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SOCNET  August 2017

SOCNET August 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, 28 Aug 2017 09:26:32 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (174 lines)

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

happy end of summer

   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, 28 Aug 2017 11:03:25 +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=538e49d354&e=55e25a0e3e



Understanding Human-Machine Networks

    In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans
and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.


Understanding Human-Machine Networks: A Cross-Disciplinary Survey

Milena Tsvetkova et al.

ACM Computing Surveys
Volume 50 Issue 1, April 2017
Article No. 12

Source: dl.acm.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=11ff3fb985&e=55e25a0e3e)


Comparison of traffic reliability index with real traffic data

    http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=048c8cad60&e=55e25a0e3e

Existing studies have developed different indices based on various 
approaches including network connectivity, delay time and flow capacity, 
estimating the traffic reliability states from different angles. However, 
these indices mainly estimate traffic reliability from single view and 
rarely consider the combined effect of city traffic dynamics and 
underlying network structure. Based on percolation theory, Li et al. has 
developed a traffic reliability index to address this issue (Proc. Natl. 
Acad. Sci. USA 112(3):669-672, 2015) [1]. Here we compare this 
percolation-based index with one of the well-known index - congestion 
delay index (CDI). Using real traffic data of Beijing and Shenzhen (two 
large cities in China), we compare the two indices in the macroscopic 
trends and microscopic extreme values. The two indices are found to 
indicate the state of real-time traffic reliability in different 
consideration. Our results can be used for better evaluation of traffic 
system reliability and mitigation measures of traffic jams.


Comparison of traffic reliability index with real traffic data
Limiao Zhang, Guanwen Zeng, Shengmin Guo, Daqing LiEmail author and Ziyou Gao
EPJ Data Science20176:19
http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2397175a1a&e=55e25a0e3e

Source: epjdatascience.springeropen.com (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5d112e96b4&e=55e25a0e3e)


Effects of network modularity on the spread of perturbation impact in experimental metapopulations

    The networks that form natural, social, and technological systems are 
vulnerable to the spreading impacts of perturbations. Theory predicts that 
networks with a clustered or modular structure˙˙where nodes within a 
module interact more frequently than they do with nodes in other 
modules˙˙might contain a perturbation, preventing it from spreading to the 
entire network. Gilarranz et al. conducted experiments with networked 
populations of springtail ( Folsomia candida ) microarthropods to show 
that modularity limits the impact of a local extinction on neighboring 
nodes (see the Perspective by Sales-Pardo). In networks with high 
modularity, the perturbation was contained within the targeted module, and 
its impact did not spread to nodes beyond it. However, simulations 
revealed that modularity is beneficial to the network only when 
perturbations are present; otherwise, it hinders population growth.

Science , this issue p. [199][1]; see also p. [128][2]

[1]: /lookup/doi/10.1126/science.aal4122
[2]: /lookup/doi/10.1126/science.aan8075

Source: science.sciencemag.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=28068f67a6&e=55e25a0e3e)



Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications

    Traffic signals are ubiquitous devices that first appeared in 1868. 
Recent advances in information and communications technology (ICT) have 
led to unprecedented improvements in such areas as mobile handheld devices 
(i.e., smartphones), the electric power industry (i.e., smart grids), 
transportation infrastructure, and vehicle area networks. Given the trend 
towards interconnectivity, it is only a matter of time before vehicles 
communicate with one another and with infrastructure. In fact, several 
pilots of such vehicle-to-vehicle and vehicle-to-infrastructure (e.g. 
traffic lights and parking spaces) communication systems are already 
operational. This survey of autonomous and self-organized traffic 
signaling control has been undertaken with these potential developments in 
mind. Our research results indicate that, while many sophisticated 
techniques have attempted to improve the scheduling of traffic signal 
control, either real-time sensing of traffic patterns or a priori 
knowledge of traffic flow is required to optimize traffic. Once this is 
achieved, communication between traffic signals will serve to vastly 
improve overall traffic efficiency.


Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications
Sanjay Goel, Stephen F. Bush, Carlos Gershenson

Source: arxiv.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=6e85f95fdf&e=55e25a0e3e)



The role of city size and urban form in the surface urban heat island

    http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a3afc0853a&e=55e25a0e3e

Urban climate is determined by a variety of factors, whose knowledge can 
help to attenuate heat stress in the context of ongoing urbanization and 
climate change. We study the influence of city size and urban form on the 
Urban Heat Island (UHI) phenomenon in Europe and find a complex interplay 
between UHI intensity and city size, fractality, and anisometry. Due to 
correlations among these urban factors, interactions in the multi-linear 
regression need to be taken into account. We find that among the largest 
5,000 cities, the UHI intensity increases with the logarithm of the city 
size and with the fractal dimension, but decreases with the logarithm of 
the anisometry. Typically, the size has the strongest influence, followed 
by the compactness, and the smallest is the influence of the degree to 
which the cities stretch. Accordingly, from the point of view of UHI 
alleviation, small, disperse, and stretched cities are preferable. 
However, such recommendations need to be balanced against e.g. positive 
agglomeration effects of large cities. Therefore, trade-offs must be made 
regarding local and global aims.


The role of city size and urban form in the surface urban heat island
Bin Zhou, Diego Rybski & Jürgen P. Kropp
Scientific Reports 7, Article number: 4791 (2017)
doi:10.1038/s41598-017-04242-2

Source: www.nature.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=53038ff630&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=ae192e074c&e=55e25a0e3e ) and using the "Suggest" button.
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