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A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

5 hours 21 min ago

Saad Alqithami
Computers 2021, 10(1), 12

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

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Self-organized biotectonics of termite nests

5 hours 46 min ago

Alexander Heyde, Lijie Guo, Christian Jost, Guy Theraulaz, and L. Mahadevan
PNAS February 2, 2021 118 (5) e2006985118

Termite nests are a remarkable example of functional self-organization that show how structure and function emerge on multiple length and time scales in ecophysiology. To understand the process by which this arises, we document the labyrinthine architecture within the subterranean nests of the African termite Apicotermes lamani and develop a simple mathematical model that relies on the physical and biological interactions between termites, pheromones, and mud in the nest. Our model explains the formation of parallel floors connected by linear and helical ramps, consistent with observations of real nests. In describing this multiagent system, we elucidate principles of physical and behavioral coupling with relevance to swarm intelligence and architectural design.

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Combinatorial approach to spreading processes on networks

Mon, 01/18/2021 - 12:48

Dario Mazzilli & Filippo Radicchi
The European Physical Journal B volume 94, Article number: 15 (2021)

Stochastic spreading models defined on complex network topologies are used to mimic the diffusion of diseases, information, and opinions in real-world systems. Existing theoretical approaches to the characterization of the models in terms of microscopic configurations rely on some approximation of independence among dynamical variables, thus introducing a systematic bias in the prediction of the ground-truth dynamics. Here, we develop a combinatorial framework based on the approximation that spreading may occur only along the shortest paths connecting pairs of nodes. The approximation overestimates dynamical correlations among node states and leads to biased predictions. Systematic bias is, however, pointing in the opposite direction of existing approximations. We show that the combination of the two biased approaches generates predictions of the ground-truth dynamics that are more accurate than the ones given by the two approximations if used in isolation. We further take advantage of the combinatorial approximation to characterize theoretical properties of some inference problems, and show that the reconstruction of microscopic configurations is very sensitive to both the place where and the time when partial knowledge of the system is acquired.

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Survival of the Systems

Sun, 01/17/2021 - 16:16

Timothy M.Lenton, Timothy A.Kohler, Pablo A.Marquet, Richard A.Boyle, Michel Crucifix, David M.Wilkinson, Marten Scheffer

Trends Ecol. Evol.

Recent theoretical progress highlights that natural selection can occur based solely on differential persistence of biological entities, without the need for conventional replication.

This calls for a reconsideration of how ecosystems and social (-ecological) systems can evolve, based on identifying system-level properties that affect their persistence.

Feedback cycles have irreducible properties arising from the interactions of unrelated components, and are critical to determining ecosystem and social system persistence.

Self-perpetuating feedbacks involving the acquisition and recycling of resources, alteration of local environmental conditions, and amplification of disturbance factors, enhance ecosystem and social system spread and persistence.

Cycles built from the by-products of traits, naturally selected at lower levels, avoid conflict between levels and types of selection.

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Dynamics of cascades on burstiness-controlled temporal networks

Sat, 01/16/2021 - 13:50

Samuel Unicomb, Gerardo Iñiguez, James P. Gleeson & Márton Karsai
Nature Communications volume 12, Article number: 133 (2021)

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.

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Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

Fri, 01/15/2021 - 18:53

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

Detailed characterizations of SARS-CoV-2 transmission risk across different social settings can inform the design of targeted and less disruptive non-pharmaceutical interventions (NPI), yet these data have been lacking. Here we integrate real-time, anonymous and privacy-enhanced geolocalized mobility data with census and demographic data in the New York City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV-2 transmission. The aim is to estimate where, when, and how many transmission events happened in those urban areas during the first wave of the pandemic. We estimate that most infections (80%) are produced by a small number of people (27%), and that about 10% of events can be considered super-spreading events (SSEs), i.e. generating more than eight secondary infections. Although mass gatherings present an important risk for future SSEs, we find that the bulk of transmission in the first wave occurred in smaller events at settings like workplaces, grocery stores, or food venues. We also observe that places where the majority of transmission and SSEs happened changed during the pandemic and are different across cities, a signal of the large underlying behavioral component underneath them. Our results demonstrate that constant real-time tracking of transmission events is needed to create, evaluate, and refine more effective and localized measures to contain the pandemic.

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Dynamics of informal risk sharing in collective index insurance

Fri, 01/15/2021 - 15:48

Fernando P. Santos, Jorge M. Pacheco, Francisco C. Santos & Simon A. Levin
Nature Sustainability (2021)

Extreme weather events often prevent low-income farmers from accessing high-return technologies that would enhance their productivity. As a result, they often fall into poverty traps, a problem likely to worsen as the frequency of weather disasters increases due to climate change. Insurance offers, in principle, a solution for this conundrum and a means to guarantee households’ wellbeing. Group collective index insurance constitutes an alternative to indemnity or individual index insurance, and has the potential to alleviate basis risk through within-group informal transfers. Here we show that collective index insurance introduces a coordination dilemma of insurance adoption: socially optimal outcomes are obtained when everyone adopts insurance; however, a minimum fraction of contributors is necessary before the effects of basis risk can be averaged out and individuals start taking up insurance. We further show that additional mechanisms—such as local peer monitoring and defector exclusion—are necessary to stabilize informal transfers and collective index insurance adoption. Together, collective index insurance and informal transfers may thus constitute a practical instrument to improve sustainability in developing countries.

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Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data 

Fri, 01/15/2021 - 13:47

Danny Valdez, Marijn ten Thij, Krishna Bathina, Lauren A Rutter, Johan Bollen

J Med Internet Res 2020;22(12):e21418

Background: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.

Objective: This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?

Methods: We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.

Results: LDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.

Conclusions: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

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The multidisciplinary nature of COVID-19 research

Thu, 01/14/2021 - 16:18

Ricardo Arencibia-Jorge, Lourdes García-García, Ernesto Galbán-Rodríguez, Humberto Carrillo-Calvet

Objective We analyzed the scientific output after COVID-19 and contrasted it with studies published in the aftermath of seven epidemics/pandemics: Severe Acute Respiratory Syndrome (SARS), Influenza A virus H5N1 and Influenza A virus H1N1 human infections, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and Dengue.

Design/Methodology/Approach We examined bibliometric measures for COVID-19 and the rest of studied epidemics/pandemics. Data were extracted from Web of Science, using its journal classification scheme as a proxy to quantify the multidisciplinary coverage of scientific output. We proposed a novel Thematic Dispersion Index (TDI) for the analysis of pandemic early stages.

Results/Discussion The literature on the seven epidemics/pandemics before COVID-19 has shown explosive growth of the scientific production and continuous impact during the first three years following each emergence or re-emergence of the specific infectious disease. A subsequent decline was observed with the progressive control of each health emergency. We observed an unprecedented growth in COVID-19 scientific production. TDI measured for COVID-19 (29,4) in just six months, was higher than TDI of the rest (7,5 to 21) during the first three years after epidemic initiation.

Conclusions COVID-19 literature showed the broadest subject coverage, which is clearly a consecuence of its social, economic, and political impact. The proposed indicator (TDI), allowed the study of multidisciplinarity, differentiating the thematic complexity of COVID-19 from the previous seven epidemics/pandemics.

Originality/Value The multidisciplinary nature and thematic complexity of COVID-19 research were successfully analyzed through a scientometric perspective.

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A scaling law in CRISPR repertoire sizes arises from avoidance of autoimmunity

Wed, 01/13/2021 - 16:27

Hanrong Chen, Andreas Mayer, Vijay Balasubramanian
Some bacteria and archaea possess an adaptive immune system that maintains a memory of past viral infections as DNA elements called spacers, stored in the CRISPR loci of their genomes. This memory is used to mount targeted responses against threats. However, cross-reactivity of CRISPR targeting mechanisms suggests that incorporation of foreign spacers can also lead to autoimmunity. We show that balancing antiviral defense against autoimmunity predicts a scaling law relating spacer length and CRISPR repertoire size. By analyzing a database of microbial CRISPR-Cas systems, we find that the predicted scaling law is realized empirically across prokaryotes, and arises through the proportionate use of different CRISPR types by species differing in the size of immune memory. In contrast, strains with nonfunctional CRISPR loci do not show this scaling. We also demonstrate that simple population-level selection mechanisms can generate the scaling, along with observed variations between strains of a given species.

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The world is not a theorem

Wed, 01/13/2021 - 16:11

Stuart A. Kauffman, Andrea Roli
The evolution of the biosphere unfolds as a luxuriant generative process of new living forms and functions. Organisms adapt to their environment, and exploit novel opportunities that are created in this continuous blooming dynamics. Affordances play a fundamental role in the evolution of the biosphere, as they represent the opportunities organisms may choose for achieving their goals, thus actualizing what is in potentia. In this paper we maintain that affordances elude a formalization in mathematical terms: we argue that it is not possible to apply set theory to affordances, therefore we cannot devise a mathematical theory of affordances and the evolution of the biosphere. 

ALIFE 2021: International conference on artificial life ALIFE 2021

Wed, 01/13/2021 - 13:50

The ALIFE conferences are the major meetings of the artificial life research community since 1987. These scientific gatherings are supported by the International Society for Artificial Life (ISAL).​

The 2021 Conference on Artificial Life ALIFE 2021 will take place in Prague (Czech Republic), 19-23 July, 2021.

The conference theme will be

Robots: The century past and the century ahead.

The world-wide used word “robot” comes from Czech. It was first used to depict a fictional humanoid in Czech writer Karel Čapek’s play R.U.R. Although the play is one hundred years old it opens many contemporary questions and many of them are related to artificial life research. It will be great to celebrate this centenary with artificial life community in the Czech Republic!

ALIFE 2021 will be a hybrid conference on artificial life. It will be a combination of a “live” in-person event in Prague with a “virtual” online component.

We hope to create opportunities for all people interested in artificial life to attend our ALIFE 2021 conference. We are looking forward to the return to the traditional conference enabling social gathering. On the other side, hybrid format will enable remote participation by people who might be unable to attend physically due to travel, through a wish to reduce the carbon footprint of the event or because of other constraints. 

Collective Intelligence

Tue, 01/12/2021 - 13:29

Collective Intelligence is a transdisciplinary journal devoted to advancing the theoretical and empirical understanding of group performance in diverse systems, from adaptive matter to cellular and neural systems to animal societies to all types of human organizations to hybrid AI-human teams and nanobot swarms.

Editors-in-Chief: Jessica Flack, Panos Ipeirotis, Scott E Page & Geoff Mulgan 

The Complexity of Increasing Returns

Sun, 01/10/2021 - 10:33

While the idea of increasing returns—the tendency for what is ahead to get further ahead—has been part of economics since the pin factory, it was long resisted by economists. The reasons were both simple and profound.

For decades, economists had a strong preference for models with a single equilibrium. This preference was incompatible with the idea of increasing returns.

Imagine a farmer choosing whether to use her land to grow food or raise cattle. She begins by planting her most fertile land. When that runs out, she moves into worse land, where the returns for her efforts will decrease. Eventually, the next patch of land is not worth tilling so she dedicates it to cattle instead.

In this story, diminishing returns lead the farmer to allocate land optimally among crops and cattle. It follows that diminishing returns are the secret behind the invisible hand. They imply that economies allocate resources optimally among multiple activities, leading to a strong policy implication: markets find an equilibrium that is both efficient and fair.

The Complexity of Increasing Returns

César A. Hidalgo

NAE Reports, Winter Issue of The Bridge on Complex Unifiable Systems

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New Quantum Algorithms Finally Crack Nonlinear Equations

Sat, 01/09/2021 - 10:15

Sometimes, it’s easy for a computer to predict the future. Simple phenomena, such as how sap flows down a tree trunk, are straightforward and can be captured in a few lines of code using what mathematicians call linear differential equations. But in nonlinear systems, interactions can affect themselves: When air streams past a jet’s wings, the air flow alters molecular interactions, which alter the air flow, and so on. This feedback loop breeds chaos, where small changes in initial conditions lead to wildly different behavior later, making predictions nearly impossible — no matter how powerful the computer.

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How Claude Shannon’s Information Theory Invented the Future

Fri, 01/08/2021 - 16:21

Science seeks the basic laws of nature. Mathematics searches for new theorems to build upon the old. Engineering builds systems to solve human needs. The three disciplines are interdependent but distinct. Very rarely does one individual simultaneously make central contributions to all three — but Claude Shannon was a rare individual.

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Why free will is beyond physics

Fri, 01/08/2021 - 14:43

Philip Ball argues that “free will” is not ruled out by physics – because it doesn’t stem from physics in the first place

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Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases

Fri, 01/08/2021 - 12:24

In recent years, social media has become an important platform for political discourse, being a site of both political conversations between voters and political advertisements from campaigns. While their individual influences on public discourse are well documented, the interplay between individual-level cognitive biases, social influence processes, dueling campaign efforts, and social media platforms remains unexamined. We introduce an agent-based model that integrates these dynamics and illustrates how their combination can lead to the formation of echo chambers. We find that the range of political viewpoints that individuals are willing to consider is a key determinant in the formation of polarized networks and the emergence of echo chambers and show that aggressive political campaigns can have counterproductive outcomes by radicalizing supporters and alienating moderates. Our model results demonstrate how certain elements of public discourse and political polarization can be understood as the result of an interactive process of shifting individual opinions, evolving social networks, and political campaigns. We also introduce a dynamic empirical case, retweet networks from the final stage of the 2016 U.S. presidential election, to show how our proposed model can be calibrated with real-world behavior.

Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases
Xin Wang, Antonio D. Sirianni, Shaoting Tang, Zhiming Zheng, and Feng Fu
Phys. Rev. X 10, 041042

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The dynamics of injunctive social norms | Evolutionary Human Sciences

Fri, 01/08/2021 - 10:19

Injunctive social norms are behaviours that one is expected to follow and expects others to follow in a given social situation; they are maintained by the threat of disapproval or punishment and by the process of internalization. Injunctive norms govern all aspects of our social life but the understanding of their effects on individual and group behaviour is currently rather incomplete. Here I develop a general mathematical approach describing the dynamics of injunctive norms in heterogeneous groups. My approach captures various costs and benefits, both material and normative, associated with norm-related behaviours including punishment and disapproval by others. It also allows for errors in decision-making and explicitly accounts for differences between individuals in their values, beliefs about the population state, and sensitivity to the actions of others. In addition, it enables one to study the consequences of mixing populations with different normative values and the effects of persuasive interventions. I describe how interactions of these factors affect individual and group behaviour. As an illustration, I consider policies developed by practitioners to abolish the norms of footbinding and female genital cutting, to decrease college students’ drinking, and to increase pro-environmental behaviours. The theory developed here can be used for achieving a better understanding of historical and current social processes as well as for developing practical policies better accounting for human social behaviour.

The dynamics of injunctive social norms

Sergey Gavrilets

Evolutionary Human Sciences

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Fighting Misinformation on Social Media | Mohsen Mosleh | TEDxMIT

Thu, 01/07/2021 - 11:29


There is a lot of worry these days about misinformation that’s being shared on social media.
The success of this kind of content is both surprising and concerning, and has led to new fields of research exploring how much misinformation is out there, and what leads people to believe and share it. But much less attention has been paid to the more important question: What can actually be DONE about the problem? This is what I have been focusing on, and in this talk, I am going to tell you about one such possible solution and show how it could translate directly into an intervention that social media companies could deploy to fight misinformation online.
Mohsen Mosleh is a Lecturer (Assistant Professor) at the Science, Innovation, Technology, and Entrepreneurship Department, University of Exeter Business School and an affiliate researcher at MIT. Mohsen’s research interests lie at the intersection of data science and cognitive science. In particular, he studies how information and misinformation spread on social media, collective decision-making, and cooperation This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at

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