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Only 10 days until the return of decent pop music on the radio.

   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--You Are There!
   Director, NetLab Network      			            FRSC
         Founder, International Network for Social Network Analysis
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman    

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Date: Mon, 16 Dec 2019 12:02:01 +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 

Survey: AI adoption proves its worth, but few scale impact

    Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers with AI capabilities show the way.

Source:  ( )

Editorial: Novel Technological and Methodological Tools for the Understanding of Collective Behaviors

    Elio Tuci1, Vito Trianni, Andrew King and Simon Garnier

Front. Robot. AI, 10 December 2019

The social processes that give rise to coordinated actions of a group of agents and the emergence of global structures˙˙referred to as collective behaviors˙˙are observed in a range of biological and artificial systems. Collective behavior research, therefore, focuses upon a range of different phenomena with the common goal of understanding the dynamics of emergent group level responses, and has resulted in a burgeoning, diverse, and interdisciplinary research community.

Studying collective behaviors in biological and artificial systems is particularly challenging because of their intrinsic complexity, requiring novel approaches that can help unraveling these systems in order to explain how and why certain patterns are produced and maintained. This Research Topic brings together a collection of studies that focus on technological and methodological tools that can support the understanding of collective behaviors. The contributions included within the Research Topic can be broadly categorized as: (i) Review Articles, (ii) Tools and Technologies, and (iii) Empirical Studies.

Our goal is to facilitate the dissemination of ideas, theories, and methods among scientists that share an interest on the study of collective behavior in all its diverse manifestations. It is our hope that, together, this Research Topic and contributions may afford a more complete understanding of the nature of proximate and ultimate causes of collective behaviors in biological systems, and provide opportunity to generate a theoretical framework to engineer robust, resilient, and effective technologies, such as multi-robot systems, smart grids, and sensor networks.

Source:  ( )

Understanding and reducing the spread of misinformation online

    Gordon Pennycook, Ziv Epstein, Mohsen Mosleh, Antonio Arechar, Dean Eckles, David Rand

The spread of false and misleading news on social media is of great societal concern. Why do people share such content, and what can be done about it? In a first survey experiment (N=1,015), we demonstrate a disconnect between accuracy judgments and sharing intentions: even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. We argue against a ˙˙post-truth˙˙ interpretation, whereby people deliberately share false content because it furthers their political agenda. Instead, we propose that the problem is simply distraction: most people do not want to spread misinformation, but are distracted from accuracy by other salient motives when choosing what to share. Indeed, when directly asked, most participants say it is important to only share accurate news. Accordingly, across three survey experiments (total N=2775) and an experiment on Twitter in which we messaged N=5,482 users who had previously shared news from misleading
websites, we find that subtly inducing people to think about the concept of accuracy increases the quality of the news they share. Together, these results challenge the popular post-truth narrative. Instead, they suggest that many people are capable of detecting low-quality news content, but nonetheless share such content online because social media is not conducive to thinking analytically about truth and accuracy. Furthermore, our results translate directly into a scalable anti-misinformation intervention that is easily implementable by social media platforms.

Source: ( )

Modeling somatic computation with non-neural bioelectric networks 

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to
recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.

Source:  ( )

Dynamical Inference of Simple Heteroclinic Networks

    Maximilian Voit and Hildegard Meyer-Ortmanns

Front. Appl. Math. Stat., 10 December 2019

Heteroclinic networks are structures in phase space that consist of multiple saddle fixed points as nodes, connected by heteroclinic orbits as edges. They provide a promising candidate attractor to generate reproducible sequential series of metastable states. While from an engineering point of view it is known how to construct heteroclinic networks to achieve certain dynamics, a data based approach for the inference of heteroclinic dynamics is still missing. Here, we present a method by which a template system dynamically learns to mimic an input sequence of metastable states. To this end, the template is unidirectionally, linearly coupled to the input in a master-slave fashion, so that it is forced to follow the same sequence. Simultaneously, its eigenvalues are adapted to minimize the difference of template dynamics and input sequence. Hence, after the learning procedure, the trained template constitutes a model with dynamics that are most similar to the training data. We demonstrate the
performance of this method at various examples, including dynamics that differ from the template, as well as a regular and a random heteroclinic network. In all cases the topology of the heteroclinic network is recovered precisely, as are most eigenvalues. Our approach may thus be applied to infer the topology and the connection strength of a heteroclinic network from data in a dynamical fashion. Moreover, it may serve as a model for learning in systems of winnerless competition.

Source:  ( )

Transitivity and degree assortativity explained: The bipartite structure of social networks

    Demival Vasques Filho, Dion R. J. O'Neale

Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news", innovation, and others, are highly dependent on the structure of the social network on which they occur. However, questions on why most social networks present some particular structural features, namely high levels of
transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network --- together with the presence of cycles of length four and six --- explains the observed levels of transitivity and degree assortativity in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.

Source: ( )

Digital Fingerprints of Cognitive Reflection

    Mohsen Mosleh, Gordon Pennycook, Antonio Arechar, David Rand

Social media is playing an increasingly large role in everyday life. Thus, it is of both scientific and practical interest to understand behavior on social media platforms. Furthermore, social media provides a unique window for social scientists to deepen our understanding of the human mind. Here we investigate the relationship between individual differences in cognitive reflection and behavior on Twitter in a sample of large N = 1,953 users recruited via Prolific Academic. In doing so, we differentiate between two competing accounts of human information processing: an ˙˙intuitionist˙˙ account whereby reflection plays little role in daily life, and a ˙˙reflectionist˙˙ account whereby reflection (and, in particular, overriding intuitive responses) does play an important role. We found that people who score higher on the Cognitive Reflection Test (CRT) ˙˙ a widely used measure of reflective thinking ˙˙ were more discerning in their social media use: They followed more selectively, shared news
content from more reliable sources, and tweeted about weightier subjects. Furthermore, a network analysis indicated that the phenomenon of echo chambers, in which discourse is more likely with like-minded others, is not limited to politics: we observe ˙˙cognitive echo chambers˙˙ in which people low on cognitive reflection tend to follow the same set of accounts. Our results help to illuminate the drivers of behavior on social media platforms, and challenge intuitionist notions that reflective thinking is unimportant for everyday judgment and decision-making.

Source: ( )

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

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