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   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
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   NetLab Network                 FRSC                      INSNA Founder
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
                        http://amzn.to/zXZg39
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---------- Forwarded message ----------
Date: Mon, 6 Feb 2017 12:04:58 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8302981a37&e=55e25a0e3e


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.

Source: arxiv.org (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2bd2a1eda0&e=55e25a0e3e)



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

    http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a86000e7c0&e=55e25a0e3e

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 (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0f730c4ddf&e=55e25a0e3e) .

Source: amd.aom.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=8a05c32041&e=55e25a0e3e)

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