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Winter is still here in Toronto. Is that why ice hockey championship is 
still happening?

   Barry Wellman

   Step by step, link by link, putting it together--Streisand/Sondheim
        The earth to be spannd, connected by network--Walt Whitman
              It's Always Something--Roseanne Roseannadanna

              A day like all days, filled with those events
          that alter and illuminate our times--Walter Cronkite
   Director, NetLab Network      			            FRSC
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
         Founder, International Network for Social Network Analysis
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman    

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Date: Mon, 3 Jun 2019 11:02:02 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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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 

Worlds Hidden in Plain Sight 

Over the last three decades, the Santa Fe Institute and its network of researchers have been pursuing a revolution in science.

Ignoring the boundaries of disciplines and schools and searching for novel fundamental ideas, theories, and practices, this international community integrates the full range of scientific inquiries that will help us to understand and survive on a complex planet.

This volume collects essays from the past thirty years of research, in which contributors explain in clear and accessible language many of the deepest challenges and insights of complexity science.

Explore the evolution of complex systems science with chapters from Nobel Laureates Murray Gell-Mann and Kenneth Arrow, as well as numerous pioneering complexity researchers, including John Holland, Brian Arthur, Robert May, Richard Lewontin, Jennifer Dunne, and Geoffrey West.

Source:  ( )

What is the Entropy of a Social Organization?

    We quantify a social organization's potentiality, that is its ability to attain different configurations. The organization is represented as a network in which nodes correspond to individuals and (multi-)edges to their multiple interactions. Attainable configurations are treated as realizations from a network ensemble. To encode interaction preferences between individuals, we choose the generalized hypergeometric ensemble of random graphs, which is described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well different stages in the development of a given organization. The feasibility of the approach is demonstrated using data from 3 empirical and 2 synthetic systems.

What is the Entropy of a Social Organization?
Christian Zingg, Giona Casiraghi, Giacomo Vaccario, Frank Schweitzer

Source: ( )

Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity

    Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo
methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists˙˙ choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.

Devezer B, Nardin LG, Baumgaertner B, Buzbas EO (2019) Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity. PLoS ONE 14(5): e0216125. 

Source: ( )

Scale-free networks revealed from finite-size scaling

    Networks play a vital role in the development of predictive models of physical, biological, and social collective phenomena. A quite remarkable feature of many of these networks is that they are believed to be approximately scale free: the fraction of nodes with k incident links (the degree) follows a power law p(k)˙˙k˙˙˙˙ for sufficiently large degree k. The value of the exponent ˙˙ as well as deviations from power law scaling provide invaluable information on the mechanisms underlying the formation of the network such as small degree saturation, variations in the local fitness to compete for links, and high degree cutoffs owing to the finite size of the network. Indeed real networks are not infinitely large and the largest degree of any network cannot be larger than the number of nodes. Finite size scaling is a useful tool for analyzing deviations from power law behavior in the vicinity of a critical point in a physical system arising due to a finite correlation length. Here we show that
despite the essential differences between networks and critical phenomena, finite size scaling provides a powerful framework for analyzing self-similarity and the scale free nature of empirical networks. We analyze about two hundred naturally occurring networks with distinct dynamical origins, and find that a large number of these follow the finite size scaling hypothesis without any self-tuning. Notably this is the case of biological protein interaction networks, technological computer and hyperlink networks and informational citation and lexical networks. Marked deviations appear in other examples, especially infrastructure and transportation networks, but also social, affiliation and annotation networks. Strikingly, the values of the scaling exponents are not independent but satisfy an approximate exponential relationship.

Scale-free networks revealed from finite-size scaling
Matteo Serafino, Giulio Cimini, Amos Maritan, Samir Suweis, Jayanth R. Banavar, Guido Caldarelli

Source: ( )

Reconciling cooperation, biodiversity and stability in complex ecological communities 

Empirical evidences show that ecosystems with high biodiversity can 
persist in time even in the presence of few types of resources and are 
more stable than low biodiverse communities. This evidence is contrasted 
by the conventional mathematical modeling, which predicts that the 
presence of many species and/or cooperative interactions are detrimental 
for ecological stability and persistence. Here we propose a modelling 
framework for population dynamics, which also include indirect cooperative 
interactions mediated by other species (e.g. habitat modification). We 
show that in the large system size limit, any number of species can 
coexist and stability increases as the number of species grows, if 
mediated cooperation is present, even in presence of exploitative or 
harmful interactions (e.g. antibiotics). Our theoretical approach thus 
shows that appropriate models of mediated cooperation naturally lead to a 
solution of the long-standing question about complexity-stability paradox 
and on how highly biodiverse communities can coexist.

Source:  ( )

Friendship Paradox Biases Perceptions in Directed Networks

    How popular a topic or an opinion appears to be in a network can be 
very different from its actual popularity. For example, in an online 
network of a social media platform, the number of people who mention a 
topic in their posts---i.e., its global popularity---can be dramatically 
different from how people see it in their social feeds---i.e., its 
perceived popularity---where the feeds aggregate their friends' posts. We 
trace the origin of this discrepancy to the friendship paradox in directed 
networks, which states that people are less popular than their friends (or 
followers) are, on average. We identify conditions on network structure 
that give rise to this perception bias, and validate the findings 
empirically using data from Twitter. Within messages posted by Twitter 
users in our sample, we identify topics that appear more frequently within 
the users' social feeds, than they do globally, i.e., among all posts. In 
addition, we present a polling algorithm that leverages the friendship 
paradox to obtain a statistically efficient estimate of a topic's global 
prevalence from biased perceptions of individuals. We characterize the 
bias of the polling estimate, provide an upper bound for its variance, and 
validate the algorithm's efficiency through synthetic polling experiments 
on our Twitter data. Our paper elucidates the non-intuitive ways in which 
the structure of directed networks can distort social perceptions and 
resulting behaviors.

Friendship Paradox Biases Perceptions in Directed Networks
Nazanin Alipourfard, Buddhika Nettasinghe, Andres Abeliuk, Vikram Krishnamurthy, Kristina Lerman

Source: ( )

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

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