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SOCNET  October 2017

SOCNET October 2017

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 9 Oct 2017 08:20:22 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (211 lines)

*****  To join INSNA, visit http://www.insna.org  *****

Many thanks to the half-dozen folks who responded telling me they find my 
editing of this useful. (Of course the hard work is done by the editors of 
Complexity Digest itself: I just winnow).

Happy Thanksgiving, Succoth, Autumn Festival, and surviving another day of 
Trump

   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
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
   http://www.chass.utoronto.ca/~wellman            http://amzn.to/zXZg39
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 9 Oct 2017 11:02:39 +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-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=887901372c&e=55e25a0e3e



Where is technology taking the economy?

    We are creating an intelligence that is external to humans and housed 
in the virtual economy. This is bringing us into a new economic era˙˙a 
distributive one˙˙where different rules apply.


Where is technology taking the economy?
By W. Brian Arthur

McKinsey Quaterly

Source: www.mckinsey.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=6be9513289&e=55e25a0e3e)



The shape of collaborations

    The structure of scientific collaborations has been the object of 
intense study both for its importance for innovation and scientific 
advancement, and as a model system for social group coordination and 
formation thanks to the availability of authorship data. Over the last 
years, complex networks approach to this problem have yielded important 
insights and shaped our understanding of scientific communities. In this 
paper we propose to complement the picture provided by network tools with 
that coming from using simplicial descriptions of publications and the 
corresponding topological methods. We show that it is natural to extend 
the concept of triadic closure to simplicial complexes and show the 
presence of strong simplicial closure. Focusing on the differences between 
scientific fields, we find that, while categories are characterized by 
different collaboration size distributions, the distributions of how many 
collaborations to which an author is able to participate is conserved 
across fields pointing to underlying attentional and temporal constraints. 
We then show that homological cycles, that can intuitively be thought as 
hole in the network fabric, are an important part of the underlying 
community linking structure.


The shape of collaborations
Alice PataniaEmail authorView ORCID ID profile, Giovanni Petri and Francesco Vaccarino
EPJ Data Science20176:18
http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4138e3a8fe&e=55e25a0e3e

Source: epjdatascience.springeropen.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9a43f1030b&e=55e25a0e3e)



Data-driven modeling of collaboration networks: a cross-domain analysis

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d58cec4a1e&e=55e25a0e3e

We analyze large-scale data sets about collaborations from two different 
domains: economics, specifically 22,000 R&D alliances between 14,500 
firms, and science, specifically 300,000 co-authorship relations between 
95,000 scientists. Considering the different domains of the data sets, we 
address two questions: (a) to what extent do the collaboration networks 
reconstructed from the data share common structural features, and (b) can 
their structure be reproduced by the same agent-based model. In our 
data-driven modeling approach we use aggregated network data to calibrate 
the probabilities at which agents establish collaborations with either 
newcomers or established agents. The model is then validated by its 
ability to reproduce network features not used for calibration, including 
distributions of degrees, path lengths, local clustering coefficients and 
sizes of disconnected components. Emphasis is put on comparing domains, 
but also sub-domains (economic sectors, scientific specializations). 
Interpreting the link probabilities as strategies for link formation, we 
find that in R&D collaborations newcomers prefer links with established 
agents, while in co-authorship relations newcomers prefer links with other 
newcomers. Our results shed new light on the long-standing question about 
the role of endogenous and exogenous factors (i.e., different information 
available to the initiator of a collaboration) in network formation.


Data-driven modeling of collaboration networks: a cross-domain analysis
Mario V Tomasello, Giacomo VaccarioEmail authorView ORCID ID profile and Frank Schweitzer
EPJ Data Science20176:22
http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=449ebc0b57&e=55e25a0e3e

Source: epjdatascience.springeropen.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7d008c7f97&e=55e25a0e3e)



Estimating local commuting patterns from geolocated Twitter data

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ab8c6c5010&e=55e25a0e3e

The emergence of large stores of transactional data generated by 
increasing use of digital devices presents a huge opportunity for 
policymakers to improve their knowledge of the local environment and thus 
make more informed and better decisions. A research frontier is hence 
emerging which involves exploring the type of measures that can be drawn 
from data stores such as mobile phone logs, Internet searches and 
contributions to social media platforms and the extent to which these 
measures are accurate reflections of the wider population. This paper 
contributes to this research frontier, by exploring the extent to which 
local commuting patterns can be estimated from data drawn from Twitter. It 
makes three contributions in particular. First, it shows that heuristics 
applied to geolocated Twitter data offer a good proxy for local commuting 
patterns; one which outperforms the current best method for estimating 
these patterns (the radiation model). This finding is of particular 
significance because we make use of relatively coarse geolocation data (at 
the city level) and use simple heuristics based on frequency counts. 
Second, it investigates sources of error in the proxy measure, showing 
that the model performs better on short trips with higher volumes of 
commuters; it also looks at demographic biases but finds that, 
surprisingly, measurements are not significantly affected by the fact that 
the demographic makeup of Twitter users differs significantly from the 
population as a whole. Finally, it looks at potential ways of going beyond 
simple frequency heuristics by incorporating temporal information into 
models.


Estimating local commuting patterns from geolocated Twitter data
Graham McNeill, Jonathan Bright, and Scott A Hale

EPJ Data Science 2017 6:24

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=eeb98ca943&e=55e25a0e3e

Source: epjdatascience.springeropen.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=566d731bb6&e=55e25a0e3e)


Reliable uncertainties in indirect measurements

    In this article we present very intuitive, easy to follow, yet mathematically rigorous, approach to the so called data fitting process. Rather than minimizing the distance between measured and simulated data points, we prefer to find such an area in searched parameters' space that generates simulated curve crossing as many acquired experimental points as possible, but at least half of them. Such a task is pretty easy to attack with interval calculations. The problem is, however, that interval calculations operate on guaranteed intervals, that is on pairs of numbers determining minimal and maximal values of measured quantity while in vast majority of cases our measured quantities are expressed rather as a pair of two other numbers: the average value and its standard deviation. Here we propose the combination of interval calculus with basic notions from probability and statistics. This approach makes possible to obtain the results in familiar form as reliable values of searched
parameters, their standard deviations, and their correlations as well. There are no assumptions concerning the probability density distributions of experimental values besides the obvious one that their variances are finite. Neither the symmetry of uncertainties of experimental distributions is required (assumed) nor those uncertainties have to be `small.' As a side effect, outliers are quietly and safely ignored, even if numerous.


Reliable uncertainties in indirect measurements
Marek W. Gutowski

Source: arxiv.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2c1a5d2665&e=55e25a0e3e)


Postdoctoral Position in Network Dynamics at Northwestern University

    The group of Prof. Adilson E. Motter at Northwestern University has an opening for postdoctoral researchers interested in dynamical aspects of complex network systems. To apply, candidates should e-mail a CV and a brief research statement to Prof. Motter at [log in to unmask] The application deadline is November 15, 2017. For more information, please visit: http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5b86d434df&e=55e25a0e3e.

Source: dyn.phys.northwestern.edu (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4e72056e2c&e=55e25a0e3e)



It˙˙s Complicated˙˙The Relationship of Complexity Theory to Normative Discourse in Science, Society, and Beyond

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0e29be7e00&e=55e25a0e3e

Place & Date: 9am ˙˙ 4:30pm, Nov 14th, 2017 at Clark Center S360, Stanford University.

Registration link: http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=acd1e87b82&e=55e25a0e3e

There will be three focusing questions for the Symposium:

What is ˙˙Complexity Science˙˙?
How is Complexity Science integrated into various disciplines?
How does Complexity Science affect how we solve scientific, social, or philosophical problems?
All proceedings of the Symposium will be professionally video recorded and uploaded to our Youtube account, http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=19e8813db5&e=55e25a0e3e

Source: complexity.stanford.edu (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=47ab9e1567&e=55e25a0e3e)



==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b4cba3a4d0&e=55e25a0e3e ) and using the "Suggest" button.
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