Month: January 2021

The Sci-Hub effect on papers’ citations

Juan C. Correa, Henry Laverde-Rojas, Julian Tejada & Fernando Marmolejo-Ramos
Scientometrics (2021)

Citations are often used as a metric of the impact of scientific publications. Here, we examine how the number of downloads from Sci-Hub as well as various characteristics of publications and their authors predicts future citations. Using data from 12 leading journals in economics, consumer research, neuroscience, and multidisciplinary research, we found that articles downloaded from Sci-Hub were cited 1.72 times more than papers not downloaded from Sci-Hub and that the number of downloads from Sci-Hub was a robust predictor of future citations. Among other characteristics of publications, the number of figures in a manuscript consistently predicts its future citations. The results suggest that limited access to publications may limit some scientific research from achieving its full impact.

Read the full article at: link.springer.com

Economic complexity theory and applications

César A. Hidalgo
Nature Reviews Physics (2021)

Economic complexity methods have become popular tools in economic geography, international development and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions and income inequality.

Read the full article at: www.nature.com

Complexity Weekend – May 21-23, 2021

Complexity Science is an interdisciplinary and inclusive framework for studying, designing, and controlling Complex system behavior, such as global pandemics, extreme weather events, electoral politics, economic recovery and poverty, and much more. Over the course of one weekend, you will experience Complexity from a variety of perspectives, while developing solutions to real-world problems in a team setting, such as:

  • Information flow in a time of global connectivity
  • Adaptive planning for communities amidst turbulence and uncertainty
  • Addressing climate change and extreme weather events
  • Ensuring fair and accurate elections
  • Evaluating shelter-in-place policy efficacy during a pandemic
  • Building resiliency into businesses, governments, and families
  • Healthcare policy and efficacy
  • Mental health and wellness
  • Any other difficult and ongoing problems you can identify 

Many of the teams formed from our previous Complexity Weekends (May 2019, May 2020) have resulted in productive collaboration and continued work to this day.

www.complexityweekend.com

Short-term prediction through ordinal patterns

Yair Neuman, Yochai Cohen and Boaz Tamir

Royal Society Open Science

January 2021 Volume 8, Issue 1

Prediction in natural environments is a challenging task, and there is a lack of clarity around how a myopic organism can make short-term predictions given limited data availability and cognitive resources. In this context, we may ask what kind of resources are available to the organism to help it address the challenge of short-term prediction within its own cognitive limits. We point to one potentially important resource: ordinal patterns, which are extensively used in physics but not in the study of cognitive processes. We explain the potential importance of ordinal patterns for short-term prediction, and how natural constraints imposed through (i) ordinal pattern types, (ii) their transition probabilities and (iii) their irreversibility signature may support short-term prediction. Having tested these ideas on a massive dataset of Bitcoin prices representing a highly fluctuating environment, we provide preliminary empirical support showing how organisms characterized by bounded rationality may generate short-term predictions by relying on ordinal patterns.

Read the full article at: royalsocietypublishing.org

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

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.

Read the full article at: www.mdpi.com