82 research outputs found
The strength of the strongest ties in collaborative problem solving
Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems
Spreading in Social Systems: Reflections
In this final chapter, we consider the state-of-the-art for spreading in
social systems and discuss the future of the field. As part of this reflection,
we identify a set of key challenges ahead. The challenges include the following
questions: how can we improve the quality, quantity, extent, and accessibility
of datasets? How can we extract more information from limited datasets? How can
we take individual cognition and decision making processes into account? How
can we incorporate other complexity of the real contagion processes? Finally,
how can we translate research into positive real-world impact? In the
following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems";
Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur
Probing empirical contact networks by simulation of spreading dynamics
Disease, opinions, ideas, gossip, etc. all spread on social networks. How
these networks are connected (the network structure) influences the dynamics of
the spreading processes. By investigating these relationships one gains
understanding both of the spreading itself and the structure and function of
the contact network. In this chapter, we will summarize the recent literature
using simulation of spreading processes on top of empirical contact data. We
will mostly focus on disease simulations on temporal proximity networks --
networks recording who is close to whom, at what time -- but also cover other
types of networks and spreading processes. We analyze 29 empirical networks to
illustrate the methods
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System
Combining low cost wireless EEG sensors with smartphones offers novel
opportunities for mobile brain imaging in an everyday context. We present a
framework for building multi-platform, portable EEG applications with real-time
3D source reconstruction. The system - Smartphone Brain Scanner - combines an
off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such
represents the first fully mobile system for real-time 3D EEG imaging. We
discuss the benefits and challenges of a fully portable system, including
technical limitations as well as real-time reconstruction of 3D images of brain
activity. We present examples of the brain activity captured in a simple
experiment involving imagined finger tapping, showing that the acquired signal
in a relevant brain region is similar to that obtained with standard EEG lab
equipment. Although the quality of the signal in a mobile solution using a
off-the-shelf consumer neuroheadset is lower compared to that obtained using
high density standard EEG equipment, we propose that mobile application
development may offset the disadvantages and provide completely new
opportunities for neuroimaging in natural settings
Academic Performance and Behavioral Patterns
Identifying the factors that influence academic performance is an essential
part of educational research. Previous studies have documented the importance
of personality traits, class attendance, and social network structure. Because
most of these analyses were based on a single behavioral aspect and/or small
sample sizes, there is currently no quantification of the interplay of these
factors. Here, we study the academic performance among a cohort of 538
undergraduate students forming a single, densely connected social network. Our
work is based on data collected using smartphones, which the students used as
their primary phones for two years. The availability of multi-channel data from
a single population allows us to directly compare the explanatory power of
individual and social characteristics. We find that the most informative
indicators of performance are based on social ties and that network indicators
result in better model performance than individual characteristics (including
both personality and class attendance). We confirm earlier findings that class
attendance is the most important predictor among individual characteristics.
Finally, our results suggest the presence of strong homophily and/or peer
effects among university students
Mechanism, assessment and management of pain in chronic pancreatitis: Recommendations of a multidisciplinary study group
AbstractDescriptionPain in patients with chronic pancreatitis (CP) remains the primary clinical complaint and source of poor quality of life. However, clear guidance on evaluation and treatment is lacking.MethodsPancreatic Pain working groups reviewed information on pain mechanisms, clinical pain assessment and pain treatment in CP. Levels of evidence were assigned using the Oxford system, and consensus was based on GRADE. A consensus meeting was held during PancreasFest 2012 with substantial post-meeting discussion, debate, and manuscript refinement.ResultsTwelve discussion questions and proposed guidance statements were presented. Conference participates concluded: Disease Mechanism: Pain etiology is multifactorial, but data are lacking to effectively link symptoms with pathologic feature and molecular subtypes. Assessment of Pain: Pain should be assessed at each clinical visit, but evidence to support an optimal approach to assessing pain character, frequency and severity is lacking. Management: There was general agreement on the roles for endoscopic and surgical therapies, but less agreement on optimal patient selection for medical, psychological, endoscopic, surgical and other therapies.ConclusionsProgress is occurring in pain biology and treatment options, but pain in patients with CP remains a major problem that is inadequately understood, measured and managed. The growing body of information needs to be translated into more effective clinical care
Mobile Phone Data for Children on the Move: Challenges and Opportunities
Today, 95% of the global population has 2G mobile phone coverage and the
number of individuals who own a mobile phone is at an all time high. Mobile
phones generate rich data on billions of people across different societal
contexts and have in the last decade helped redefine how we do research and
build tools to understand society. As such, mobile phone data has the potential
to revolutionize how we tackle humanitarian problems, such as the many suffered
by refugees all over the world. While promising, mobile phone data and the new
computational approaches bring both opportunities and challenges. Mobile phone
traces contain detailed information regarding people's whereabouts, social
life, and even financial standing. Therefore, developing and adopting
strategies that open data up to the wider humanitarian and international
development community for analysis and research while simultaneously protecting
the privacy of individuals is of paramount importance. Here we outline the
challenging situation of children on the move and actions UNICEF is pushing in
helping displaced children and youth globally, and discuss opportunities where
mobile phone data can be used. We identify three key challenges: data access,
data and algorithmic bias, and operationalization of research, which need to be
addressed if mobile phone data is to be successfully applied in humanitarian
contexts.Comment: 13 pages, book chapte
Evidence for a Conserved Quantity in Human Mobility
Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the ‘Dunbar number’ describing a cognitive upper limit to an individual’s number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences
Multi-scale spatio-temporal analysis of human mobility
The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals' digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life
- …