Month: January 2017

The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations

With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5$\times 10^{12}$) observations of end-user internet connections, with temporal resolution of just 15min over 2006-2012. We show how these data can be used to provide scientific insights in diverse fields such as technological diffusion, chronobiology and economics.  To our knowledge, our study is the first of its kind to use online/offline activity of the entire internet to infer such insights, demonstrating the potential of the internet as a quantitative social data-science platform.


Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Collaboration to Crowd-Based Problem Solving Performance

Organizations are increasingly turning to crowdsourcing to solve difficult problems. This is often driven by the desire to find the best subject matter experts, strongly incentivize them, and engage them with as little coordination cost as possible. A growing number of authors, however, are calling for increased collaboration in crowdsourcing settings, hoping to draw upon the advantages of teamwork observed in traditional settings. The question is how to effectively incorporate team-based collaboration in a setting that has traditionally been individual-based. We report on a large field experiment of team collaboration on an online platform, in which incentives and team membership were randomly assigned, to evaluate the influence of exogenous inputs (member skills and incentives) and emergent collaboration processes on performance of crowd-based teams. Building on advances in machine learning and complex systems theory, we leverage new measurement techniques to examine the content and timing of team collaboration. We find that temporal “burstiness” of team activity and the diversity of information exchanged among team members are strong predictors of performance, even when inputs such as incentives and member skills are controlled. We discuss implications for research on crowdsourcing and team collaboration.


Preprint at SSRN.


Motter Group Postdoctoral Positions

The group has openings for postdoctoral researchers interested in dynamical aspects of complex network systems. The main topics of interest include: dynamics of ecological, chemical, and power-grid networks; applications of control theory and game theory to complex systems; missing information in biochemical and combustion reaction networks; cascades, synchronization, and consensus phenomena; implications of symmetry in network structure and dynamics; applications to sustainability, climate, and energy problems. Ideal candidates will be recent PhD’s in physics, applied mathematics, engineering, computer science, statistics, or related fields. One position is available immediately.  To apply, candidates should e-mail a CV and a brief research statement to Prof. Motter at . The CV should include a list of publications and contact information of at least two references who can provide recommendation letters. Deadline for applications: March 1, 2017.  For more information on the research in the group, please visit:


43 Visions for Complexity

Coping with the complexities of the social world in the 21st century requires deeper quantitative and predictive understanding. Forty-three internationally acclaimed scientists and thinkers share their vision for complexity science in the next decade in this invaluable book. Topics cover how complexity and big data science could help society to tackle the great challenges ahead, and how the newly established Complexity Science Hub Vienna might be a facilitator on this path.


43 Visions for Complexity. Edited by Stefan Thurner

World Scientific


See Also: Table of contents and sample chapter