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SOCNET  June 2017

SOCNET June 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, 5 Jun 2017 21:57:26 -0400

Content-Type:

MULTIPART/MIXED

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Parts/Attachments

TEXT/PLAIN (79 lines)

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

Great talking with some of you at the SUnbelt

   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
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
                        http://amzn.to/zXZg39
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 5 Jun 2017 11:03:08 +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=70aeabb651&e=55e25a0e3e


Looplessness in networks is linked to trophic coherence

    Complex systems such as cells, brains, or ecosystems are made up of many interconnected elements, each one acting on its neighbors, and sometimes influencing its own state via feedback loops. Certain biological networks have surprisingly few such loops. Although this may be advantageous in various ways, it is not known how feedback is suppressed. We show that trophic coherence, a structural property of ecosystems, is key to the extent of feedback in these as well as in many other systems, including networks related to genes, neurons, metabolites, words, computers, and trading nations. We derive mathematical expressions that provide a benchmark against which to examine empirical data, and conclude that ˙˙looplessness˙˙ in nature is probably a consequence of trophic coherenc

Source: www.pnas.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=1385761329&e=55e25a0e3e)

Fair Topologies: Community Structures and Network Hubs Drive Emergence of Fairness Norms

    http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=522e0b1696&e=55e25a0e3e

Fairness has long been argued to govern human behavior in a wide range of social, economic, and organizational activities. The sense of fairness, although universal, varies across different societies. In this study, using a computational model, we test the hypothesis that the topology of social interaction can causally explain some of the cross-societal variations in fairness norms. We show that two network parameters, namely, community structure, as measured by the modularity index, and network hubiness, represented by the skewness of degree distribution, have the most significant impact on emergence of collective fair behavior. These two parameters can explain much of the variations in fairness norms across societies and can also be linked to hypotheses suggested by earlier empirical studies in social and organizational sciences. We devised a multi-layered model that combines local agent interactions with social learning, thus enables both strategic behavior as well as
diffusion of successful strategies. By applying multivariate statistics on the results, we obtain the relation between network structural features and the collective fair behavior.


Fair Topologies: Community Structures and Network Hubs Drive Emergence of Fairness Norms

Mohsen Mosleh, Babak Heydari

Source: www.nature.com (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3d1b6179f4&e=55e25a0e3e)


The missing links: A global study on uncovering financial network structures from partial data

    Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks.


The missing links: A global study on uncovering financial network structures from partial data

Kartik Anand, et al.

Journal of Financial Stability

Source: www.sciencedirect.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=6d36bb909c&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=f2e0c6e421&e=55e25a0e3e ) and using the "Suggest" button.
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