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SOCNET  May 2015

SOCNET May 2015

Subject:

Selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 18 May 2015 08:58:25 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (125 lines)

*****  To join INSNA, visit http://www.insna.org  *****

Happy Victoria Day
   Barry Wellman
  _______________________________________________________________________
   FRSC                 INSNA Founder               University of Toronto
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED:The New Social Operating System.  Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39        Print $14  Kindle $9
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 18 May 2015 11:04:19 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
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://comdig.unam.mx?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033

The Intrafirm Complexity of Systemically Important Financial Institutions

    In November, 2011, the Financial Stability Board, in collaboration with the International Monetary Fund, published a list of 29 "systemically important financial institutions" (SIFIs). This designation reflects a concern that the failure of any one of them could have dramatic negative consequences for the global economy and is based on "their size, complexity, and systemic interconnectedness". While the characteristics of "size" and "systemic interconnectedness" have been the subject of a good deal of quantitative analysis, less attention has been paid to measures of a firm's "complexity." In this paper we take on the challenges of measuring the complexity of a financial institution and to that end explore the use of the structure of an individual firm's control hierarchy as a proxy for institutional complexity. The control hierarchy is a network representation of the institution and its subsidiaries. We show that this mathematical representation (and various associated
metrics) provides a consistent way to compare the complexity of firms with often very disparate business models and as such may provide the foundation for determining a SIFI designation. By quantifying the level of complexity of a firm, our approach also may prove useful should firms need to reduce their level of complexity either in response to business or regulatory needs. Using a data set containing the control hierarchies of many of the designated SIFIs, we find that in the past two years, these firms have decreased their level of complexity, perhaps in response to regulatory requirements.

The Intrafirm Complexity of Systemically Important Financial Institutions
Robin L. Lumsdaine, Daniel N. Rockmore, Nicholas Foti, Gregory Leibon, J. Doyne Farmer

http://arxiv.org/abs/1505.02305?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033

See it on Scoop.it (http://www.scoop.it/t/papers/p/4043535143/2015/05/15/the-intrafirm-complexity-of-systemically-important-financial-institutions?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033) , via Papers (http://www.scoop.it/t/papers?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033)



On the Optimality and Predictability of Cultural Markets with Social Influence

    Social influence is ubiquitous in cultural markets, from book recommendations in Amazon, to song popularities in iTunes and the ranking of newspaper articles in the online edition of the New York Times to mention only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability.
Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consider a simple policy which ranks the products by quality when presenting them to market participants. We show that, in this setting, market efficiency always benefits from social influence. Moreover, we prove that the market converges almost surely to a monopoly for the product of highest quality, making the market both predictable and asymptotically optimal. Computational experiments confirm that the quality ranking policy identifies "blockbusters" in reasonable time, outperforms other policies, and is highly predictable. These results indicate that social influence does not necessarily increase market unpredicatibility. The outcome really depends on how social influence is used.

On the Optimality and Predictability of Cultural Markets with Social Influence
Pascal Van Hentenryck, Andres Abeliuk, Franco Berbeglia, Gerardo Berbeglia

http://arxiv.org/abs/1505.02469?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033

See it on Scoop.it (http://www.scoop.it/t/papers/p/4043535171/2015/05/14/on-the-optimality-and-predictability-of-cultural-markets-with-social-influence?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033) , via Papers (http://www.scoop.it/t/papers?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033)


Duality between Temporal Networks and Signals: Extraction of the Temporal Network Structures

    We develop a framework to track the structure of temporal networks with a signal processing approach. The method is based on the duality between networks and signals using a multidimensional scaling technique. This enables a study of the network structure using frequency patterns of the corresponding signals. An extension is proposed for temporal networks, thereby enabling a tracking of the network structure over time. A method to automatically extract the most significant frequency patterns and their activation coefficients over time is then introduced, using nonnegative matrix factorization of the temporal spectra. The framework, inspired by audio decomposition, allows transforming back these frequency patterns into networks, to highlight the evolution of the underlying structure of the network over time. The effectiveness of the method is first evidenced on a toy example, prior being used to study a temporal network of face-to-face contacts. The extraction of sub-networks
highlights significant structures decomposed on time intervals.

Duality between Temporal Networks and Signals: Extraction of the Temporal Network Structures
Ronan Hamon, Pierre Borgnat, Patrick Flandrin, Céline Robardet

http://arxiv.org/abs/1505.03044?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033

See it on Scoop.it (http://www.scoop.it/t/papers/p/4043537002/2015/05/13/duality-between-temporal-networks-and-signals-extraction-of-the-temporal-network-structures?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033) , via Papers (http://www.scoop.it/t/papers?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033)



Multifractal to monofractal evolution of the London's street network

    We perform a multifractal analysis of the evolution of London's street network from 1786 to 2010. First, we show that a single fractal dimension, commonly associated with the morphological description of cities, does not su ce to capture the dynamics of the system. Instead, for a proper characterization of such a dynamics, the multifractal spectrum needs to be considered. Our analysis reveals that London evolves from an inhomogeneous fractal structure, that can be described in terms of a multifractal, to a homogeneous one, that converges to monofractality. We argue that London's multifractal to monofracal evolution might be a special outcome of the constraint imposed on its growth by a green belt. Through a series of simulations, we show that multifractal objects, constructed through di usion limited aggregation, evolve towards monofractality if their growth is constrained by a non-permeable boundary.

Multifractal to monofractal evolution of the London's street network
Roberto Murcio, A. Paolo Masucci, Elsa Arcaute, Michael Batty

http://arxiv.org/abs/1505.02760?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033

See it on Scoop.it (http://www.scoop.it/t/papers/p/4043536017/2015/05/13/multifractal-to-monofractal-evolution-of-the-london-s-street-network?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033) , via Papers (http://www.scoop.it/t/papers?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033)



Improving measures of topological robustness in networks of networks and suggestion of a novel way to counter both failure propagation and isolation

    The study of interdependent complex networks in the last decade has shown how cascading failure can result in the recursive and complete fragmentation of all connected systems from the destruction of a comparatively small number of nodes. Existing ˙˙network of networks˙˙ approaches are still in infancy and have shown limits when trying to model the robustness of real-world systems, due to simplifying assumptions regarding network interdependencies and post-attack viability. In order to increase the realism of such models, we challenge such assumptions by validating the following four hypotheses trough experimental results obtained from computer based simulations. Firstly, we suggest that, in the case of network topologies vulnerable to fragmentation, replacing the standard measure of robustness based on the size of the one largest remaining connected component by a new measure allowing secondary components to remain viable when measuring post-attack viability can make a
significant improvement to the model. Secondly, we show that it is possible to influence the way failure propagation is balanced between coupled networks while keeping the same overall robustness score by allowing nodes in a given network to have multiple counter parts in another network. Thirdly, we challenge the generalised assumption that partitioning between networks is a good way to increase robustness and find that isolation is a force as equally destructive as the iterative propagation of cascading failure. This result significantly alters where the optimum robustness lies in the balance between isolation and inter-network coupling in such interconnected systems. Finally, we propose a solution to the consequent problem of seemingly ever increasing vulnerability of interdependent networks to both cascading failure and isolation: the use of permutable nodes that would give such systems rewiring capabilities. This last concept could have wide implications when trying to
improve the topological resilience of natural or engineered interdependent networks.

Improving measures of topological robustness in networks of networks and suggestion of a novel way to counter both failure propagation and isolation
Mehdi Khoury, Seth Bullock, Gaihua Fu and Richard Dawson

Infrastructure Complexity 2015, 2:1

http://dx.doi.org/10.1186/s40551-015-0004-9?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033 ;

See it on Scoop.it (http://www.scoop.it/t/papers/p/4043534054/2015/05/13/improving-measures-of-topological-robustness-in-networks-of-networks-and-suggestion-of-a-novel-way-to-counter-both-failure-propagation-and-isolation?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033) , via Papers (http://www.scoop.it/t/papers?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033)



Knowledge Machines: Digital Transformations of the Sciences and Humanities (by Eric T. Meyer & Ralph Schroeder)

    In Knowledge Machines, Eric Meyer and Ralph Schroeder argue that digital technologies have fundamentally changed research practices in the sciences, social sciences, and humanities. Meyer and Schroeder show that digital tools and data, used collectively and in distributed mode -- which they term e-research -- have transformed not just the consumption of knowledge but also the production of knowledge. Digital technologies for research are reshaping how knowledge advances in disciplines that range from physics to literary analysis.

Meyer and Schroeder map the rise of digital research and offer case studies from many fields, including biomedicine, social science uses of the Web, astronomy, and large-scale textual analysis in the humanities. They consider such topics as the challenges of sharing research data and of big data approaches, disciplinary differences and new forms of interdisciplinary collaboration, the shifting boundaries between researchers and their publics, and the ways that digital tools promote openness in science.

This book considers the transformations of research from a number of perspectives, drawing especially on the sociology of science and technology and social informatics. It shows that the use of digital tools and data is not just a technical issue; it affects research practices, collaboration models, publishing choices, and even the kinds of research and research questions scholars choose to pursue. Knowledge Machines examines the nature and implications of these transformations for scholarly research.



See it on Scoop.it (http://www.scoop.it/t/cxbooks/p/4043077222/2015/05/11/knowledge-machines-digital-transformations-of-the-sciences-and-humanities-by-eric-t-meyer-ralph-schroeder?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033) , via CxBooks (http://www.scoop.it/t/cxbooks?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033)



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

You can contribute to Complexity Digest selecting one of our topics (http://www.scoop.it/u/complexity-digest?utm_source=Complexity+Digest&utm_campaign=2148736194-RSS_EMAIL_CAMPAIGN&utm_medium=email&utm_term=0_f55ea67de1-2148736194-67211033 ) and using the "Suggest" button.
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