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Tired of hearing that Spring is coming tomorrow when the temp is in the 
minuses (Celsius, of course)

   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
    The earth to be spannd, connected by network -- Walt Whitman
        It's Always Something -- Roseanne Roseannadanna
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman  

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Date: Mon, 19 Mar 2018 12:02:47 +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

Antagonistic Phenomena in Network Dynamics

Recent research on the network modeling of complex systems has led to a convenient representation of numerous natural, social, and engineered systems that are now recognized as networks of interacting parts. Such systems can exhibit a wealth of phenomena that not only cannot be anticipated from merely examining their parts, as per the textbook definition of complexity, but also challenge intuition even when considered in the context of what is now known in network science. Here, we review the recent literature on two major classes of such phenomena that have far-reaching implications: (a) antagonistic responses to changes of states or parameters and (b) coexistence of seemingly incongruous behaviors or properties˙˙both deriving from the collective and inherently decentralized nature of the dynamics. They include effects as diverse as negative compressibility in engineered materials, rescue interactions in biological networks, negative resistance in fluid networks, and the
Braess paradox occurring across transport and supply networks. They also include remote synchronization, chimera states, and the converse of symmetry breaking in brain, power-grid, and oscillator networks as well as remote control in biological and bioinspired systems. By offering a unified view of these various scenarios, we suggest that they are representative of a yet broader class of unprecedented network phenomena that ought to be revealed and explained by future research.

Antagonistic Phenomena in Network Dynamics
Adilson E. Motter and Marc Timme

Annual Review of Condensed Matter Physics
Vol. 9:463-484 (Volume publication date March 2018)

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Assessing Human Judgment of Computationally Generated Swarming Behavior

Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects˙˙ assessment of the behavior of a simplified version of Reynolds˙˙ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but
finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.

Assessing Human Judgment of Computationally Generated Swarming Behavior

John Harvey, Kathryn Elizabeth Merrick and Hussein A. Abbass

Front. Robot. AI, 22 February 2018 |

Source: (

Social norm complexity and past reputations in the evolution of cooperation

Indirect reciprocity is the most elaborate and cognitively demanding1 ( of all known cooperation mechanisms2 ( , and is the most specifically human1 ( ,3 ( because it involves reputation and status. By helping someone, individuals may increase their reputation, which may change the predisposition of others to help them in future. The revision of an individual˙˙s reputation depends on the social norms that establish what characterizes a good or bad action and thus provide a basis for morality3 ( . Norms based on indirect reciprocity are often sufficiently complex that an individual˙˙s ability to follow subjective rules becomes important4 ( ,5
( ,6 ( , even in models that disregard the past reputations of individuals, and reduce reputations to either ˙˙good˙˙ or ˙˙bad˙˙ and actions to binary decisions7 ( ,8 ( . Here we include past reputations in such a model and identify the key pattern in the associated norms that promotes cooperation. Of the norms that comply with this pattern, the one that leads to maximal cooperation (greater than 90 per cent) with minimum complexity does not discriminate on the basis of past reputation; the relative performance of this norm is particularly evident when we consider a ˙˙complexity cost˙˙ in the decision process. This combination of high cooperation and low complexity suggests that simple moral principles can elicit cooperation even in complex environments.

Social norm complexity and past reputations in the evolution of cooperation
Fernando P. Santos, Francisco C. Santos & Jorge M. Pacheco
Nature volume 555, pages 242˙˙245 (08 March 2018)

Source: (

Turing Lecture: Better living through trusted data ˙˙ Alex ˙˙Sandy˙˙ Pentland, MIT Media Lab

Big Data, AI, and social media echo chambers can feel scary, but if harnessed correctly they can dramatically improve our quality of life. The potential for improvement comes first from better scientific understanding of our human minds and bodies, and second from a more open and shared understanding of society, government, and our day-to-day lives. The key to achieving these positive results is aggressive pursuit of a new, broad science of human life to unify the traditional and narrow sciences, and making data a trusted and safe resource for everyone. We are building such systems today, and are changing ˙˙business as usual˙˙ for governments around the world, as well as beginning to unify fragmented social and computational sciences.

Source: (

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