In a new piece in PNAS, Dasgupta,Scircle & Hunsinger demonstrate the
importance of team gender composition. They show that females have greater
participation, self-confidence, and career aspirations when they are
assigned to teams with more females.

Abstract: For years, public discourse in science education, technology, and
policy-making has focused on the “leaky pipeline” problem: the observation
that fewer women than men enter science, technology, engineering, and
mathematics fields and more women than men leave. Less attention has
focused on experimentally testing solutions to this problem. We report an
experiment investigating one solution: we created “microenvironments”
(small groups) in engineering with varying proportions of women to identify
which environment increases motivation and participation, and whether
outcomes depend on students’ academic stage. Female engineering students
were randomly assigned to one of three engineering groups of varying sex
composition: 75% women, 50% women, or 25% women. For first-years, group
composition had a large effect: women in female-majority and sex-parity
groups felt less anxious than women in female-minority groups. However,
among advanced students, sex composition had no effect on anxiety.
Importantly, group composition significantly affected verbal participation,
regardless of women’s academic seniority: women participated more in
female-majority groups than sex-parity or female-minority groups.
Additionally, when assigned to female-minority groups, women who harbored
implicit masculine stereotypes about engineering reported less confidence
and engineering career aspirations. However, in sex-parity and
female-majority groups, confidence and career aspirations remained high
regardless of implicit stereotypes. These data suggest that creating small
groups with high proportions of women in otherwise male-dominated fields is
one way to keep women engaged and aspiring toward engineering careers.
Although sex parity works sometimes, it is insufficient to boost women’s
verbal participation in group work, which often affects learning and
mastery.

Link: http://www.pnas.org/content/early/2015/04/03/1422822112

Original Article:

http://www.technologyreview.com/view/534576/how-network-science-is-changing-our-understanding-of-law/

Summary:

Konaris et al at the National Technical University of Athens in Greece expanded on existing legal network analysis by extracting “all the documents from the European Community’s legal database dating back to 1951” and organizing the texts into three subnetworks: treaties between countries, regulations and directives that are based on these treaties, and case laws that have emerged from the application of these regulations. Analysis of each subsection showed that “all were small world networks in themselves” that generally demonstrate high levels of resilience. This model produced a novel perspective because it takes into account both the temporal dynamism and hierarchical nature of European law. In addition to mapping citations (references that do not modify the target document) Konaris also represented legal bias (edit references that modify either the text or the lifecycle of the target document) and accounted for their effects over time. This revealed “a steep increase in the density of links within the network over time”. Ultimately, Konaris suggests that the clusters and related connections illustrated by this work may “help legislators determine the effect of proposed changes and improve the effectiveness of legal information retrieval”.

Paper:

http://arxiv.org/abs/1501.05237

Christopher Monterola of that A*STAR Institute of High Performance Computing in Singapore and co-researchers at and The Logistics Institute-Asia Pacific put forth a computer model that predicts the most expedient over-ground routes using real-time data uploaded by agents on the scene. This model allows users to project the “flow of goods and other relief efforts, and quantify the reachability of critical loci within a geographic area”. To create this tool, Moterola’s team “developed a procedure that automatically transforms street maps into a network of nodes (road intersections) and edges (road segments)”. By allowing for continuous updates from crowd-sourced platforms like OpenStreetMap, the model incorporates a flexibility that enables local governments to visualize multiple infrastructure destruction scenarios.

The team achieved this by comparing “two different model networks: a grid lattice common in cities and a ‘scale-free’ road network that represents a mix of urban hubs and rural spokes”. In the resulting paper, Monterola et al. discuss “the inaccuracy of assuming that road networks follow a structure similar to the more commonly studied scale-free, random, and/or grid (regular) network configurations” – contradicting conventional assumptions used in preparedness planning. Hopefully, these findings will help correct the flaws of existing practices that forestall relief to victims (like those of Typhoon Haiyan in 2013) and mitigate the loss of life and property.

News Article:
http://phys.org/news/2014-12-algorithm-automatically-quickest-calamity-affected-sites.html#jCp

Full Article:
http://www.worldscientific.com.turing.library.northwestern.edu/doi/pdf/10.1142/S0129183114500478

This special issue of the Proceedings of the IEEE focuses on how digital technology is changing the structure and dynamics of social networks and the tools we have for studying and designing them. Three main take-home messages:

• Social media, search, and data extraction technologies are not only changing the structure and dynamics of social networks, but are also changing how controllable these systems are.

• Precision, quantitatively justified interventions into behavioral dynamics are increasingly feasible within the digital domain, permitting large-scale experiments on human behavior and social systems. This is useful and presents challenges.

• We understand the relationship between energy and information – how bits get converted to watts – for electrical circuits, but not for social networks. In biology, computational social science, and the science of social engineering, the development of a functional theory of information is a central theoretical challenge that needs to be addressed if these disciplines are to have strong foundations.

Read more here: http://mae.engr.ucdavis.edu/dsouza/Pubs/PIEEE_vol102_12.pdf