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Introduction to Urban Science

Complexity Digest - Fri, 08/13/2021 - 08:24

A novel, integrative approach to cities as complex adaptive systems, applicable to issues ranging from innovation to economic prosperity to settlement patterns.

Human beings around the world increasingly live in urban environments. In Introduction to Urban Science, Luis Bettencourt takes a novel, integrative approach to understanding cities as complex adaptive systems, claiming that they require us to frame the field of urban science in a way that goes beyond existing theory in such traditional disciplines as sociology, geography, and economics. He explores the processes facilitated by and, in many cases, unleashed for the first time by urban life through the lenses of social heterogeneity, complex networks, scaling, circular causality, and information.

Though the idea that cities are complex adaptive systems has become mainstream, until now those who study cities have lacked a comprehensive theoretical framework for understanding cities and urbanization, for generating useful and falsifiable predictions, and for constructing a solid body of empirical evidence so that the discipline of urban science can continue to develop. Bettencourt applies his framework to such issues as innovation and development across scales, human reasoning and strategic decision-making, patterns of settlement and mobility and their influence on socioeconomic life and resource use, inequality and inequity, biodiversity, and the challenges of sustainable development in both high- and low-income nations. It is crucial, says Bettencourt, to realize that cities are not “zero-sum games” and that knowledge, human cooperation, and collective action can build a better future.

More at: mitpress.mit.edu

Intelligence as information processing: brains, swarms, and computers

Complexity Digest - Thu, 08/12/2021 - 11:18

Carlos Gershenson
There is no agreed definition of intelligence, so it is problematic to simply ask whether brains, swarms, computers, or other systems are intelligent or not. To compare the potential intelligence exhibited by different cognitive systems, I use the common approach used by artificial intelligence and artificial life: Instead of studying the substrate of systems, let us focus on their organization. This organization can be measured with information. Thus, I apply an informationist epistemology to describe cognitive systems, including brains and computers. This allows me to frame the usefulness and limitations of the brain-computer analogy in different contexts. I also use this perspective to discuss the evolution and ecology of intelligence.

Read the full article at: arxiv.org

Systems Science, Cybernetics, and Complexity

Complexity Digest - Sun, 08/08/2021 - 12:33

Gary S. Metcalf and Stuart A. Kauffman

Systems science, cybernetics, and complexity all evolved out of concerns for understanding complex phenomena in science. They also share many of the same theoretical roots, as well as histories which converge across leading figures and places in time. They can be conceived as three realms which shared and competed for prominence. All have influenced and been incorporated into scientific disciplines, though much of the history has been forgotten by current generations. Those historical roots remain relevant and important to future progress in science. This chapter provides a brief summary of the history and foundations of these domains.

Read the full article at: link.springer.com

Science Is Political, and We Must Deal with It

Complexity Digest - Sat, 08/07/2021 - 06:23

Philip Ball

J. Phys. Chem. Lett. 2021, 12, 27, 6336–6340

The issue is not, then, whether and how science can resist being “politicized”, but how the political and ideological dimensions of science can best be managed to make it most effective and beneficial both as an intellectual quest and as a means of, as Bacon put it, relieving (hu)mankind’s estate.

Read the full article at: pubs.acs.org

Relation between Constitutions, Socioeconomics and The Rule of Law: a quantitative thermodynamic approach

Complexity Digest - Thu, 08/05/2021 - 12:49

Klaus Jaffe, Edrey Martinez, Ana Cecilia Soarez, Jose Gregorio Contreras, Juan C Correa, Antonio Canova
Based on what we know about thermodynamics of synergy, we explored the relationship between countries socio-cultural order (negentropy), estimated through their constitutions, indicators of Rule of Law and their academic development; with countries indicators of Free Energy (amount of useful work, productivity, socioeconomic health). The analysis of 219 indicators unveiled strong correlations between estimates of the Rule of Law and the number of Academic Publications, with the socioeconomic health indicators: GDP, Human Development Index and Infant Mortality. In contrast, correlations with the length of constitutions (number of words and of articles), suggest that the proliferation of legal rules hinders the rule of law and socioeconomic development, or that under-development and/or the lack of the rule of law foments the proliferation of legal rules. These findings suggest that not any order favors productivity (Free Energy) and that excess regulations and state tutelage increase social entropy decreasing socioeconomic health.

Read the full article at: arxiv.org

A new nature-inspired optimization for community discovery in complex networks

Complexity Digest - Tue, 08/03/2021 - 13:12

Xiaoyu Li, Chao Gao, Songxin Wang, Zhen Wang, Chen Liu & Xianghua Li 

The European Physical Journal B volume 94, Article number: 137 (2021)

The community structure, owing to its significant status, is of extraordinary significance in comprehending and detecting inherent functions in real networks. However, the community structures are always hard to be identified, and whether the existing algorithms are based on optimization or heuristics, the robustness and accuracy should be improved. The physarum (i.e., slime molds with multi heads) has proved its ability to produce foraging networks. Therefore, we adopt physarum so that the optimization-based community detection algorithms can work more efficiently. Specifically, a physarum-based network model (pnm), which is capable of identifying inter-edges of the community in a network, is used to optimize the prior knowledge of existing evolutional algorithms (i.e., genetic algorithm, particle swarm optimization algorithm and ant colony algorithm). the optimized algorithms have been compared with some advanced methods in synthetic and real networks. experimental results have verified the effectiveness of the proposed method.

Read the full article at: link.springer.com

When less is more: Robot swarms adapt better to changes with constrained communication

Complexity Digest - Mon, 08/02/2021 - 16:05

Mohamed S. Talamali, Arindam Saha, James A. R. Marshall, and Andreagiovanni Reina
Science Robotics 6(56): eabf1416
https://doi.org/10.1126/scirobotics.abf1416 
video: https://bcove.video/3zwyQpA 

You found a new better bar, with nicer drinks and healthier snacks, but you do not know how to convince your friends to deviate from their established favourite bar and take them to the new better place. Next time, you should consider convincing your friends one by one, rather than reaching out in the group chat. Recent research published today in Science Robotics, suggests that this strategy will increase your probability of convincing the entire group to choose the better bar.
The study has found that a population of naive individuals, when globally connected, can be unable to discard outdated beliefs and adopt better available alternatives. Instead, when the social network is sparse and individuals only share information locally, the population can effectively adapt to changes and reach an agreement in favour of the best option.
Researchers investigated how a swarm of autonomous robots could adapt to environmental changes and found the counterintuitive result that reduced social information would improve the spreading of localised information, and, in turn, allows an informed minority to effectively change the opinion of the entire group. This finding is opposed to the widely accepted and intuitive belief in network science that more connections lead to more effective information exchange. While information spreading speed may indeed increase, the study showed that adaptation—the ability to modify the group’s belief in light of new information—is impaired.

Read the full article at: robotics.sciencemag.org

Atlas of Forecasts: Modeling and Mapping Desirable Futures

Complexity Digest - Mon, 08/02/2021 - 10:36

Forecasting the future with advanced data models and visualizations.

To envision and create the futures we want, society needs an appropriate understanding of the likely impact of alternative actions. Data models and visualizations offer a way to understand and intelligently manage complex, interlinked systems in science and technology, education, and policymaking. Atlas of Forecasts, from the creator of Atlas of Science and Atlas of Knowledge, shows how we can use data to predict, communicate, and ultimately attain desirable futures.

Using advanced data visualizations to introduce different types of computational models, Atlas of Forecasts demonstrates how models can inform effective decision-making in education, science, technology, and policymaking. The models and maps presented aim to help anyone understand key processes and outcomes of complex systems dynamics, including which human skills are needed in an artificial intelligence–empowered economy; what progress in science and technology is likely to be made; and how policymakers can future-proof regions or nations. This Atlas offers a driver’s seat-perspective for a test-drive of the future.

More at: mitpress.mit.edu

Self-organized multistability in the forest fire model

Complexity Digest - 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.

Read the full article at: link.aps.org

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

Complexity Digest - 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”.

Read the full article at: phys.org

Modelling and measuring open-endedness

Complexity Digest - 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.

Read the full article at: workshops.alife.org

Heterogeneity-stabilized homogeneous states in driven media

Complexity Digest - 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).
https://www.nature.com/articles/s41467-021-24459-0
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.

Read the full article at: www.nature.com

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

Complexity Digest - Tue, 07/27/2021 - 12:42

Moritz U.G. Kraemer, et al.

Science  22 Jul 2021:
eabj0113
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.

Read the full article at: science.sciencemag.org

A Statistical Model of Word Rank Evolution

Complexity Digest - 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.

Read the full article at: arxiv.org

Handbook of Cities and Networks

Complexity Digest - 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.

More at: www.e-elgar.com

ALIFE 2021: The 2021 Conference on Artificial Life

Complexity Digest - 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.

Read the full proceedings at: direct.mit.edu

Engineering self-organized criticality in living cells

Complexity Digest - 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.

Read the full article at: www.nature.com

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

Complexity Digest - 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.

Read the full article at: www.pnas.org

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

Complexity Digest - 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.

Read the full article at: advances.sciencemag.org

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

Complexity Digest - 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.

Read the full article at: arxiv.org

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