Complejidad - Sistemas Complejos (libros, papers)
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Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data - Sebans Curve

Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data - Sebans Curve | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it
Complejidady Economía's insight:

DT statistics,
DT time-frequency analysis, and 
DT low-dimensional reductions 
The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems.

 

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Complejidad - Sistemas Complejos (libros, papers)
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Identifying Self-Organization and Adaptability in Complex Adaptive Systems

Self-organization and adaptability are critical properties of complex adaptive systems (CAS), and their analysis provides insight into the design of these systems, consequently leading to real-world advancements. However, these properties are difficult to analyze in real-world scenarios due to performance constraints, metric design, and limitations in existing modeling tools. Several metrics have been proposed for their identification, but metric effectiveness under the same experimental settings has not been studied before. In this paper we present an observation tool, part of a complex adaptive systems modeling framework, that allows for the analysis of these metrics for large-scale complex models. We compare and contrast a wide range of metrics implemented in our observation tool. Our experimental analysis uses the classic model of Game of Life to provide a baseline for analysis, and a more complex Emergency Department model to further explore the suitability of these metrics and the modeling and analysis challenges faced when using them.

 

Identifying Self-Organization and Adaptability in Complex Adaptive Systems

Lachlan Birdsey ; Claudia Szabo ; Katrina Falkner

Published in: Self-Adaptive and Self-Organizing Systems (SASO), 2017 IEEE 11th International Conference on


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Simulation-Optimization of the Mexico City Public Transportation Network: A Complex Network Analysis Framework - Springer

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Complexity: Decoding deep similarities : Nature

Philip Ball applauds Geoffrey West's opus on the laws that lurk in organisms, cities and companies.
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Complex Adaptive Systems Modeling | Full text | Introduction to the modeling and analysis of complex systems: a review

Sayama, H Introduction to the Modeling and Analysis of Complex Systems Open SUNY textbooks, Milne Library, State University of New York at Geneseo (2015). 485 pages, Print ISBN: 1942341083.
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Adaptive Individuals in Evolving Populations: Models and Algorithms (Santa Fe Institute Studies in the Sciences of Complexity, Proceedings Vol 26): Richard K. Belew, Melanie Mitchell: 9780201483697...

Adaptive Individuals in Evolving Populations: Models and Algorithms (Santa Fe Institute Studies in the Sciences of Complexity, Proceedings Vol 26) [Richard K. Belew, Melanie Mitchell] on Amazon.com. *FREE* shipping on qualifying offers. The theory of evolution has been most successful explaining the emergence of new species in terms of their morphological traits. Ethologists teach that behaviors
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Discovering the laws of urbanisation

In 2012 the world's population exceeded 7 billion, and since 2008 the number of individuals living in urban areas has surpassed that of rural areas. This is the result of an overall increase of life expectancy in many countries that has caused an unprecedented growth of the world's total population during recent decades, combined with a net migration flow from rural villages to urban agglomerations. While it is clear that the rate of natural increase and migration flows are the driving forces shaping the spatial distribution of population, a general consensus on the mechanisms that characterise the urbanisation process is still lacking. Here we present two fundamental laws of urbanisation that are quantitatively supported by empirical evidence: 1) the number of cities in a country is proportional to the country's total population, irrespective of the country's area, and 2) the average distance between cities scales as the inverse of the square root of the country's population density. We study the spatio-temporal evolution of population considering two classes of models, Gravity and Intervening Opportunities, to estimate migration flows and show that they produce different spatial patterns of cities.

 

Discovering the laws of urbanisation
Filippo Simini, Charlotte James

http://arxiv.org/abs/1512.03747


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Great reading on Urbanisation
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Information Flows? A Critique of Transfer Entropies

A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it and a derivative quantity, the causation entropy, do not, in fact, quantify the flow of information. At one and the same time they can overestimate flow or underestimate influence. We isolate why this is the case and propose alternate measures for information flow. An auxiliary consequence reveals that the proliferation of networks as a now-common theoretical model for large-scale systems in concert with the use of transfer-like entropies has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems, despite the occurrence of polyadic dependencies. The net result is that much of the sophisticated organization of complex systems goes undetected.

 

Information Flows? A Critique of Transfer Entropies
Ryan G. James, Nix Barnett, James P. Crutchfield

http://arxiv.org/abs/1512.06479


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Interacting Behavior and Emerging Complexity

Can we quantify the change of complexity throughout evolutionary processes? We attempt to address this question through an empirical approach. In very general terms, we simulate two simple organisms on a computer that compete over limited available resources. We implement Global Rules that determine the interaction between two Elementary Cellular Automata on the same grid. Global Rules change the complexity of the state evolution output which suggests that some complexity is intrinsic to the interaction rules themselves. The largest increases in complexity occurred when the interacting elementary rules had very little complexity, suggesting that they are able to accept complexity through interaction only. We also found that some Class 3 or 4 CA rules are more fragile than others to Global Rules, while others are more robust, hence suggesting some intrinsic properties of the rules independent of the Global Rule choice. We provide statistical mappings of Elementary Cellular Automata exposed to Global Rules and different initial conditions onto different complexity classes.

 

Interacting Behavior and Emerging Complexity
Alyssa Adams, Hector Zenil, Eduardo Hermo Reyes, Joost Joosten

http://arxiv.org/abs/1512.07450


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The Critical Few

The Critical Few | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it

To maintain stability yet retain the flexibility to adapt to changing circumstances, social systems must strike a balance between the maintenance of a shared reality and the survival of minority opinion. A computational model is presented that investigates the interplay of two basic, oppositional social processes—conformity and anticonformity—in promoting the emergence of this balance. Computer simulations employing a cellular automata platform tested hypotheses concerning the survival of minority opinion and the maintenance of system stability for different proportions of anticonformity. Results revealed that a relatively small proportion of anticonformists facilitated the survival of a minority opinion held by a larger number of conformists who would otherwise succumb to pressures for social consensus. Beyond a critical threshold, however, increased proportions of anticonformists undermined social stability. Understanding the adaptive benefits of balanced oppositional forces has implications for optimal functioning in psychological and social processes in general.

 

The Critical Few: Anticonformists at the Crossroads of Minority Opinion Survival and Collapse
by Matthew Jarman, Andrzej Nowak, Wojciech Borkowski, David Serfass, Alexander Wong and Robin Vallacher
http://jasss.soc.surrey.ac.uk/18/1/6.html


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Thermodynamics of firms' growth

The distribution of firms' growth and firms' sizes is a topic under intense scrutiny. In this paper, we show that a thermodynamic model based on the maximum entropy principle, with dynamical prior information, can be constructed that adequately describes the dynamics and distribution of firms' growth. Our theoretical framework is tested against a comprehensive database of Spanish firms, which covers, to a very large extent, Spain's economic activity, with a total of 1 155 142 firms evolving along a full decade. We show that the empirical exponent of Pareto's law, a rule often observed in the rank distribution of large-size firms, is explained by the capacity of economic system for creating/destroying firms, and that can be used to measure the health of a capitalist-based economy. Indeed, our model predicts that when the exponent is larger than 1, creation of firms is favoured; when it is smaller than 1, destruction of firms is favoured instead; and when it equals 1 (matching Zipf's law), the system is in a full macroeconomic equilibrium, entailing ‘free’ creation and/or destruction of firms. For medium and smaller firm sizes, the dynamical regime changes, the whole distribution can no longer be fitted to a single simple analytical form and numerical prediction is required. Our model constitutes the basis for a full predictive framework regarding the economic evolution of an ensemble of firms. Such a structure can be potentially used to develop simulations and test hypothetical scenarios, such as economic crisis or the response to specific policy measures.

 

Thermodynamics of firms' growth
Eduardo Zambrano, Alberto Hernando, Aurelio Fernández Bariviera, Ricardo Hernando, Angelo Plastino

Interface

November 2015
Volume: 12 Issue: 112

http://dx.doi.org/10.1098/rsif.2015.0789 


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The Hidden Power Laws of Ecosystems

The Hidden Power Laws of Ecosystems | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it

Here’s how to cause a ruckus: Ask a bunch of naturalists to simplify the world. We usually think in terms of a web of complicated interactions among animals, plants, microbes, earth, wind, and fire—what Darwin called “the entangled bank.” Reducing the bank’s complexity to broad generalizations can seem dishonest.
So when Tony Ives, a theoretical ecologist at the University of Wisconsin, prodded his colleagues at the 2013 meeting of the Ecological Society of America by calling for a vote on whether they ought to seek out general laws, it probably wasn’t surprising that two-thirds of the room voted no.1
Despite the skepticism, the kinds of general laws made possible by simplification have remarkable predictive powers. They could let us calculate how many species there are in ecosystems that are too big to sample thoroughly, or how many will be lost after habitat destruction.


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Gary Bamford's curator insight, November 1, 2015 3:52 PM

The complexity of complexity!

Seung HL Lee's curator insight, November 16, 1:33 PM

The complexity of complexity!

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Measuring the Complexity of Continuous Distributions

We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.

 

Measuring the Complexity of Continuous Distributions
Guillermo Santamaría-Bonfil, Nelson Fernández, Carlos Gershenson

http://arxiv.org/abs/1511.00529


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Weighting dissimilarities to detect communities in networks

Weighting dissimilarities to detect communities in networks | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it

Many complex systems can be described as networks exhibiting inner organization as communities of nodes. The identification of communities is a key factor to understand community-based functionality. We propose a family of measures based on the weighted sum of two dissimilarity quantifiers that facilitates efficient classification of communities by tuning the quantifiers’ relative weight to the network’s particularities. Additionally, two new dissimilarities are introduced and incorporated in our analysis. The effectiveness of our approach is tested by examining the Zachary’s Karate Club Network and the Caenorhabditis elegans reactions network. The analysis reveals the method’s classification power as confirmed by the efficient detection of intrapathway metabolic functions in C. elegans.


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Generating realistic scaled complex networks

Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks including verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.

 

Generating realistic scaled complex networks
Christian L. Staudt, Michael Hamann, Alexander Gutfraind, Ilya Safroand Henning Meyerhenke
Applied Network Science 2017 2:36
https://doi.org/10.1007/s41109-017-0054-z


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Social Synthesis: Finding Dynamic Patterns in Complex Social Systems (Hardback) - Routledge

How is it possible to understand society and the problems it faces? What sense can be made of the behaviour of markets and government interventions? How can citizens understand the course that their lives take and the opportunities available to…
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Complexity theory and financial regulation

Complexity theory and financial regulation | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it

Science Vol 351, Issue 6275 19 February 2016

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[1510.04967] A simple agent-based spatial model of the economy: tools for policy

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Complex networks as an emerging property of hierarchical preferential attachment

Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

 

Complex networks as an emerging property of hierarchical preferential attachment
Laurent Hébert-Dufresne, Edward Laurence, Antoine Allard, Jean-Gabriel Young, and Louis J. Dubé
Phys. Rev. E 92, 062809

http://dx.doi.org/10.1103/PhysRevE.92.062809


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Dynamical criticality: overview and open questions

In this paper we provide a survey of the most relevant work on dynamical criticality, with particular emphasis on the criticality hypothesis, which states that systems in a dynamical regime between order and chaos have evolutionary advantages with respect to ordered and disordered (chaotic) systems. We review the main contributions concerning dynamics and information processing at the edge of chaos, and we illustrate the main achievements in the detection of critical dynamics in biological systems. Finally, we discuss open questions and outlook future work.

 

Dynamical criticality: overview and open questions
Andrea Roli, Marco Villani, Alessandro Filisetti, Roberto Serra

http://arxiv.org/abs/1512.05259


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Even online, you can't have more than 150 friends

Even online, you can't have more than 150 friends | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it
Sci-fi game allows researchers to test the limits of our human relationships

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Free Energy Rate Density and Self-organization in Complex Systems

One of the most important tasks in science is to understand the self-organization's arrow of time. To attempt this we utilize the connection between self-organization and non-equilibrium thermodynamics. Eric Chaisson calculated an exponential increase of Free Energy Rate Density (FERD) in Cosmic Evolution, from the Big Bang until now, paralleling the increase of system's structure. We term these studies "Devology". We connect FERD to the principle of least action for complex systems, driving their increase of action efficiency. We study CPUs as a specific system in which the organization, the total amount of action and FERD are connected in a positive feedback loop, providing exponential growth of all three and power law relations between them. This is a deep connection, reaching to the first principles of physics: the least action principle and the second law of thermodynamics. We propose size-density and complexity-density rules in addition to the established size-complexity one.

 

Free Energy Rate Density and Self-organization in Complex Systems
Georgi Yordanov Georgiev, Erin Gombos, Timothy Bates, Kaitlin Henry, Alexander Casey, Michael Daly

http://arxiv.org/abs/1511.00186


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Accurate market price formation model with both supply-demand and trend-following for global food prices providing policy recommendations

Recent increases in food prices are linked to widespread hunger and social unrest. The causes of high food prices have been debated. Here we rule out explanations that are not consistent with the data and construct a dynamic model of food prices using two factors determined to have the largest impact: corn-to-ethanol conversion and investor speculation. We overcome limitations of equilibrium theories that are unable to quantify the impact of speculation by using a dynamic model of trend following. The model accurately fits the data. Ethanol conversion results in a smooth price increase, whereas speculation results in bubbles and crashes. These findings significantly inform the discussion about food prices and market equilibrium and have immediate policy implications.

 

Accurate market price formation model with both supply-demand and trend-following for global food prices providing policy recommendations

Marco Lagi, Yavni Bar-Yam, Karla Z. Bertrand, and Yaneer Bar-Yam

PNAS

http://dx.doi.org/10.1073/pnas.1413108112 ;


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Lifelong memories may reside in nets around brain cells

Lifelong memories may reside in nets around brain cells | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it

In 1898, Italian biologist Camillo Golgi saw something odd in the slices of brain tissue he examined under his micro scope: weblike lattices surrounding many neurons. Golgi could not discern their purpose, and many dismissed the nets as an artifact of his staining technique. For the next century, the lattices remained largely obscure. But last week at the annual meeting of the Society for Neuroscience here, researchers offered tantalizing new evidence that holes in these nets could be the storage sites for long-term memories.

Perineuronal nets (PNNs), as they are known today, are scaffolds of linked proteins and sugars that resemble cartilage. A growing body of research suggests that PNNs may control the formation and function of synapses, the microscopic junctions between neurons that allow cells to communicate and that may play a role in learning and memory, says neuroscientist Sakina Palida (...)

 

Lifelong memories may reside in nets around brain cells
Emily Underwood

Science 30 October 2015:
Vol. 350 no. 6260 pp. 491-492
http://dx.doi.org/10.1126/science.350.6260.491


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Complexity Digest's curator insight, October 30, 2015 1:14 PM

It is commonly considered that neuronal patterns store memories. This research suggests that memories are fixed at the molecular scale, increasing by orders of magnitude the estimated information storage of nervous systems.

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Society: Build digital democracy

Society: Build digital democracy | Complejidad - Sistemas Complejos (libros, papers) | Scoop.it

To ensure that no government, company or person with sole control of digital filters can manipulate our decisions, we need information systems that are transparent, trustworthy and user-controlled. Each of us must be able to choose, modify and build our own tools for winnowing information.

 

Society: Build digital democracy
Dirk Helbing & Evangelos Pournaras

http://www.nature.com/news/society-build-digital-democracy-1.18690 ;

 

See Also: 

Building a Smart Digital Society Together

https://youtu.be/4slnjohWeZk ;


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Cooperation, competition and the emergence of criticality in communities of adaptive systems

The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between exceedingly ordered and too noisy states. We here present a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We study --both analytically and computationally-- the circumstances under which criticality is the best possible outcome of the dynamical process, confirming the convergence to critical points under very generic conditions. Finally, we analyze the effect of cooperation (agents try to enhance not only their fitness, but also that of other individuals) and competition (agents try to improve their own fitness and to diminish those of competitors) within our setting. The conclusion is that, while competition fosters criticality, cooperation hinders it and can lead to more ordered or more disordered consensual solutions.

 

Cooperation, competition and the emergence of criticality in communities of adaptive systems
Jorge Hidalgo, Jacopo Grilli, Samir Suweis, Amos Maritan, Miguel A. Munoz

http://arxiv.org/abs/1510.05941


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