Complexity At Large


  • Carlos Gershenson


The following news item is taken in part from the 2013/01 issue of arXiv titled “Systemic delay propagation in the US airport network,” by Pablo Fleurquin, Jose J. Ramasco, and Victor M. Eguiluz.

Technologically driven transport systems are characterized by a networked structure connecting operation centers and by a dynamics ruled by pre-established schedules. Schedules impose serious constraints on the timing of the operations, condition the allocation of resources, and define a baseline to assess system performance. Here we study the performance of an air transportation system in terms of delays. Technical, operational, or meteorological issues affecting some flights give rise to primary delays. When operations continue, such delays can propagate, magnify, and eventually involve a significant part of the network. We define metrics able to quantify the level of network congestion and introduce a model that reproduces the delay propagation patterns observed in the US performance data. Our results indicate that there is a non-negligible risk of systemic instability even under normal operating conditions. We also identify passenger and crew connectivity as the most relevant internal factor contributing to delay spreading.

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The following news item is taken in part from the 2013/01 issue of Computers & Mathematics with Applications titled “Modeling complex systems with adaptive networks,” by Hiroki Sayama, Irene Pestov, Jeffrey Schmidt, Benjamin James Bush, Chun Wong, Junichi Yamanoi, and Thilo Gross.

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks, and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of recent literature on mathematical/computational modeling and analysis of such networks. We also report our recent work on several applications of computational adaptive network modeling and analysis to real-world problems, including temporal development of search and rescue operational networks, automated rule discovery from empirical network evolution data, and cultural integration in corporate merger.

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The following news item is taken in part from the 2013/01 issue of arXiv titled “Evolutionary dynamics of group interactions on structured populations: A review,” by Matjaz Perc, Jesús Gómez-Gardeñes, Attila Szolnoki, Luis M. Floría, and Yamir Moreno.

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and nonliving matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proven valuable for studying pattern formation, equilibrium selection, and self-organization in evolutionary games. Here we review recent advances in the study of evolutionary dynamics of group interactions on structured populations, including lattices, complex networks, and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical, and social sciences, benefits from the synergy between statistical physics, network science, and evolutionary game theory.

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The following news item is taken in part from the 2013/01 issue of arXiv titled “City boundaries and the universality of scaling laws,” by Elsa Arcaute, Erez Hatna, Peter Ferguson, Hyejin Youn, Anders Johansson, and Michael Batty.

This paper investigates the universality and robustness of scaling laws for urban systems, according to the work by Bettencourt, Lobo, and West among others, using England and Wales as a case study. Initial results employing the demarcations for cities from the European Statistical Commission digress from the expected patterns. We therefore develop a method for producing multiple city definitions based on both morphological and functional characteristics, determined by population density and commuting to work journeys. For each of these realizations of cities, we construct urban attributes by aggregating high resolution census data. The approach produces a set of more than twenty thousand possible definitions of urban systems for England and Wales. We use these as a laboratory to explore the behavior of the scaling exponent for each configuration. The analysis of a large set of urban indicators for the full range of system realizations shows that the scaling exponent is notably sensitive to boundary change, particularly for indicators that have a nonlinear relationship with population size. These findings highlight the crucial role of system description when attempting to identify patterns of behavior across cities, and the need for consistency in defining boundaries if a theory of cities is to be devised.

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The following news item is taken in part from the 2013/01/10 issue of Nature titled “Tipping points: From patterns to predictions,” by Carl Boettiger and Alan Hastings.

There has been much talk about tipping points over the past few years, and about the warning signals that may precede them. You could be forgiven for thinking that the forecasting of epidemics and stock-market crashes is just around the corner. But no one has yet managed to use the theory on early warning signals to predict a natural catastrophe. The rewards of bridging the gap between the real world and mathematical conceptualizations of catastrophic shifts would be vast. Climate scientists might be able to foresee major shifts in the ocean currents with a rise in global temperatures; ecologists could potentially stave off pest outbreaks; and policies might be implemented to avert the collapse of fisheries (…) But for such applications to emerge, researchers should resist the lure of general rules. We must instead use all the available data to develop tools to study the specific properties of real systems.

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The following news item is taken in part from the 2013/01 issue of PLoS Biol titled “Evolutionary Biology for the 21st Century,” by Jonathan B. Losos, et al.

We live in an exciting time for biology. Technological advances have made data collection easier and cheaper than we could ever have imagined just 10 years ago. We can now synthesize and analyze large data sets containing genomes, transcriptomes, proteomes, and multivariate phenotypes. At the same time, society's need for the results of biological research has never been greater. Solutions to many of the world's most pressing problems—feeding a global population, coping with climate change, preserving ecosystems and biodiversity, curing and preventing genetically based diseases—will rely heavily on biologists, collaborating across disciplines.

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The following news item is taken in part from the 2013/01 issue of Futures titled “Complexity, the Science of Cities and Long-range Futures,” by Robert Hugh Samet.

The emergence of a “science of cities” provides the foundations for long-range futures research that may be applied to models of climate change, with a time horizon in excess of 150 years. The features of a complexity theory of cities have been developed at multiple levels with scientific analogies such as ecology, biology, and physics. The following principles apply: (1) Complexity science unifies a wide variety of urban phenomena including emergence, technological evolution, civil phase transitions, macrolaws, and resilience to system failures and extreme events. (2) World urbanization raises the number of levels in the urban hierarchy, with an increasing number of megacities with over 10 million inhabitants. (3) Urban development involves the institutional coordination of technological development with engineered transformations. (4) Civil and societal transitions arise with increasing per capita investment, such that some social norms and planning standards have consistent scaling factors across a range of city sizes for countries at similar stages of development. (5) The trajectory of the urban system depends upon the allometric pattern of growth for cities, and human settlements in 2150 will occupy less than 10% of the world's land area.

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The following news item is taken in part from the 2013/01 issue of SFI Working Papers titled “Urban Scaling in Prehispanic Central Mexico,” by Scott G. Ortman, Andrew Cabaniss, and Luís M. A. Bettencourt.

Despite the fact that cities are increasingly the fundamental socioeconomic units of human societies worldwide, a unified quantitative framework concerning urban form and function has yet to be established. As a step in this direction, we analyze settlement data from the Prehispanic Basin of Mexico to show that this system displays spatial scaling properties analogous to those observed for modern cities. Our data derive from some 1,400 settlements occupied over two millennia and spanning four major cultural periods characterized by different levels of political centralization and socioeconomic development. We show that, for each period, total settlement area increases with population size according to a scale invariant relation, with exponent α = 2/3–5/6, in agreement with expectations of emerging theory. These findings, from an urban system that evolved independently from old-world cities, suggest that principles of human settlement organization are very general and may apply to the entire range of human history.

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The following news item is taken in part from the 2012/12 issue of SFI Working Papers titled “The Evolutionary Ecology of Technological Innovations,” by Ricard V. Solé, Sergi Valverde, Marti Rosas Casals, Stuart Kauffman, Doyne Farmer, and Niles Eldredge.

Technological evolution has been compared to biological evolution by many authors over the last two centuries. As a parallel experiment of innovation involving economic, historical, and social components, artifacts define a universe of evolving properties that displays episodes of diversification and extinction. Here we critically review previous work comparing the two types of evolution. Like biological evolution, technological evolution is driven by descent with variation and selection, and includes tinkering, convergence, and contingency. At the same time there are essential differences that make the two types of evolution quite distinct. Major distinctions are illustrated by current specific examples, including the evolution of cornets and the historical dynamics of information technologies. Due to their fast and rich development, the later provide a unique opportunity to study technological evolution at all scales with unprecedented resolution. Despite the presence of patterns suggesting convergent trends between man-made systems end biological ones, they provide examples of planned design that have no equivalent with natural evolution.

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The following news item is taken in part from the 2013/01 issue of Nature titled “Cytosystems Dynamics in Self-organization of Tissue Architecture,” by Yoshiki Sasai.

Our knowledge of the principles by which organ architecture develops through complex collective cell behaviors is still limited. Recent work has shown that the shape of such complex tissues as the optic cup forms by self-organization in vitro from a homogeneous population of stem cells. Multicellular self-organization involves three basic processes that are crucial for the emergence of latent intrinsic order. Based on lessons from recent studies, cytosystems dynamics is proposed as a strategy for understanding collective multicellular behaviors, incorporating four-dimensional measurement, theoretical modeling, and experimental reconstitution.

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The following news item is taken in part from the 2013/01 issue of arXiv titled “Ecosystems Perspective on Financial Networks: Diagnostic Tools,” by Eduardo Viegas, Misako Takayasu, Wataru Miura, Koutarou Tamura, Takaaki Ohnishi, Hideki Takayasu, and Henrik Jeldtoft Jensen.

The economical world consists of a highly interconnected and interdependent network of firms. Here we develop temporal and structural network tools to analyze the state of the economy. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dynamics surrounding the crash in 2008, is seen as a key emergent feature arising naturally from the evolutionary and adaptive dynamics inherent to the financial markets. Similarly, collusion amongst construction firms in a number of regions in Japan in the 2000s can be identified with the formation of clusters of anomalous highly connected companies.

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The following news item is taken in part from the 2013/01 issue of Scientific Reports titled “'Chaotic Ising-like Dynamics in Traffic Signals,” by Hideyuki Suzuki, Jun-ichi Imura, and Kazuyuki Aihara.

The green and red lights of a traffic signal can be viewed as the up and down states of an Ising spin. Moreover, traffic signals in a city interact with each other, if they are controlled in a decentralized way. In this paper, a simple model of such interacting signals on a finite-size two-dimensional lattice is shown to have Ising-like dynamics that undergoes a ferromagnetic phase transition. Probabilistic behavior of the model is realized by chaotic billiard dynamics that arises from coupled non-chaotic elements. This purely deterministic model is expected to serve as a starting point for considering statistical mechanics of traffic signals.

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