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   Barry Wellman
  _______________________________________________________________________
   FRSC                 INSNA Founder               University of Toronto
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED:The New Social Operating System.  Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39        Print $14  Kindle $9
   _______________________________________________________________________

Misery loves company: happiness and communication in the city

    The high population density in cities confers many advantages, 
including improved social interaction and information exchange. However, 
it is often argued that urban living comes at the expense of reducing 
happiness. The goal of this research is to shed light on the relationship 
between urban communication and urban happiness. We analyze geo-located 
social media posts (tweets) within a major urban center (Milan) to produce 
a detailed spatial map of urban sentiments. We combine this data with 
high-resolution mobile communication intensity data among different urban 
areas. Our results reveal that happy (respectively unhappy) areas 
preferentially communicate with other areas of their type. This 
observation constitutes evidence of homophilous communities at the scale 
of an entire city (Milan), and has implications on interventions that aim 
to improve urban well-being.

Misery loves company: happiness and communication in the city
Alshamsi A, Awad E, Almehrezi M, Babushkin V, Chang P, Shoroye Z, Tóth A, Rahwan I
EPJ Data Science 2015, 4 :7

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Partitioning Uncertain Workflows

    It is common practice to partition complex workflows into separate channels in order to speed up their completion times. When this is done within a distributed environment, unavoidable fluctuations make individual realizations depart from the expected average gains. We present a method for breaking any complex workflow into several workloads in such a way that once their outputs are joined, their full completion takes less time and exhibit smaller variance than when running in only one channel. We demonstrate the effectiveness of this method in two different scenarios; the optimization of a convex function and the transmission of a large computer file over the Internet.

Partitioning Uncertain Workflows
Bernardo A. Huberman, Freddy C. Chua

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Cascades in multiplex financial networks with debts of different seniority

    A model of a banking network predicts the balance of high- and low-priority debts that ensures financial stability.


Synopsis: http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d976b3e3d1&e=55e25a0e3e


Cascades in multiplex financial networks with debts of different seniority


The seniority of debt, which determines the order in which a bankrupt institution repays its debts, is an important and sometimes contentious feature of financial crises, yet its impact on systemwide stability is not well understood. We capture seniority of debt in a multiplex network, a graph of nodes connected by multiple types of edges. Here an edge between banks denotes a debt contract of a certain level of seniority. Next we study cascading default. There exist multiple kinds of bankruptcy, indexed by the highest level of seniority at which a bank cannot repay all its debts. Self-interested banks would prefer that all their loans be made at the most senior level. However, mixing debts of different seniority levels makes the system more stable in that it shrinks the set of network densities for which bankruptcies spread widely. We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erd˙˙s-Rényi networks. If
institutions erode their buffer against insolvency, then this optimal seniority ratio rises; in other words, if default thresholds fall, then more loans should be senior. We generalize the analytical results to arbitrarily many levels of seniority and to heavy-tailed degree distributions.


Charles D. Brummitt and Teruyoshi Kobayashi

Phys. Rev. E 91, 062813 (2015)

Published June 24, 2015

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Jigsaw percolation: What social networks can collaboratively solve a puzzle?

    We introduce a new kind of percolation on finite graphs called jigsaw 
percolation. This model attempts to capture networks of people who 
innovate by merging ideas and who solve problems by piecing together 
solutions. Each person in a social network has a unique piece of a jigsaw 
puzzle. Acquainted people with compatible puzzle pieces merge their puzzle 
pieces. More generally, groups of people with merged puzzle pieces merge 
if the groups know one another and have a pair of compatible puzzle 
pieces. The social network solves the puzzle if it eventually merges all 
the puzzle pieces. For an Erd˙˙s˙˙Rényi social network with n vertices and 
edge probability p_n, we define the critical value p_c(n) for a connected 
puzzle graph to be the p_n for which the chance of solving the puzzle 
equals 1/2. We prove that for the n-cycle (ring) puzzle, p_c(n)=˙˙(1/log 
n), and for an arbitrary connected puzzle graph with bounded maximum 
degree, p_c(n)=O(1/log n) and ˙˙(1/n^b)for any b>0. Surprisingly, with 
probability tending to 1 as the network size increases to infinity, social 
networks with a power-law degree distribution cannot solve any 
bounded-degree puzzle. This model suggests a mechanism for recent 
empirical claims that innovation increases with social density, and it 
might begin to show what social networks stifle creativity and what 
networks collectively innovate.

Brummitt, Charles D.; Chatterjee, Shirshendu; Dey, Partha S.; Sivakoff, David. Jigsaw percolation: What social networks can collaboratively solve a puzzle?. Ann. Appl. Probab. 25 (2015), no. 4, 2013--2038. doi:10.1214/14-AAP1041.http://projecteuclid.org/euclid.aoap/1432212435.

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Invisible Hands: Self-Organization and the Eighteenth Century (by Jonathan Sheehan & Dror Wahrman)

    Why is the world orderly, and how does this order come to be? Human 
beings inhabit a multitude of apparently ordered systems˙˙natural, social, 
political, economic, cognitive, and others˙˙whose origins and purposes are 
often obscure. In the eighteenth century, older certainties about such 
orders, rooted in either divine providence or the mechanical operations of 
nature, began to fall away. In their place arose a new appreciation for 
the complexity of things, a new recognition of the world˙˙s disorder and 
randomness, new doubts about simple relations of cause and effect˙˙but 
with them also a new ability to imagine the world˙˙s orders, whether 
natural or manmade, as self-organizing. If large systems are left to their 
own devices, eighteenth-century Europeans increasingly came to believe, 
order will emerge on its own without any need for external design or 
direction.

In Invisible Hands, Jonathan Sheehan and Dror Wahrman trace the many appearances of the language of self-organization in the eighteenth-century West. Across an array of domains, including religion, society, philosophy, science, politics, economy, and law, they show how and why this way of thinking came into the public view, then grew in prominence and arrived at the threshold of the nineteenth century in versatile, multifarious, and often surprising forms. Offering a new synthesis of intellectual and cultural developments, Invisible Hands is a landmark contribution to the history of the Enlightenment and eighteenth-century culture.



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Challenges in Data Science  a complex systems perspectiv International Conference

    October 14-17 2015
Castello del Valentino
Torino, Italy

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Data Science is a key ingredient for understanding the increasingly complex social, economic & technological systems. Number and size of the relevant datasets is growing and becoming increasingly heterogeneous. Financial systems, map and mobility data, urban space usage, human  behavior and infrastructure are increasingly linked to each other to provide services to each single user of the global community  worldwide. Data and platform access  can add clues to solve a number of issues, in order to  achieve efficient interoperability and performances. However, data access may imply a number of risks, intrinsically related to the individual privacy and social security.

The main objective of the meeting is the  exploration of the multiple methodological intersections that have been devised in the diverse areas to provide insights regarding e.g. acquisition and analysis of complex networks, resilience and vulnerability, cybersecurity and privacy. Data Science & Complex Systems Science can borrow new ideas and techniques from each other contributing to the synergetic comprehension of both disciplines.  Complex Systems Science is mainly expected to contribute a new paradigms for representing and extracting information about structures and dynamics characterized by interacting elements, thus providing new clues in classical data mining tasks like classification or regression.  Ultimate aim of the meeting is to discuss current understanding and devise further applications of data science in  mapping complex networks evolution and interaction.

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Toward a Unified Ecology (by Timothy F. H. Allen & Thomas W. Hoekstra)

    The first edition of Toward a Unified Ecology was ahead of its time. For the second edition, the authors present a new synthesis of their core ideas on evaluating communities, organisms, populations, biomes, models, and management. The book now places greater emphasis on post-normal critiques, cognizant of ever-present observer values in the system. The problem it addresses is how to work holistically on complex things that cannot be defined, and this book continues to build an approach to the problem of scaling in ecosystems. Provoked by complexity theory, the authors add a whole new chapter on the central role of narrative in science and how models improve them. The book takes data and modeling seriously, with a sophisticated philosophy of science.



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

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