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Complexity - Complex Systems Theory
Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. The equations from which models of complex systems are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of natural systems that are considered fundamentally complex. wikipedia (en)
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Complex networks analysis in socioeconomic models (v1) UPDATED (see link)

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.

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updated  to v2 : http://arxiv.org/pdf/1403.6767v2

 

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Signals and Boundaries: Building Blocks for Complex Adaptive Systems (by John H. Holland)

Signals and Boundaries: Building Blocks for Complex Adaptive Systems

~ John H. Holland (author) More about this product
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Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about "steering" these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies.

Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.


Via Complexity Digest, António F Fonseca
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Costas Bouyioukos's curator insight, March 18, 1:41 PM

John Holland's new book!

António F Fonseca's curator insight, March 23, 5:23 AM

Why communicate, why not, for example, just command?

june holley's curator insight, March 23, 7:43 AM

Just got this. His stuff is usually excellent so I have high hopes.

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Nasa-funded study: industrial civilisation headed for 'irreversible collapse'?

Nasa-funded study: industrial civilisation headed for 'irreversible collapse'? | Complexity - Complex Systems Theory | Scoop.it

Nafeez Ahmed: Natural and social scientists develop new model of how 'perfect storm' of crises could unravel global system

 

 

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Predictability of extreme events in social media

It is part of our daily social-media experience that seemingly ordinary items (videos, news, publications, etc.) unexpectedly gain an enormous amount of attention. Here we investigate how unexpected these events are. We propose a method that, given some information on the items, quantifies the predictability of events, i.e., the potential of identifying in advance the most successful items defined as the upper bound for the quality of any prediction based on the same information. Applying this method to different data, ranging from views in YouTube videos to posts in Usenet discussion groups, we invariantly find that the predictability increases for the most extreme events. This indicates that, despite the inherently stochastic collective dynamics of users, efficient prediction is possible for the most extreme events.
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Complexity Science and the concept of "Social Cognition"

Complexity Science and the concept of "Social Cognition" | Complexity - Complex Systems Theory | Scoop.it
Activities such as distributed collaboration are becoming more common as organizations become geographically diverse and they have important consequences when the collective group makes important decisions.

Via Roger D. Jones, PhD
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Kaya, l'équation qui calcule l'avenir de l'humanité

Kaya, l'équation qui calcule l'avenir de l'humanité | Complexity - Complex Systems Theory | Scoop.it
Comprendre l'équation de Kaya, qui permet de dynamiser les rapports entre les composantes d'un écosystème, est primordial pour penser l'avenir de l'espèce.
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PLOS ONE Complex systems articles

PLOS ONE Complex systems articles | Complexity - Complex Systems Theory | Scoop.it

PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.


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Urban Emergencies : Emergent Urbanism

Urban Emergencies : Emergent Urbanism | Complexity - Complex Systems Theory | Scoop.it

Urban Emergencies : Emergent Urbanism (UE:EU) is an independent research group exploring international and interdisciplinary perspectives on the implications of emergent risks on the built environment and its inhabitants.

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How to Save Human Lives with Complexity Science

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are not effective and sufficient to contain them. The failure of many conventional approaches results from their neglection of feedback loops, instabilities and/or cascade effects, due to which equilibrium models do often not provide a good picture of the actual system behavior. However, the complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be understood by means of complexity science, which enables one to address the aforementioned problems more successfully. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

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Complexity Explorer -- Course Syllabi

The Course Syllabi database contains a collection of annotated links to course syllabi related to complex systems. These syllabi can be searched according to class topics, institution, instructor, education level, and several other attributes. These syllabi will be useful for instructors developing their own courses on various topics, as well as serving as guides to people who want to learn on their own.


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Reflexivity, complexity, and the nature of social science

Reflexivity, complexity, and the nature of social science | Complexity - Complex Systems Theory | Scoop.it

In 1987, George Soros introduced his concepts of reflexivity and fallibility and has further developed and applied these concepts over subsequent decades. This paper attempts to build on Soros's framework, provide his concepts with a more precise definition, and put them in the context of recent thinking on complex adaptive systems. The paper proposes that systems can be classified along a ‘spectrum of complexity’ and that under specific conditions not only social systems but also natural and artificial systems can be considered ‘complex reflexive.’ The epistemological challenges associated with scientifically understanding a phenomenon stem not from whether its domain is social, natural, or artificial, but where it falls along this spectrum. Reflexive systems present particular challenges; however, evolutionary model-dependent realism provides a bridge between Soros and Popper and a potential path forward for economics.

 

 

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Fractal Geometry

Fractal Geometry | Complexity - Complex Systems Theory | Scoop.it

"I find the ideas in the fractals, both as a body of knowledge and as a metaphor, an incredibly important way of looking at the world." Vice President and Nobel Laureate Al Gore, New York Times, Wednesday, June 21, 2000, discussing some of the "big think" questions that intrigue him.

This is a collection of pages meant to support a first course in fractal geometry for students without especially strong mathematical preparation, or any particular interest in science.
Each of the topics contains examples of fractals in the arts, humanities, or social sciences; these and other examples are collected in the panorama.
Fractal geometry is a new way of looking at the world; we have been surrounded by natural patterns, unsuspected but easily recognized after only an hour's training.

 

 

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Scientific method: Statistical errors

Scientific method: Statistical errors | Complexity - Complex Systems Theory | Scoop.it
P values, the 'gold standard' of statistical validity, are not as reliable as many scientists assume.

Via Eric L Berlow
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Eric L Berlow's curator insight, February 21, 12:55 PM

Nice perspective on the use and mis-use of p-values

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The story of the Gömböc | plus.maths.org

The story of the Gömböc | plus.maths.org | Complexity - Complex Systems Theory | Scoop.it

A Gömböc is a strange thing. It looks like an egg with sharp edges, and when you put it down it starts wriggling and rolling around with an apparent will of its own. Until quite recently, no-one knew whether Gömböcs even existed. Even now, Gábor Domokos, one of their discoverers, reckons that in some sense they barely exists at all. So what are Gömböcs and what makes them special?

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Why Model? Joshua M. Epstein

Why Model? Joshua M. Epstein | Complexity - Complex Systems Theory | Scoop.it

This lecture treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and 'summarize' data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom. It is based on the author's 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute.

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António F Fonseca's curator insight, March 23, 5:20 AM

The classical paper about modelling and simulation. Very clear.

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Power-law distributions in binned empirical data

Many man-made and natural phenomena, including the intensity of earthquakes, population of cities, and size of international wars, are believed to follow power-law distributions. The accurate identification of power-law patterns has significant consequences for developing an understanding of complex systems. However, statistical evidence for or against the power-law hypothesis is complicated by large fluctuations in the empirical distribution's tail, and these are worsened when information is lost from binning the data. We adapt the statistically principled framework for testing the power-law hypothesis, developed by Clauset, Shalizi and Newman, to the case of binned data. This approach includes maximum-likelihood fitting, a hypothesis test based on the Kolmogorov-Smirnov goodness-of-fit statistic and likelihood ratio tests for comparing against alternative explanations. We evaluate the effectiveness of these methods on synthetic binned data with known structure and apply them to twelve real-world binned data sets with heavy-tailed patterns.
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Scaling and Predictability in Stock Markets: A Comparative Study

Scaling and Predictability in Stock Markets: A Comparative Study | Complexity - Complex Systems Theory | Scoop.it

Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US) stock market is a representative of emerging (developed) markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling.

 

 

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Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System

Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System | Complexity - Complex Systems Theory | Scoop.it

Social systems have recently attracted much attention, with attempts to understand social behavior with the aid of statistical mechanics applied to complex systems. Collective properties of such systems emerge from couplings between components, for example, individual persons, transportation nodes such as airports or subway stations, and administrative districts. Among various collective properties, criticality is known as a characteristic property of a complex system, which helps the systems to respond flexibly to external perturbations. This work considers the criticality of the urban transportation system entailed in the massive smart card data on the Seoul transportation network. Analyzing the passenger flow on the Seoul bus system during one week, we find explicit power-law correlations in the system, that is, power-law behavior of the strength correlation function of bus stops and verify scale invariance of the strength fluctuations. Such criticality is probed by means of the scaling and renormalization analysis of the modified gravity model applied to the system. Here a group of nearby (bare) bus stops are transformed into a (renormalized) “block stop” and the scaling relations of the network density turn out to be closely related to the fractal dimensions of the system, revealing the underlying structure. Specifically, the resulting renormalized values of the gravity exponent and of the Hill coefficient give a good description of the Seoul bus system: The former measures the characteristic dimensionality of the network whereas the latter reflects the coupling between distinct transportation modes. It is thus demonstrated that such ideas of physics as scaling and renormalization can be applied successfully to social phenomena exemplified by the passenger flow.

 

 

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PLOS ONE Nonlinear dynamics articles

PLOS ONE Nonlinear dynamics articles | Complexity - Complex Systems Theory | Scoop.it

PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.


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Cybernetics and Information Theory in the United States, France and the Soviet Union

Mindell et al. (2003) «Cybernetics and Information Theory in the United States, France and the Soviet Union» http://t.co/WxxvghtyUz

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António F Fonseca's curator insight, March 1, 7:58 AM

Very interesting document!

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ICCSA 2014

ICCSA 2014 | Complexity - Complex Systems Theory | Scoop.it
ICCSA 2014The 4th International Conference on Complex Systems and ApplicationsJune 23-26, 2014Le Havre, France
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Complexity Explorer -- Resources

The Resources section contains annotated links to a wide variety of web-based resources related to complex systems. These include journals, conferences, tutorials, software, videos, among other types of resources that will be useful for all levels of interest.


Via Complexity Digest, Bill Aukett
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KONECT - The Koblenz Network Collection

KONECT - The Koblenz Network Collection | Complexity - Complex Systems Theory | Scoop.it

KONECT is the Koblenz Network Collection. KONECT is a project to collect large network datasets of all types in order to perform research in the area of network mining, collected by the Institute of Web Science and Technologies of the University of Koblenz–Landau. KONECT contains over a hundred network datasets of various types.

A network as provided by KONECT is a set of nodes connected by links. An example of a network is a social network: a set of users connected by links which represent friendship relations. A network is represented mathematically by a graph, in which nodes are called vertices and links are called edges.

Most networks are asymmetric: The fact that user A follows user B on the microblogging site Twitter does not imply that user B follows user A. The Twitter graph is thus directed. In the DBLP authorship network, scientific publications are connected to their authors. The DBLP publication network thus has two classes of nodes; it is bipartite.

KONECT provides:

Code to generate all network datasets from the Web
Statistics and plots viewable online
Download of selected datasets (where legally possible)

To be added in the future:

Analysis code to generate all statistics and plots

 

 

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Power laws - Stanford Complexity Group

Power laws - Stanford Complexity Group | Complexity - Complex Systems Theory | Scoop.it

Class description:

We've all heard the buzzwords - chaos, fractals, networks, power laws.  What do these terms mean in a rigorous, mathematical sense? This 1-2 credit seminar will explore formalisms associated with the study of complex systems. These include non-linear dynamics (and their associated phase space mappings, as well as chaos), graph theory (networks), and fractals (and their associated power laws). Through readings, in-class problem sets, and hands-on computer-based simulations, we will pursue a concrete understanding of these concepts as well as the ability to implement them as mathematical tools. A basic course in calculus and differential equations and some coding experience would be helpful but is not required.

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Motility at the Origin of Life: Its Characterization and a Model

Motility at the Origin of Life: Its Characterization and a Model | Complexity - Complex Systems Theory | Scoop.it

Due to recent advances in synthetic biology and artificial life, the origin of life is currently a hot topic of research. We review the literature and argue that the two traditionally competing replicator-first and metabolism-first approaches are merging into one integrated theory of individuation and evolution. We contribute to the maturation of this more inclusive approach by highlighting some problematic assumptions that still lead to an ximpoverished conception of the phenomenon of life. In particular, we argue that the new consensus has so far failed to consider the relevance of intermediate time scales. We propose that an adequate theory of life must account for the fact that all living beings are situated in at least four distinct time scales, which are typically associated with metabolism, motility, development, and evolution. In this view, self-movement, adaptive behavior, and morphological changes could have already been present at the origin of life. In order to illustrate this possibility, we analyze a minimal model of lifelike phenomena, namely, of precarious, individuated, dissipative structures that can be found in simple reaction-diffusion systems. Based on our analysis, we suggest that processes on intermediate time scales could have already been operative in prebiotic systems. They may have facilitated and constrained changes occurring in the faster- and slower-paced time scales of chemical self-individuation and evolution by natural selection, respectively.

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