Month: April 2020

Critical slowing down associated with critical transition and risk of collapse in crypto-currency

The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display the signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as the early warning signals of two major price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time-series’ autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.

Source: royalsocietypublishing.org

Mapping the coevolution, leadership and financing of research on viral vectors, RNAi, CRISPR/Cas9 and other genomic editing technologies

Fajardo-Ortiz D, Shattuck A, Hornbostel S

PLoS ONE 15(4): e0227593.

Genomic editing technologies are developing rapidly, promising significant developments for biomedicine, agriculture and other fields. In the present investigation, we analyzed and compared the process of innovation for six genomic technologies: viral vectors, RNAi, TALENs, meganucleases, ZFNs and CRISPR/Cas including the profile of the main research institutions and their funders, to understand how innovation evolved and what institutions influenced research trajectories. A Web of Science search of papers on viral vectors RNAi, CRISPR/Cas, TALENs, ZFNs and meganucleases was used to build a citation network of 16,746 papers. An analysis of network clustering combined with text mining was performed. For viral vectors, a long-term process of incremental innovation was identified, which was largely publicly funded in the United States and the European Union. The trajectory of RNAi research included clusters related to the study of RNAi as a biological phenomenon and its use in functional genomics, biomedicine and pest control. A British philanthropic organization and a US pharmaceutical company played a key role in the development of basic RNAi research and clinical application respectively, in addition to government and academic institutions. In the case of CRISPR/Cas research, basic science discoveries led to the technical improvements, and these two in turn provided the information required for the development of biomedical, agricultural, livestock and industrial applications. The trajectory of CRISPR/Cas research exhibits a geopolitical division of the investigation efforts between the US, as the main producer and funder of basic research and technical improvements, and Chinese research institutions increasingly leading applied research. Our results reflect a change in the model for financing science, with reduced public financing for basic science and applied research on publicly funded technological developments in the US, and the emergence of China as a scientific superpower, with implications for the development of applications of genomic technologies.

Source: journals.plos.org

An Agent-Based Model of Opinion Polarization Driven by Emotions

Frank Schweitzer, Tamas Krivachy, and David Garcia

Complexity Volume 2020 |Article ID 5282035

We provide an agent-based model to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents. The driving variable is the emotional state of agents, characterized by their valence, quantifying the emotion from unpleasant to pleasant, and their arousal, quantifying the degree of activity associated with the emotion. Both determine their emotional expression, from which collective emotional information is generated. This information feeds back on the dynamics of emotional states and individual opinions in a nonlinear manner. We derive the critical conditions for emotional interactions to obtain either consensus or polarization of opinions. Stochastic agent-based simulations and formal analyses of the model explain our results. Possible ways to validate the model are discussed.

Source: www.hindawi.com

Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action

Flaminio Squazzoni, J. Gareth Polhill, Bruce Edmonds, Petra Ahrweiler, Patrycja Antosz, Geeske Scholz, Émile Chappin, Melania Borit, Harko Verhagen, Francesca Giardini and Nigel Gilbert

JASSS 23(2),10

The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

Source: jasss.soc.surrey.ac.uk

Assessing changes in commuting and individual mobility in major metropolitan areas in the United States during the COVID-19 outbreak

Brennan Klein, Timothy LaRock, Stefan McCabe, Leo Torres, Filippo Privitera, Brennan Lake, Moritz U. G. Kraemer, John S. Brownstein, David Lazer, Tina Eliassi-Rad, Samuel V. Scarpino, Matteo Chinazzi, and Alessandro Vespignani

On March 16, 2020, the United States government issued new guidelines promoting public health social social distancing interventions to reduce the spread of the COVID-19 epidemic in the country [1]. In addition, many state and local governments in the United States have enacted stay-at-home policies banning mass gatherings, enforcing school closures, and promoting smart working. So far, however, the extent to which these policies have resulted in reduced people’s mobility has not been quantified. By analyzing data from millions of (anonymized, aggregated, privacy-enhanced) devices, we estimate that by March 23 the policies have generally reduced by half the overall mobility in several major U.S. cities. In order to gauge the observed results we know events, we note that the commuting volume on Monday, March 16, approached those of a typical snow day or analogous day when public schools are partially closed (i.e. January 2). By Friday, March 20, we observe commuting numbers that resemble those measured on federal holidays (i.e. Martin Luther King Jr. Day in January or Presidents’ Day in February). Currently, we are unable to quantify the extent to which this reduced commuting volume is driven by people working from home or simply an increase in unemployment, though it is surely a mixture of both. Whether this reduction in mobility is enough to change the course of this pandemic is not yet known, but it does provide guidance for further measures that can be implemented at a national scale in the United States.

Source: www.networkscienceinstitute.org