such as robot swarms and wireless sensor networks with the potential to create new emerging and disruptive technologies. Collective decision-making, ubiquitous across living and artificial collectives, can benefit from an interdisciplinary approach as apparently different systems may share similar mechanisms. With this special issue, we aim to push forward such an interdisciplinary approach by providing perspectives and insights from biology, information science, and engineering.

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Mathematicians Prove Symmetry of Phase Transitions

Fri, 07/16/2021 - 20:09

A group of mathematicians has shown that at critical moments, a symmetry called rotational invariance is a universal property across many physical systems.

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