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excerpts
Barry Wellman
A vision is just a vision if it's only in your head
Step by step, link by link, putting it together
Streisand/Sondheim
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NetLab Network FRSC INSNA Founder
http://www.chass.utoronto.ca/~wellman twitter: @barrywellman
NETWORKED: The New Social Operating System Lee Rainie & Barry Wellman
http://amzn.to/zXZg39
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Date: Mon, 9 May 2016 11:03:48 +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 http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b4b471f987&e=55e25a0e3e
Untangling performance from success
Fame, popularity and celebrity status, frequently used tokens of
success, are often loosely related to, or even divorced from professional
performance. This dichotomy is partly rooted in the difficulty to
distinguish performance, an individual measure that captures the actions
of a performer, from success, a collective measure that captures a
community˙˙s reactions to these actions. Yet, finding the relationship
between the two measures is essential for all areas that aim to
objectively reward excellence, from science to business. Here we quantify
the relationship between performance and success by focusing on tennis, an
individual sport where the two quantities can be independently measured.
We show that a predictive model, relying only on a tennis player˙˙s
performance in tournaments, can accurately predict an athlete˙˙s
popularity, both during a player˙˙s active years and after retirement.
Hence the model establishes a direct link between performance and
momentary popularity. The agreement between the performance-driven and
observed popularity suggests that in most areas of human achievement
exceptional visibility may be rooted in detectable performance measures.
Untangling performance from success
Burcu Yucesoy and Albert-László Barabási
EPJ Data Science20165:17
http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5c4ca15eb0&e=55e25a0e3e
Complexity Digest's insight:
See Also http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d7af2bb817&e=55e25a0e3e (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=955e10591d&e=55e25a0e3e)
See it on Scoop.it (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d11c0d118b&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7ff081e67b&e=55e25a0e3e)
Combining complex networks and data mining: why and how
The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual
differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have be used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex networks metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.
Combining complex networks and data mining: why and how
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, S. Boccaletti
http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=35ed1d5950&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2e4e51dac7&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a36b9af57e&e=55e25a0e3e)
An Evolutionary Game Theoretic Approach to Multi-Sector Coordination and Self-Organization
Coordination games provide ubiquitous interaction paradigms to frame human behavioral features, such as information transmission, conventions and languages as well as socio-economic processes and institutions. By using a dynamical approach, such as Evolutionary Game Theory (EGT), one is able to follow, in detail, the self-organization process by which a population of individuals coordinates into a given behavior. Real socio-economic scenarios, however, often involve the interaction between multiple co-evolving sectors, with specific options of their own, that call for generalized and more sophisticated mathematical frameworks. In this paper, we explore a general EGT approach to deal with coordination dynamics in which individuals from multiple sectors interact. Starting from a two-sector, consumer/producer scenario, we investigate the effects of including a third co-evolving sector that we call public. We explore the changes in the self-organization process of all sectors,
given the feedback that this new sector imparts on the other two.
An Evolutionary Game Theoretic Approach to Multi-Sector Coordination and Self-Organization
Fernando P. Santos, Sara Encarnaçăo, Francisco C. Santos, Juval Portugali and Jorge M. Pacheco
Entropy 2016, 18(4), 152; http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=320245a927&e=55e25a0e3e
See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a2ac5496ed&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=578f50c711&e=55e25a0e3e)
Past the power law: Complex systems and the limiting law of restricted diversity
Probability distributions have proven effective at modeling diversity in complex systems. The two most common are the Gaussian normal and skewed-right. While the mechanics of the former are well-known; the latter less so, given the significant limitations of the power-law. Moving past the power-law, we demonstrate that there exists, hidden-in-full-view, a limiting law governing the diversity of complexity in skewed-right systems; which can be measured using a case-based version of Shannon entropy, resulting in a 60/40 rule. For our study, given the wide range of approaches to measuring complexity (i.e., descriptive, constructive, etc), we examined eight different systems, which varied significantly in scale and composition (from galaxies to genes). We found that skewed-right complex systems obey the law of restricted diversity; that is, when plotted for a variety of natural and human-made systems, as the diversity of complexity (primarily in terms of the number of types;
but also, secondarily, in terms of the frequency of cases) a limiting law of restricted diversity emerges, constraining the majority of cases to simpler types. Even more compelling, this limiting law obeys a scale-free 60/40 rule: when measured using , 60%(or more) of the cases in these systems reside within the first 40% (or less) of the lower bound of equiprobable diversity types˙˙with or without long-tail and whether or not the distribution fits a power-law. Furthermore, as an extension of the Pareto Principle, this lower bound accounts for only a small percentage of the total diversity; that is, while the top 20% of cases constitute a sizable percentage of the total diversity in a system, the bottom 60% are highly constrained. In short, as the central limit theorem governs the diversity of complexity in normal distributions, restricted diversity seems to govern the diversity of complexity in skewed-right distributions.
Past the power law: Complex systems and the limiting law of restricted diversity
Brian Castellani and Rajeev Rajaram
Complexity
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Complex Motions and Chaos in Nonlinear Systems
This book brings together 12 chapters on a new stream of research examining complex phenomena in nonlinear systems˙˙including engineering, physics, and social science. Complex Motions and Chaos in Nonlinear Systems provides readers a particular vantage of the nature and nonlinear phenomena in nonlinear dynamics that can develop the corresponding mathematical theory and apply nonlinear design to practical engineering as well as the study of other complex phenomena including those investigated within social science.
See it on Scoop.it (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=76c47849a9&e=55e25a0e3e) , via CxBooks (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=dc533c5593&e=55e25a0e3e)
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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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