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SOCNET  October 2015

SOCNET October 2015

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 26 Oct 2015 09:34:02 -0400

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MULTIPART/MIXED

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



---------- Forwarded message ----------
Date: Mon, 26 Oct 2015 12:03:30 +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=e85af054d9&e=55e25a0e3e

Making big data work: smart, sustainable, and safe cities

    The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the ˙˙Telecom Italia Big Data Challenge 2014˙˙ and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness.

Making big data work: smart, sustainable, and safe cities
Bruno Lepri, Fabrizio Antonelli, Fabio Pianesi and Alex Pentland

EPJ Data Science 2015, 4:16  http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3e3d917559&e=55e25a0e3e  (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e22d1794a4&e=55e25a0e3e) ;
Quantifying randomness in real networks

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks˙˙the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain˙˙and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of
network randomness, and release software to generate dk-random graphs.

Quantifying randomness in real networks
˙˙ Chiara Orsini, Marija M. Dankulov, Pol Colomer-de-Simón, Almerima Jamakovic, Priya Mahadevan, Amin Vahdat, Kevin E. Bassler, Zoltán Toroczkai, Marián Boguñá, Guido Caldarelli, Santo Fortunato & Dmitri Krioukov

Nature Communications 6, Article number: 8627 http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f460f787f1&e=55e25a0e3e

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Neuroscience: Connectomes make the map

    The subjects studied by connectome researchers range from living people 
to the preserved brains of tiny animals such as worms and flies. The 
investigative technologies range from MRI scanners to light microscopes 
and electron microscopes. Irrespective of the specifics, scientists ˙˙ 
with the aid of computers ˙˙ painstakingly chart connections to build an 
atlas. The map-makers hope that revealing the connectome's structure will 
help neuroscientists to navigate as they work out how different parts of 
the brain function together.

Neuroscience: Connectomes make the map
Amber Dance
Nature 526, 147˙˙149 (01 October 2015) http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c853e0b4f9&e=55e25a0e3e

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Inequality and visibility of wealth in experimental social network

    Humans prefer relatively equal distributions of resources1, 2, 3, 4, 5, 
yet societies have varying degrees of economic inequality6. To investigate 
some of the possible determinants and consequences of inequality, here we 
perform experiments involving a networked public goods game in which 
subjects interact and gain or lose wealth. Subjects (n = 1,462) were 
randomly assigned to have higher or lower initial endowments, and were 
embedded within social networks with three levels of economic inequality 
(Gini coefficient = 0.0, 0.2, and 0.4). In addition, we manipulated the 
visibility of the wealth of network neighbours. We show that wealth 
visibility facilitates the downstream consequences of initial 
inequality˙˙in initially more unequal situations, wealth visibility leads 
to greater inequality than when wealth is invisible. This result reflects 
a heterogeneous response to visibility in richer versus poorer subjects. 
We also find that making wealth visible has adverse welfare consequences, 
yielding lower levels of overall cooperation, inter-connectedness, and 
wealth. High initial levels of economic inequality alone, however, have 
relatively few deleterious welfare effects.

Inequality and visibility of wealth in experimental social networks
˙˙ Akihiro Nishi, Hirokazu Shirado, David G. Rand & Nicholas A. Christakis

Nature 526, 426˙˙429 (15 October 2015) http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=08c7f84545&e=55e25a0e3e

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Doctoral Program in Complexity Sciences

    The Doctoral Program in Complexity Sciences provides an integrated training that enable doctoral students understand the environment in which they live, by applying modelling methods and computer simulation, and solve complex problems using information technology, including support systems to organizational processes in complex environments. Developing these skills will enable the integration of multidisciplinary knowledge and the autonomously formulation of judgements from data that is often incomplete.

The Doctoral Program in Complexity Sciences is taught in ISCTE and FCUL. It has an international dimension based on a set of protocols to the Paris-Dauphine University (France), with the University of Savoie (France) and the Academy of Economic Studies of Bucharest (Romania). There are teachers of exchanges with the University Paul Sabatier in Toulouse (France), with the Open University (UK), with the University of Utrecht (Netherlands) and the University of Texas (USA).


The new curriculum comprehends a 1st curricular year and a 2nd and 3rd years mainly dedicated to research at PhD level. Students are invited to develop their research projects at LabMAg (FCUL, Lisbon), ISTAR (ISCTE-IUL, Lisbon), and IITGn (Gandhinagar, India).


A new class for the 1st curricular year will start in February 2016. Classes will take place on Tuesdays and Wednesdays, from 18h to 21h30.


Applications for the 1st curricular year are open until the 23rd of December, 2015. Applications are submitted through the form available at the ISCTE-IUL applications website.

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Classification and unification of the microscopic deterministic traffic models

    We identify a universal mathematical structure in microscopic 
deterministic traffic models (with identical drivers), and thus we show 
that all such existing models in the literature, including both the 
two-phase and three-phase models, can be understood as special cases of a 
master model by expansion around a set of well-defined ground states. This 
allows any two traffic models to be properly compared and identified. The 
three-phase models are characterized by the vanishing of leading orders of 
expansion within a certain density range, and as an example the popular 
intelligent driver model is shown to be equivalent to a generalized 
optimal velocity (OV) model. We also explore the diverse solutions of the 
generalized OV model that can be important both for understanding human 
driving behaviors and algorithms for autonomous driverless vehicles.


Classification and unification of the microscopic deterministic traffic models

Bo Yang and Christopher Monterola
Phys. Rev. E 92, 042802 ˙˙ Published 2 October 2015

DOI http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3e6844cae2&e=55e25a0e3e



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Cross-boundary Behavioural Reprogrammability of Cellular Automata from Emulation Networks

    We explore the reprogramming capabilities of computer programs using cellular automata (CA). We show a series of boundary crossing results, including a Wolfram Class 1 Elementary Cellular Automaton (ECA) emulating a Class 2 ECA, a Class 2 ECA emulating a Class 3 ECA, and a Class 3 ECA emulating a Class 2 ECA, along with results of a similar type for general CA (neighbourhood range r=3/2), including a Class 1 CA emulating a Class 3 CA, Classes 3 and 4 CAs emulating Class 4 CAs, and Class 4 emulating Class 3 CAs. All these emulations occur with only a constant overhead, and hence are computationally efficient. By constructing emulation networks through an exhaustive search in the compiler space, we show that topological properties determining emulation direction, such as ingoing and outgoing hub degrees, suggest a topological classification of class 4 behaviour consistent with Turing universality conjectures. We also found that no hacking strategy based on compiler complexity
or compiler similarity is suggested. We also introduce a definition of prime rules applicable to CAs-- analogous to that of prime numbers--according to which CA rules act as basic constructors of all other rules. We show a Turing universality result of a composition of ECA rules emulating rule 110. The approach yields a novel perspective on complexity, controllability, causality, and reprogrammability of even the simplest computer programs providing strong evidence that computation universality is ubiquitous. The results suggest that complexity is, or can be, generally driven by initial conditions, and are therefore in this sense more fundamental than even the underlying rules that we show can asymptotically carry any desired computation.

Cross-boundary Behavioural Reprogrammability of Cellular Automata from Emulation Networks
Jürgen Riedel, Hector Zenil

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=0edb94dc03&e=55e25a0e3e

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    Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong
and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman˙˙s original method which had evidently gone unnoticed since its publication over a decade ago.

Pham T, Sheridan P, Shimodaira H (2015) PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks. PLoS ONE 10(9): e0137796. http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4f1a9f407a&e=55e25a0e3e

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The Centered Mind: What the Science of Working Memory Shows Us About the Nature of Human Thought (by Peter Carruthers)

    The Centered Mind offers a new view of the nature and causal 
determinants of both reflective thinking and, more generally, the stream 
of consciousness. Peter Carruthers argues that conscious thought is always 
sensory-based, relying on the resources of the working-memory system. This 
system has been much studied by cognitive scientists. It enables sensory 
images to be sustained and manipulated through attentional signals 
directed at midlevel sensory areas of the brain. When abstract conceptual 
representations are bound into these images, we consciously experience 
ourselves as making judgments or arriving at decisions. Thus one might 
hear oneself as judging, in inner speech, that it is time to go home, for 
example. However, our amodal (non-sensory) propositional attitudes are 
never actually among the contents of this stream of conscious reflection. 
Our beliefs, goals, and decisions are only ever active in the background 
of consciousness, working behind the scenes to select the sensory-based 
imagery that occurs in working memory. They are never themselves 
conscious.

Drawing on extensive knowledge of the scientific literature on working memory and related topics, Carruthers builds an argument that challenges the central assumptions of many philosophers. In addition to arguing that non-sensory propositional attitudes are never conscious, he also shows that they are never under direct intentional control. Written with his usual clarity and directness, The Centered Mind will be essential reading for all philosophers and cognitive scientists interested in the nature of human thought processes.



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Postdoc position: Real-time social event prediction using Big Data

    We are seeking two mathematical and computational modellers to join the "Beat the News" Program of the Data to Decisions Cooperative Research Centre (D2D), based in the School of Mathematical Sciences at the University of Adelaide. D2D is a Cooperative Research Centre with over 20 partners across academia, industry and government, which seeks to address the Big Data challenges facing Australia's defence, national security and law enforcement agencies. The Beat the News program seeks to research, develop, integrate and evaluate technology that will automatically and accurately predict the occurrence of future events such as disease outbreaks, civil unrest, political crises, and election outcomes.
The successful applicants will work with Dr. Lewis Mitchell, who leads two project streams within the "Beat the News" program of D2D, on the topics of using open data for:
(i)  disease prediction using epidemiological models and data assimilation, and
(ii)  civil unrest and election prediction using Bayesian network models.

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What You Gotta Know to Play Good in the Iterated Prisoner˙˙s Dilemma

    Here we describe all the memory-one good strategies for the non-symmetric version of the Prisoner˙˙s Dilemma.  We discuss the special advantages and problems associated with some specific good strategies.

What You Gotta Know to Play Good in the Iterated Prisoner˙˙s Dilemma
Ethan Akin

Games 2015, 6(3), 175-190; http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=1af074cf62&e=55e25a0e3e

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Ants 2016: Tenth International Conference on Swarm Intelligence

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=e0251438a7&e=55e25a0e3e

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The 3rd Tokyo Network Workshop

    The 3rd Tokyo Network Workshop

International and interdisciplinary meeting of researchers sharing recent advances in methods and practical applications of research in complex networks.

31 October 2015, Waseda University, Tokyo

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==============================================
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=17d481ad85&e=55e25a0e3e ) and using the "Suggest" button.
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