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   Barry Wellman

    A vision is just a vision if it's only in your head
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   NetLab Network                 FRSC                      INSNA Founder           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman

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Date: Mon, 23 Jan 2017 12:02:55 +0000
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Subject: [utf-8] Latest Complexity Digest Posts

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

Dynamics on expanding spaces: modeling the emergence of novelties

    Novelties are part of our daily lives. We constantly adopt new 
technologies, conceive new ideas, meet new people, experiment with new 
situations. Occasionally, we as individuals, in a complicated cognitive 
and sometimes fortuitous process, come up with something that is not only 
new to us, but to our entire society so that what is a personal novelty 
can turn into an innovation at a global level. Innovations occur 
throughout social, biological and technological systems and, though we 
perceive them as a very natural ingredient of our human experience, little 
is known about the processes determining their emergence. Still the 
statistical occurrence of innovations shows striking regularities that 
represent a starting point to get a deeper insight in the whole 
phenomenology. This paper represents a small step in that direction, 
focusing on reviewing the scientific attempts to effectively model the 
emergence of the new and its regularities, with an emphasis on more recent 
contributions: from the plain Simon's model tracing back to the 1950s, to 
the newest model of Polya's urn with triggering of one novelty by another. 
What seems to be key in the successful modelling schemes proposed so far 
is the idea of looking at evolution as a path in a complex space, 
physical, conceptual, biological, technological, whose structure and 
topology get continuously reshaped and expanded by the occurrence of the 
new. Mathematically it is very interesting to look at the consequences of 
the interplay between the "actual" and the "possible" and this is the aim 
of this short review.

Dynamics on expanding spaces: modeling the emergence of novelties
Vittorio Loreto, Vito D. P. Servedio, Steven H. Strogatz, Francesca Tria

Source: (

Quantifying the diaspora of knowledge in the last century

    Academic research is driven by several factors causing different 
disciplines to act as ˙˙sources˙˙ or ˙˙sinks˙˙ of knowledge. However, how 
the flow of authors˙˙ research interests ˙˙ a proxy of human knowledge ˙˙ 
evolved across time is still poorly understood. Here, we build a 
comprehensive map of such flows across one century, revealing fundamental 
periods in the raise of interest in areas of human knowledge. We identify 
and quantify the most attractive topics over time, when a relatively 
significant number of researchers moved from their original area to 
another one, causing what we call a ˙˙diaspora of the knowledge˙˙ towards 
sinks of scientific interest, and we relate these points to crucial 
historical and political events. Noticeably, only a few areas ˙˙ like 
Medicine, Physics or Chemistry ˙˙ mainly act as sources of the diaspora, 
whereas areas like Material Science, Chemical Engineering, Neuroscience, 
Immunology and Microbiology or Environmental Science behave like sinks.

Quantifying the diaspora of knowledge in the last century
Manlio De Domenico, Elisa Omodei and Alex Arenas
Applied Network Science20161:15
DOI: 10.1007/s41109-016-0017-9

Source: (

The global dynamical complexity of the human brain network

    How much information do large brain networks integrate as a whole over 
the sum of their parts? Can the dynamical complexity of such networks be 
globally quantified in an information-theoretic way and be meaningfully 
coupled to brain function? Recently, measures of dynamical complexity such 
as integrated information have been proposed. However, problems related to 
the normalization and Bell number of partitions associated to these 
measures make these approaches computationally infeasible for large-scale 
brain networks. Our goal in this work is to address this problem. Our 
formulation of network integrated information is based on the 
Kullback-Leibler divergence between the multivariate distribution on the 
set of network states versus the corresponding factorized distribution 
over its parts. We find that implementing the maximum information 
partition optimizes computations. These methods are well-suited for large 
networks with linear stochastic dynamics. We compute the integrated 
information for both, the system˙˙s attractor states, as well as 
non-stationary dynamical states of the network. We then apply this 
formalism to brain networks to compute the integrated information for the 
human brain˙˙s connectome. Compared to a randomly re-wired network, we 
find that the specific topology of the brain generates greater information 

The global dynamical complexity of the human brain network
Xerxes D. Arsiwalla and Paul F. M. J. Verschure

Source: (

Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties

    There is a growing consensus that a fuller understanding of social 
cognition depends on more systematic studies of real-time social 
interaction. Such studies require methods that can deal with the complex 
dynamics taking place at multiple interdependent temporal and spatial 
scales, spanning sub-personal, personal, and dyadic levels of analysis. We 
demonstrate the value of adopting an extended multi-scale approach by 
re-analyzing movement time-series generated in a study of embodied dyadic 
interaction in a minimal virtual reality environment (a perceptual 
crossing experiment). Reduced movement variability revealed an 
interdependence between social awareness and social coordination that 
cannot be accounted for by either subjective or objective factors alone: 
it picks out interactions in which subjective and objective conditions are 
convergent (i.e., elevated coordination is perceived as clearly social, 
and impaired coordination is perceived as socially ambiguous). This 
finding is consistent with the claim that interpersonal interaction can be 
partially constitutive of direct social perception. Clustering statistics 
(Allan Factor) of salient events revealed fractal scaling. Complexity 
matching defined as the similarity between these scaling laws was 
significantly more pronounced in pairs of participants as compared to 
surrogate dyads. This further highlights the multi-scale and distributed 
character of social interaction and extends previous complexity matching 
results from dyadic conversation to non-verbal social interaction 
dynamics. Trials with successful joint interaction were also associated 
with an increase in local coordination. Consequently, a local coordination 
pattern emerges on the background of complex dyadic interactions in the 
PCE task and makes joint successful performance possible.

Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties
Leonardo Zapata-Fonseca, Dobromir Dotov, Ruben Fossion, and Tom Froese

Front. Psychol., 12 December 2016 |

Source: (

A Novel Procedure for Measuring Semantic Synergy

    One interesting characteristic of some complex systems is the formation 
of macro level constructions perceived as having features that cannot be 
reduced to their micro level constituents. This characteristic is 
considered to be the expression of synergy where the joint action of the 
constituents produces unique features that are irreducible to the 
constituents isolated behavior or their simple composition. The synergy, 
characterizing complex systems, has been well acknowledged but difficult 
to conceptualize and quantify in the context of computing the emerging 
meaning of various linguistic and conceptual constructs. In this paper, we 
propose a novel measure/procedure for quantifying semantic synergy. This 
measure draws on a general idea of synergy as has been proposed in 
biology. We validate this measure by providing evidence for its ability to 
predict the semantic transparency of linguistic compounds (Experiment 1) 
and the abstractness rating of nouns (Experiment 2).

A Novel Procedure for Measuring Semantic Synergy
Yair Neuman, Yiftach Neuman, and Yochai Cohen

Volume 2017 (2017), Article ID 5785617, 8 pages

Source: (

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