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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
   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    

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Date: Mon, 6 Nov 2017 12:05:06 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at 

The Trouble With Scientists ˙˙ Issue 54: The Unspoken ˙˙ Nautilus 

Sometimes it seems surprising that science functions at all. In 2005, medical science was shaken by a paper with the provocative title ˙˙Why most published research findings are false.˙˙ Written by John Ioannidis, a professor of medicine at Stanford University, it didn˙˙t actually show that any particular result was wrong. Instead, it showed that the statistics of reported positive findings was not consistent with how often one should expect to find them. As Ioannidis concluded more recently, ˙˙many published research findings are false or exaggerated, and an estimated 85 percent of research resources are wasted.˙˙

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Is Tribalism a Natural Malfunction? 

Even the genocidal machines at the violent end of the spectrum may carry a heartening lesson. They emerged from the depths of a circuit board, simulated on a supercomputer in Texas. They had no biological excuse to fall back on. Maybe we, too, shouldn˙˙t make excuses: If a behavior is so common as to emerge in the simplest simulations, perhaps we ought neither to fear it, nor to idolize it, but to treat it, the same way we do cancer, or the flu.

What if we saw tribalism as a natural malfunction of any cognitive system, silicon or carbon? As neither a universal truth or unavoidable sin, but something to be overcome?˙˙

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Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors

    Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviors for predicting the future presence of flu-like and cold symptoms (i.e. fever, sore throat, cough, shortness of breath, headache, muscle pain, malaise, and cold). To this end, we use the mobility traces from mobile phones and the daily self-reported flu-like and cold symptoms of 29 individuals from February 20, 2013 to March 21, 2013. First of all, we demonstrate that daily symptoms of an individual can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places, etc.). Then, we present and validate
models that are able to successfully predict the future presence of symptoms by analyzing the mobility patterns of our individuals. The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion.

Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors
Gianni Barlacchi, Christos Perentis, Abhinav Mehrotra, Mirco Musolesi and Bruno Lepri
EPJ Data Science20176:27 

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The Rich get Richer and the Fit get Richer Phenomena in Temporal Complex Networks in the Strategic Management Scientific Community

    The aim of this paper is to determine the general preferential attachment function and author fitness, which describe the rich get richer and fit get richer phenomena, in the co-authorship and citation networks of the strategic management scientific community. This has been done by means of the PAFit method using the community's flagship journal, namely Strategic Management Journal. The results suggest the co-authorship and citation temporal networks are governed by both the fit get richer and the rich get richer processes. The average of the attachment exponents in the co-author network is 0.3 while it is 0.29 in the citation network, which suggests the rich get richer phenomenon is similarly weak in both networks. On the other hand, the distributions of author fitness in both networks have long right tail, which implies that the intrinsic scientific quality of each author plays a crucial role in getting new citations and new co-authorships. Furthermore, author fitness in
both co-authorship and citation networks are found to be consistent with the history of the strategic management scientific community.

The Rich get Richer and the Fit get Richer Phenomena in Temporal Complex Networks in the Strategic Management Scientific Community
Ronda-Pupo Guillermo Armando, Thong Pham

Source: ( )

Phase Coexistence in Insect Swarms

    Animal aggregations are visually striking, and as such are popular examples of collective behavior in the natural world. Quantitatively demonstrating the collective nature of such groups, however, remains surprisingly difficult. Inspired by thermodynamics, we applied topological data analysis to laboratory insect swarms and found evidence for emergent, material-like states. We show that the swarms consist of a core ˙˙condensed˙˙ phase surrounded by a dilute ˙˙vapor˙˙ phase. These two phases coexist in equilibrium, and maintain their distinct macroscopic properties even though individual insects pass freely between them. We further define a pressure and chemical potential to describe these phases, extending theories of active matter to aggregations of macroscopic animals and laying the groundwork for a thermodynamic description of collective animal groups.

Phase Coexistence in Insect Swarms
Michael Sinhuber and Nicholas T. Ouellette
Phys. Rev. Lett. 119, 178003 ˙˙ Published 24 October 2017

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Jobs: SFI Program Postdoc, Neural Network Models of Social Organizations | Santa Fe Institute 

SFI seeks a program postdoc to play a key role in a project using modern information theory and numerical optimization techniques to analyze social organizations, ranging from modern firms to military organizations to complex chiefdoms to primary states.

The starting point for the project is to identify the "organization'' of a social group as the communication network(s) within that group. To begin we will adopt a group-selection perspective, assuming that the social group's network structure is (approximately) optimal, given the information-processing limitations of the agents within the social group, and the exogenous welfare function of the overall group. We intend to leverage the computational power of neural networks to solve such problems. An expanded description of this project can be found at 

This project, led by the Santa Fe Institute, is collaboration among archaeologists, economists, and computer scientists. The ideal starting date is the spring of 2018, though that is flexible. The position would last for two years, with possible extensions.

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

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