Feed aggregator

Sustainability | Special Issue : Economic Complexity and Sustainability

Complexity Digest - Tue, 05/19/2020 - 09:49

During the last decade, economic development efforts have been marked by both a return of industrial policy [1–3] and the growing need to consider social and environmental sustainability [4–7]. At the intersection of both of these topics, we find important policy efforts, such as Europe’s Green Deal [8], and also a growing academic literature on economic complexity [9,10], green growth [11], green innovation [12,13], and sustainability. On the one hand, this literature is exploring how the product space [3] and the principle of relatedness [14] can facilitate an economy’s transition into green products [15–18]. On the other hand, this literature is exploring the connection between environmental sustainability and the complexity of an economy [19–22]. In fact, evidence thus far shows that economies tend to reduce emissions when they become sufficiently complex [21–23], and also, that higher complexity economies tend to experience lower levels of income inequality [7] and higher levels of human development [24].

The purpose of this Special Issue is to stimulate, promote, and gather research at the intersection between environmental sustainability, social sustainability, and economic complexity. We are looking for contributions exploring these and other topics:

 

  • Relatedness and the development of green products/jobs/industries;
  • Sustainability and global value chains;
  • Economic complexity, environmental sustainability, and the environmental Kuznets curve;
  • Economic complexity, inequality, and sustainable human development;
  • Green Growth;
  • Green Innovation.

Source: www.mdpi.com

What Is a Complex System?

Complexity Digest - Mon, 05/18/2020 - 13:43

A clear, concise introduction to the quickly growing field of complexity science that explains its conceptual and mathematical foundations

What is a complex system? Although “complexity science” is used to understand phenomena as diverse as the behavior of honeybees, the economic markets, the human brain, and the climate, there is no agreement about its foundations. In this introduction for students, academics, and general readers, philosopher of science James Ladyman and physicist Karoline Wiesner develop an account of complexity that brings the different concepts and mathematical measures applied to complex systems into a single framework. They introduce the different features of complex systems, discuss different conceptions of complexity, and develop their own account. They explain why complexity science is so important in today’s world.

Source: yalebooks.yale.edu

Computational Social Science and Sociology

Complexity Digest - Sun, 05/17/2020 - 11:57

Achim Edelmann, Tom Wolff, Danielle Montagne, and Christopher A. Bail

Annual Review of Sociology, Volume 46

 

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; ( f ) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

Source: www.annualreviews.org

The transsortative structure of networks

Complexity Digest - Sat, 05/16/2020 - 11:51

Shin-Chieng Ngo, Allon G. Percus, Keith Burghardt and Kristina Lerman

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

Volume 476 Issue 2237

 

Network topologies can be highly non-trivial, due to the complex underlying behaviours that form them. While past research has shown that some processes on networks may be characterized by local statistics describing nodes and their neighbours, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We define a property called transsortativity that describes correlations among a node’s neighbours. Transsortativity can be systematically varied, independently of the network’s degree distribution and assortativity. Moreover, it can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion. Our work improves our ability to create and analyse more realistic models of complex networks.

Source: royalsocietypublishing.org

Evolution of cooperation on temporal networks

Complexity Digest - Fri, 05/15/2020 - 12:04

Aming Li, Lei Zhou, Qi Su, Sean P. Cornelius, Yang-Yu Liu, Long Wang & Simon A. Levin 
Nature Communications volume 11, Article number: 2259 (2020)

 

Population structure is a key determinant in fostering cooperation among naturally self-interested individuals in microbial populations, social insect groups, and human societies. Traditional research has focused on static structures, and yet most real interactions are finite in duration and changing in time, forming a temporal network. This raises the question of whether cooperation can emerge and persist despite an intrinsically fragmented population structure. Here we develop a framework to study the evolution of cooperation on temporal networks. Surprisingly, we find that network temporality actually enhances the evolution of cooperation relative to comparable static networks, despite the fact that bursty interaction patterns generally impede cooperation. We resolve this tension by proposing a measure to quantify the amount of temporality in a network, revealing an intermediate level that maximally boosts cooperation. Our results open a new avenue for investigating the evolution of cooperation and other emergent behaviours in more realistic structured populations.

Source: www.nature.com

The online competition between pro- and anti-vaccination views

Complexity Digest - Fri, 05/15/2020 - 11:49

Neil F. Johnson, Nicolas Velásquez, Nicholas Johnson Restrepo, Rhys Leahy, Nicholas Gabriel, Sara El Oud, Minzhang Zheng, Pedro Manrique, Stefan Wuchty & Yonatan Lupu 
Nature (2020)

 

Distrust in scientific expertise1,2,3,4,5,6,7,8,9,10,11,12,13,14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2,3,4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9,10,11. There is a lack of understanding about how this distrust evolves at the system level13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.

Source: www.nature.com

When All Products Are Digital: Complexity and Intangible Value in the Ecosystem of Digitizing Firms 

Complexity Digest - Thu, 05/14/2020 - 14:02

Pouya Rahmati, Ali R. Tafti, J. Christopher Westland, César Hidalgo 

 

During the last four decades, digital technologies have disrupted many industries. Car control systems have gone from mechanical to digital. Telephones have changed from sound boxes to portable computers. But have the firms that digitized their products and services become more valuable than firms that didn’t? Here we introduce the construct of digital proximity, which considers the interdependent activities of firms linked in an economic network. We then explore how the digitization of products and services affects a company’s Tobin’s q—the ratio of market value over assets—a measure of the intangible value of a firm. Our panel regression methods and robustness tests suggest the positive influence of a firm’s digital proximity on its Tobin’s q. This implies that firms able to come closer to the digital sector have increased their intangible value compared to those that have failed to do so. These findings contribute a new way of measuring digitization and its impact on firm performance that is complementary to traditional measures of information technology (IT) intensity.

Source: papers.ssrn.com

Evolution is exponentially more powerful with frequency-dependent selection

Complexity Digest - Thu, 05/14/2020 - 11:45

Valiant (2009) proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype’s fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment or static fitness landscape. Valiant’s model distinguished families of environments that are “adaptable-to” from those that are not. But this model of evolution omits the vital ecological interactions between different evolving agents – it neglects the rich biotic environment that is central to the struggle for existence.

 

In this article, I extend algorithmic Darwinism to include the ecological dynamics of frequency-dependent selection as a population-dependent bias to the distribution of challenges that specify an environment. This extended algorithmic Darwinism replaces simple invasion of wild-type by a mutant-type of higher scalar fitness with an evolutionary game between wild-type and mutant-type based on their frequency-dependent fitness function. To analyze this model, I develop a game landscape view of evolution, as a generalization of the classic fitness landscape approach that is popular in biology.

 

I show that this model of eco-evo dynamics on game landscapes can provide an exponential speed-up over the purely evolutionary dynamics of the strict algorithmic Darwinism proposed by Valiant. In particular, I prove that the noisy-Parity environment – which is known to be not adaptable-to under strict algorithmic Darwinism (and conjectured to be not PAC-learnable) – is adaptable-to by eco-evo dynamics. Thus, the ecology of frequency-dependent selection does not just increase the tempo of evolution, but fundamentally transforms its mode.

 

The eco-evo dynamic for adapting to the noisy-Parity environment proceeds by two stages: (1) a quick stage of point-mutations that moves the population to one of exponentially many local fitness peaks; followed by (2) a slower stage where each ‘step’ follows a double-mutation by a point-mutation. This second stage allows the population to hop between local fitness peaks to reach the unique global fitness peak in polynomial time. The evolutionary game dynamics of finite populations are essential for finding a short adaptive path to the global fitness peak during the second stage of the adaptation process. This highlights the rich interface between computational learning theory, evolutionary games, and long-term evolution.

Source: www.biorxiv.org

Scaling and criticality in a phenomenological renormalization group

Complexity Digest - Thu, 05/14/2020 - 11:33

We present a systematic study to test a recently introduced phenomenological renormalization group, proposed to coarse-grain data of neural activity from their correlation matrix. The approach allows, at least in principle, to establish whether the collective behavior of the network of spiking neurons is described by a non-Gaussian critical fixed point. We test this renormalization procedure in a variety of models focusing in particular on the contact process, which displays an absorbing phase transition at λ = λ c between a silent and an active state. We find that the results of the coarse graining do not depend on the presence of long-range interactions and, overall, the method proves to be able to distinguish the critical regime from the supercritical one. However, some scaling features persist in the supercritical regime, at least for a finite system, as we see in a contact process above λ c . Our results provide both a systematic test of the method and insights on the possible subtleties that one needs to consider when applying such phenomenological approaches directly to data to infer signatures of criticality.

Source: link.aps.org

Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic

Complexity Digest - Tue, 05/12/2020 - 10:46

Alberto Aleta, David Martín-Corral, Ana Pastore y Piontti, Marco Ajelli, Maria Litvinova, Matteo Chinazzi, Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini, Jr., Stefano Merler, Alex Pentland, Alessandro Vespignani, Esteban Moro, Yamir Moreno

 

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.

Source: www.cidid.org

Cumulative effects of triadic closure and homophily in social networks

Complexity Digest - Mon, 05/11/2020 - 12:28

Social network structure has often been attributed to two network evolution mechanisms—triadic closure and choice homophily—which are commonly considered independently or with static models. However, empirical studies suggest that their dynamic interplay generates the observed homophily of real-world social networks. By combining these mechanisms in a dynamic model, we confirm the longheld hypothesis that choice homophily and triadic closure cause induced homophily. We estimate how much observed homophily in friendship and communication networks is amplified due to triadic closure. We find that cumulative effects of homophily amplification can also lead to the widely documented core-periphery structure of networks, and to memory of homophilic constraints (equivalent to hysteresis in physics). The model shows that even small individual bias may prompt network-level changes such as segregation or core group dominance. Our results highlight that individual-level mechanisms should not be analyzed separately without considering the dynamics of society as a whole.

 

Aili Asikainen, Gerardo Iñiguez, Javier Ureña-Carrión, Kimmo Kaski and Mikko Kivelä

Science Advances  08 May 2020:
Vol. 6, no. 19, eaax7310
DOI: 10.1126/sciadv.aax7310

Source: advances.sciencemag.org

Ninth International Conference on Complex Networks & Their Applications Madrid, Spain December 1- 3, 2020

Complexity Digest - Mon, 05/11/2020 - 12:25

The International Conference on Complex Networks and their Applications aims at bringing together researchers from different scientific communities working on areas related to complex networks. Two types of contributions are welcome: theoretical developments arising from practical problems, and case studies where methodologies are applied. Both contributions are aimed at stimulating the interaction between theoreticians and practitioners.

Source: complexnetworks.org

Modelling COVID-19

Complexity Digest - Sat, 05/09/2020 - 09:46

Alessandro Vespignani, Huaiyu Tian, Christopher Dye, James O. Lloyd-Smith, Rosalind M. Eggo, Munik Shrestha, Samuel V. Scarpino, Bernardo Gutierrez, Moritz U. G. Kraemer, Joseph Wu, Kathy Leung & Gabriel M. Leung 
Nature Reviews Physics (2020)

 

As the COVID-19 pandemic continues, mathematical epidemiologists share their views on what models reveal about how the disease has spread, the current state of play and what work still needs to be done.

Source: www.nature.com

A Class of Models with the Potential to Represent Fundamental Physics

Complexity Digest - Fri, 05/08/2020 - 16:31

Stephen Wolfram

 

A class of models intended to be as minimal and structureless as possible is introduced. Even in cases with simple rules, rich and complex behavior is found to emerge, and striking correspondences to some important core known features of fundamental physics are seen, suggesting the possibility that the models may provide a new approach to finding a fundamental theory of physics.

Source: arxiv.org

The race for coronavirus vaccines – a graphical guide

Complexity Digest - Thu, 05/07/2020 - 12:42

More than 90 vaccines are being developed against SARS-CoV-2 by research teams in companies and universities across the world. Researchers are trialling different technologies, some of which haven’t been used in a licensed vaccine before. At least six groups have already begun injecting formulations into volunteers in safety trials; others have started testing in animals. Nature’s graphical guide explains each vaccine design.

Source: www.nature.com

How Coronavirus Mutates and Spreads

Complexity Digest - Wed, 05/06/2020 - 12:41

The virus has mutated. But that doesn’t mean it’s getting deadlier.

 

At this point in the pandemic, coronavirus genomes with 10 or fewer mutations are common, and only a small number have over 20 mutations — which is still less than a tenth of a percent of the genome.

 

Over time, viruses can evolve into new strains — in other words, viral lineages that are significantly different from each other. Since January, researchers have sequenced many thousands of SARS-CoV-2 genomes and tracked all the mutations that have arisen. So far, they haven’t found compelling evidence that the mutations have had a significant change in how the virus affects us.

Source: www.nytimes.com

US Coronavirus Death Toll Is Far Higher Than Reported, CDC Data Suggests

Complexity Digest - Tue, 05/05/2020 - 11:58

Total deaths in seven states that have been hard hit by the coronavirus pandemic are nearly 50 percent higher than normal for the five weeks from March 8 through April 11 2020, according to new death statistics from the Centers for Disease Control and Prevention. That is 9,000 more deaths than were reported as of April 11 in official counts of deaths from the coronavirus.

 

The new data is partial and most likely undercounts the recent death toll significantly. But it still illustrates how the coronavirus is causing a surge in deaths in the places it has struck, probably killing more people than the reported statistics capture. These increases belie arguments that the virus is only killing people who would have died anyway from other causes. Instead, the virus has brought a pattern of deaths unlike anything seen in recent years.

 

If you look at the provisional deaths from all causes, death counts in New York, New Jersey, Michigan, Massachusetts, Illinois, Maryland and Colorado have spiked far above their normal levels for the period. In New York City, the home of the biggest outbreak, the number of deaths over this period is more than three times the normal number. Recent data suggests it could have reached six times higher than normal.

Source: www.nytimes.com

Towards Social Capital in a Network Organization: A Conceptual Model and an Empirical Approach

Complexity Digest - Tue, 05/05/2020 - 10:43

 Saad Alqithami, Rahmat Budiarto, Musaad Alzahrani and Henry Hexmoor

Entropy 2020, 22(5), 519

 

Due to the complexity of an open multi-agent system, agents’ interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one’s current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those agents self-organize themselves for a long-term objective. This paper, therefore, aims to provide an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interactions that, in part, elucidate evolving structures and impromptu topologies of networks. We model an open source project as an organizational network and provide definitions and formulations to correlate the proposed mechanism of social capital with the achievement of an organizational charter, for example, optimized productivity. To empirically evaluate our model, we conducted a case study of an open source software project to demonstrate how social capital can be created and measured within this type of organization. The results indicate that the values of social capital are positively proportional towards optimizing agents’ productivity into successful completion of the project.

Source: www.mdpi.com

Complexity Weekend: Virtual COVID-19 Hackathon | May 22-24, 2020

Complexity Digest - Tue, 05/05/2020 - 08:28

Team Up Against COVID-19

Meet new collaborators and learn Complexity Science by doing.

Help to address the unprecedented, interconnected problems created and exposed by this pandemic. Complexity Science is an interdisciplinary and inclusive framework for studying, designing, and controlling Complex systems. Over the course of one weekend, you will learn about Complexity Science from a variety of perspectives while developing solutions in a team setting to address:

Unemployment
Shelter in Place Policy
Testing
PPE
Supply Chains
Vaccine Research
Ventilator Shortage
Mental Health
Many other ongoing problems

Here’s what to expect during this weekend experience:

This event will feature Complexity Science-inspired lectures, discussions, and workshops on Friday night and Saturday day. All attendees will then engage in a collective brainstorming and team formation process Saturday afternoon, followed by a facilitated hackathon experience with these teams on Sunday.

Source: www.complexityweekend.com

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Complexity Digest - Sat, 05/02/2020 - 10:41

Juanjuan Zhang, Maria Litvinova, Yuxia Liang, Yan Wang, Wei Wang, Shanlu Zhao, Qianhui Wu, Stefano Merler, Cécile Viboud, Alessandro Vespignani, Marco Ajelli, Hongjie Yu

Science  29 Apr 2020:
eabb8001

 

Intense non-pharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-8-fold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0-14 years are less susceptible to SARS-CoV-2 infection than adults 15-64 years of age (odd ratio 0.34, 95%CI 0.24-0.49), while in contrast, individuals over 65 years are more susceptible to infection (odd ratio 1.47, 95%CI: 1.12-1.92). Based on these data, we build a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40-60% and delay the epidemic.

Source: science.sciencemag.org

Pages

Subscribe to Self-organizing Systems Lab aggregator