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More time to peruse the selected abstracts below. Full set is in their own 

   Barry Wellman

    A vision is just a vision if it's only in your head
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   NetLab Network                 FRSC                      INSNA Founder           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman

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Learn about the latest and greatest related to complex systems research. More at

Zipf˙˙s law, unbounded complexity and open-ended evolution

    A major problem for evolutionary theory is understanding the so called {\em open-ended} nature of evolutionary change. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterise evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Several theoretical and computational approaches have been developed to properly characterise OEE. Interestingly, many complex systems displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's law. Given and inventory of basic items required to build more complex structures Zipf's law tells us that most of these elements are rare whereas a few of them are extremely common. Using Algorithmic Information Theory, in this paper we provide a
fundamental definition for open-endedness, which can be understood as {\em postulates}. Its statistical counterpart, based on standard Shannon Information theory, has the structure of a variational problem which is shown to lead to Zipf's law as the expected consequence of an evolutionary processes displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting into the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems.

Zipf's law, unbounded complexity and open-ended evolution

Bernat Corominas-Murtra, Luís Seoane, Ricard Solé

Source: (

Sequence of purchases in credit card data reveal life styles in urban populations

    From our most basic consumption to secondary needs, our spending habits reflect our life styles. Yet, in computational social sciences there is an open question about the existence of ubiquitous trends in spending habits by various groups at urban scale. Limited information collected by expenditure surveys have not proven conclusive in this regard. This is because, the frequency of purchases by type is highly uneven and follows a Zipf-like distribution. In this work, we apply text compression techniques to the purchase codes of credit card data to detect the significant sequences of transactions of each user. Five groups of consumers emerge when grouped by their similarity based on these sequences. Remarkably, individuals in each consumer group are also similar in age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, we find that
it can give us insights on collective behavior.

Sequence of purchases in credit card data reveal life styles in urban populations
Riccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Bapu Vaitla, Marta C. Gonzalez

Source: (

The many facets of community detection in complex networks

    Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines
of research and points out open directions and avenues for future research.

The many facets of community detection in complex networks
Michael T. SchaubEmail author, Jean-Charles Delvenne, Martin Rosvall and Renaud Lambiotte
Applied Network Science20172:4
DOI: 10.1007/s41109-017-0023-6

Source: (

Collective Learning in China˙˙s Regional Economic Development

    Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of China's economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. First, we show that the probability that a province will develop a new industry increases with the number of related industries that are already present in that province, a fact that is suggestive of inter-industry learning. Also, we show that the probability that a province will develop an industry increases with the number of neighboring
provinces that are developed in that industry, a fact suggestive of inter-regional learning. Moreover, we find that the combination of these two channels exhibit diminishing returns, meaning that the contribution of either of these learning channels is redundant when the other one is present. Finally, we address endogeneity concerns by using the introduction of high-speed rail as an instrument to isolate the effects of inter-regional learning. Our differences-in-differences (DID) analysis reveals that the introduction of high speed-rail increased the industrial similarity of pairs of provinces connected by high-speed rail. Also, industries in provinces that were connected by rail increased their productivity when they were connected by rail to other provinces where that industry was already present. These findings suggest that inter-regional and inter-industry learning played a role in China's great economic expansion.

Collective Learning in China's Regional Economic Development
Jian Gao, Bogang Jun, Alex "Sandy" Pentland, Tao Zhou, Cesar A. Hidalgo

Source: (

How Brain Scientists Forgot That Brains Have Owners

Five neuroscientists argue that fancy new technologies have led the field astray.

Source: (

Second Conference on Network models and stress testing for financial stability

Banco de México, the University of Zurich, Bank of Canada and the Journal of Financial Stability continue with the series of biennial conferences addressing novel research on network models and stress testing for financial stability.

The development of network and stress testing models have proven to be useful in achieving a better understanding of systemic risk. These approaches have been applied to study the implications of changes in the regulatory landscape, to understand and detect new threats to the stability of financial systems, and other financial stability related topics.

The conference aims to bring together policymakers and academics as well as industry representatives to examine progress in designing a safer financial system, to study the intended and unintended consequences of regulation on the global financial system, and to explore recent methodological advances in the study of systemic risk.

Mexico City, 2017/09/26-27

Source: (

Adaptive Local Information Transfer in Random Boolean Networks

Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The
stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.

Adaptive Local Information Transfer in Random Boolean Networks

Taichi Haruna

Artificial Life

Winter 2017, Vol. 23, No. 1, Pages: 105-118
Posted Online February 27, 2017.

Source: (

Analyzing the coevolution of interorganizational networks and organizational performance: Automakers˙˙ production networks in Japan

    Organizations create networks with one another, and these networks may in turn shape the organizations involved. Until recently, such complex dynamic processes could not be rigorously empirically analyzed because of a lack of suitable modeling and validation methods. Using stochastic actor-oriented models and unique longitudinal survey data on the changing structure of interfirm production networks in the automotive industry in Japan, this paper illustrates how to quantitatively assess and validate (1) the dynamic micro-mechanism by which organizations form their networks and (2) the role of the dynamic network structures in organizational performance. The applied model helps to explain the endogenous processes behind the recent diversification of Japanese automobile production networks. Specifically, testing the effects of network topology and network diffusion on organizational performance, the novel modeling framework enables us to discern that the restructuring of
interorganizational networks led to the increase of Japanese automakers˙˙ production per employee, and not the reverse. Traditional models that do not allow for interaction between interorganizational structure and organizational agency misrepresent this mechanism.

Analyzing the coevolution of interorganizational networks and organizational performance: Automakers˙˙ production networks in Japan

Matous, P. & Todo, Y. Appl Netw Sci (2017) 2: 5. doi:10.1007/s41109-017-0024-5

Source: (

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