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The complex dynamics of wishful thinking: The critical positivity ratio

The complex dynamics of wishful thinking: The critical positivity ratio | Complexity - Complex Systems Theory | Scoop.it

We examine critically the claims made by Fredrickson and Losada (2005) concerning the construct known as the "positivity ratio". We find no theoretical or empirical justification for the use of differential equations drawn from fluid dynamics, a subfield of physics, to describe changes in human emotions over time; furthermore, we demonstrate that the purported application of these equations contains numerous fundamental conceptual and mathematical errors. The lack of relevance of these equations and their incorrect application lead us to conclude that Fredrickson and Losada's claim to have demonstrated the existence of a critical minimum positivity ratio of 2.9013 is entirely unfounded. More generally, we urge future researchers to exercise caution in the use of advanced mathematical tools such as nonlinear dynamics and in particular to verify that the elementary conditions for their valid application have been met.

Bernard Ryefield's insight:

On the (mis)use of nonlinear dynamics tools in psychology.

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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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The Hidden Power Laws of Ecosystems - Issue 29: Scaling - Nautilus

The Hidden Power Laws of Ecosystems - Issue 29: Scaling - Nautilus | Complexity - Complex Systems Theory | 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…
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Gary Bamford's curator insight, November 1, 2015 3:52 PM

The complexity of complexity!

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Gérard Ben Arous - "Paul Lévy et les cygnes noirs"

BnF - 30 avril 2014 Conférence donnée dans le cadre du cycle "Un texte, un mathématicien", organisée par la Société…
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The Many Careers of Jay Forrester - MIT Technology Review

The Many Careers of Jay Forrester - MIT Technology Review | Complexity - Complex Systems Theory | Scoop.it
Computing pioneer Jay Forrester, SM ’45, developed magnetic-core memory. Then he founded the field of system dynamics. Those are just two of his varied pursuits.

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The physics of life

The physics of life | Complexity - Complex Systems Theory | Scoop.it
From flocking birds to swarming molecules, physicists are seeking to understand 'active matter' — and looking for a fundamental theory of the living world.

 

http://www.nature.com/news/the-physics-of-life-1.19105


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Marcelo Errera's curator insight, January 13, 1:09 PM

Organization emerges naturally. One more manifestation of the constructal law.

 

By the way, soon to appear:

The Physics of Life: The Evolution of Everything 
by Adrian Bejan 
Link: http://amzn.com/1250078822

Francisco Restivo's curator insight, January 14, 6:29 PM

Living world is the real laboratory.

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Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks | Complexity - Complex Systems Theory | Scoop.it

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.

 (Illustrative videos can be downloaded from here:this http URL)


Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks
Peter Csermely

http://arxiv.org/abs/1511.01238 ;


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Complexity Digest's curator insight, November 18, 2015 6:13 PM

See Also: http://networkdecisions.linkgroup.hu 

António F Fonseca's curator insight, November 23, 2015 3:30 AM

Interesting  paper about fast cores and slow periphery,  conflict in the elite vs democratic consensus.

Marcelo Errera's curator insight, November 24, 2015 11:32 AM

Yes, there must be few fasts and many slows.  It's been predicted by CL in many instances.

 

http://www.researchgate.net/publication/273527384_Constructal_Law_Optimization_as_Design_Evolution

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Nature’s Critical Warning System | Quanta Magazine

Nature’s Critical Warning System |  Quanta Magazine | Complexity - Complex Systems Theory | Scoop.it

Scientists are homing in on a warning signal that arises in complex systems like ecological food webs, the brain and the Earth’s climate. Could it help prevent future catastrophes?

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Sequential memory: Binding dynamics

Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L − 1, where L is the number of modalities.
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[1510.04803] Hidden attractors in fundamental problems and engineering models

[1510.04803] Hidden attractors in fundamental problems and engineering models | Complexity - Complex Systems Theory | Scoop.it
Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered.
 
 
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Urban Scaling in Europe

Over the last decades, in disciplines as diverse as economics, geography, and complex systems, a perspective has arisen proposing that many properties of cities are quantitatively predictable due to agglomeration or scaling effects. Using new harmonized definitions for functional urban areas, we examine to what extent these ideas apply to European cities. We show that while most large urban systems in Western Europe (France, Germany, Italy, Spain, UK) approximately agree with theoretical expectations, the small number of cities in each nation and their natural variability preclude drawing strong conclusions. We demonstrate how this problem can be overcome so that cities from different urban systems can be pooled together to construct larger datasets. This leads to a simple statistical procedure to identify urban scaling relations, which then clearly emerge as a property of European cities. We compare the predictions of urban scaling to Zipf's law for the size distribution of cities and show that while the former holds well the latter is a poor descriptor of European cities. We conclude with scenarios for the size and properties of future pan-European megacities and their implications for the economic productivity, technological sophistication and regional inequalities of an integrated European urban system.

 

Urban Scaling in Europe
Luis M. A. Bettencourt, Jose Lobo

http://arxiv.org/abs/1510.00902


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Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University - YouTube

Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University - YouTube | Complexity - Complex Systems Theory | Scoop.it

This course of 25 lectures, filmed at Cornell University in Spring 2014, is intended for newcomers to nonlinear dynamics and chaos. It closely follows Prof. Strogatz's book, "Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering." 

The mathematical treatment is friendly and informal, but still careful. Analytical methods, concrete examples, and geometric intuition are stressed. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors. 

A unique feature of the course is its emphasis on applications. These include airplane wing vibrations, biological rhythms, insect outbreaks, chemical oscillators, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with the mathematical theory. The theoretical work is enlivened by frequent use of computer graphics, simulations, and videotaped demonstrations of nonlinear phenomena.

The essential prerequisite is single-variable calculus, including curve sketching, Taylor series, and separable differential equations. In a few places, multivariable calculus (partial derivatives, Jacobian matrix, divergence theorem) and linear algebra (eigenvalues and eigenvectors) are used. Fourier analysis is not assumed, and is developed where needed. Introductory physics is used throughout. Other scientific prerequisites would depend on the applications considered, but in all cases, a first course should be adequate preparation.

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The predator-prey power law: Biomass scaling across terrestrial and aquatic biomes

Ecosystems exhibit surprising regularities in structure and function across terrestrial and aquatic biomes worldwide. We assembled a global data set for 2260 communities of large mammals, invertebrates, plants, and plankton. We find that predator and prey biomass follow a general scaling law with exponents consistently near ¾. This pervasive pattern implies that the structure of the biomass pyramid becomes increasingly bottom-heavy at higher biomass. Similar exponents are obtained for community production-biomass relations, suggesting conserved links between ecosystem structure and function. These exponents are similar to many body mass allometries, and yet ecosystem scaling emerges independently from individual-level scaling, which is not fully understood. These patterns suggest a greater degree of ecosystem-level organization than previously recognized and a more predictive approach to ecological theory.

 

The predator-prey power law: Biomass scaling across terrestrial and aquatic biomes
Ian A. Hatton, Kevin S. McCann, John M. Fryxell, T. Jonathan Davies, Matteo Smerlak, Anthony R. E. Sinclair, Michel Loreau

Science 4 September 2015:
Vol. 349 no. 6252
http://dx.doi.org/10.1126/science.aac6284 ;


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A Theory of Cheap Control in Embodied Systems

A Theory of Cheap Control in Embodied Systems | Complexity - Complex Systems Theory | Scoop.it
Author Summary Given a body and an environment, what is the brain complexity needed in order to generate a desired set of behaviors? The general understanding is that the physical properties of the body and the environment correlate with the required brain complexity. More precisely, it has been pointed that naturally evolved intelligent systems tend to exploit their embodiment constraints and that this allows them to express complex behaviors with relatively concise brains. Although this principle of parsimonious control has been formulated quite some time ago, only recently one has begun to develop the formalism that is required for making quantitative statements on the sufficient brain complexity given embodiment constraints. In this work we propose a precise mathematical approach that links the physical and behavioral constraints of an agent to the required controller complexity. As controller architecture we choose a well-known artificial neural network, the conditional restricted Boltzmann machine, and define its complexity as the number of hidden units. We conduct experiments with a virtual six-legged walking creature, which provide evidence for the accuracy of the theoretical predictions.
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Jean-Michel Coron - "La régulation des systèmes complexes depuis Maxwell"

BnF - 11 mai 2011 Conférence donnée dans le cadre du cycle "Un texte, un mathématicien", organisée par la Société…
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[1602.02540] Variations on chaos in physics: from unpredictability to universal laws

[1602.02540] Variations on chaos in physics: from unpredictability to universal laws | Complexity - Complex Systems Theory | Scoop.it

The tremendous popular success of Chaos Theory shares some common points with the not less fortunate Relativity: they both rely on a misunderstanding. Indeed, ironically , the scientific meaning of these terms for mathematicians and physicists is quite opposite to the one most people have in mind and are attracted by. One may suspect that part of the psychological roots of this seductive appeal relies in the fact that with these ambiguous names, together with some superficial clichés or slogans immediately related to them ("the butterfly effect" or "everything is relative"), some have the more or less secret hope to find matter that would undermine two pillars of science, namely its ability to predict and to bring out a universal objectivity. Here I propose to focus on Chaos Theory and illustrate on several examples how, very much like Relativity, it strengthens the position it seems to contend with at first sight: the failure of predictability can be overcome and leads to precise, stable and even more universal predictions.

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[1601.06176] What is Information?

[1601.06176] What is Information? | Complexity - Complex Systems Theory | Scoop.it

Information is a precise concept that can be defined mathematically, but its relationship to what we call "knowledge" is not always made clear. Furthermore, the concepts "entropy" and "information", while deeply related, are distinct and must be used with care, something that is not always achieved in the literature. In this elementary introduction, the concepts of entropy and information are laid out one by one, explained intuitively, but defined rigorously. I argue that a proper understanding of information in terms of prediction is key to a number of disciplines beyond engineering, such as physics and biology.

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SIR-Network Model and Its Application to Dengue Fever

SIR-Network Model and Its Application to Dengue Fever | Complexity - Complex Systems Theory | Scoop.it

The SIR-network model, introduced in [S. Boatto et al., SIR-Network Model for Epidemics Dynamics in a City, in preparation] and [L. Stolerman, Spreading of an Epidemic over a City: A Model on Networks, Master's thesis, 2012 (in Portuguese)], deals with the propagation of disease epidemics in highly populated cities. The nodes, or vertices, are the city's neighborhoods, in which the local populations are assumed to be well-mixed. The directed edges represent the fractions of people moving from their neighborhoods of residence to those of daily activities. First, we present some fundamental properties of the basic reproduction number ($R_o$) for this model. In particular, we focus on how $R_o$ depends upon the geometry and the heterogeneity (different infection rates in each vertex) of the network. This allows us to conclude whether an epidemic outbreak can be expected or not. Second, we submit the SIR-network model to data fitting, using data collected during the 2008 Rio de Janeiro dengue fever epidemic. Important conclusions are drawn from the fitted parameters, and we show that improved results are found when a time-dependent infection parameter is introduced.


Read More: http://epubs.siam.org/doi/10.1137/140996148

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Complex Adaptive Systems Modeling | Full text | In Memoriam: John Henry Holland—a pioneer of complex adaptive systems research (February 2, 1929–August 9, 2015)

“The study of cas is a difficult, exciting task. The returns are likely to be proportionate to the difficulty.” Holland ([2006])

On August 9, 2015, cancer took Prof. John Henry Holland away from us. Prof. Holland was a pioneer of Complex Adaptive Systems (CAS) research and a true inspiration. He is known not only for his work on CAS, Holland ([1962], [1992])—which he would fondly write as “cas”—but also for his seminal work on adaptation in natural and artificial systems leading to the creation of genetic algorithms and eventually the fields of evolutionary computation, Holland ([1995]) and Learning Classifier Systems, Holland and Holyoak ([1989]).

Holland was a truly interdisciplinary academic. He had an undergraduate degree in Physics from MIT (1950), an M.A. in Mathematics (1954) and possibly the first ever PhD in Computer Science (1959), both from the University of Michigan—a place where he also subsequently served as a Professor of Psychology, Electrical Engineering and Computer Science.

Holland leaves behind his legacy in the form of a large number of thought-provoking articles, video lectures, books, and inspired people—ranging from colleagues, fellows and students to budding complexity enthusiasts. Two of his recent books summarize his views on CAS in both a longer, Holland ([2012]) as well as a shorter form, Holland ([2014]). It is easy to foresee that these works will serve not only as a guide to CAS but also guidance for future generations. Holland will indeed be greatly missed.

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Unpredictability and Complexity

Unpredictability and Complexity | Complexity - Complex Systems Theory | Scoop.it

Chaos Theory Applied to the Literary Arts


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New guide to systems thinking | From Poverty to Power

New guide to systems thinking | From Poverty to Power | Complexity - Complex Systems Theory | Scoop.it
Systems thinking is fascinating, but often baffling and it's hard to apply it in a practically useful way. A new Oxfam guide tries to help

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Modeling Environmental Complexity | Earth, Atmospheric, and Planetary Sciences | MIT OpenCourseWare

Modeling Environmental Complexity | Earth, Atmospheric, and Planetary Sciences | MIT OpenCourseWare | Complexity - Complex Systems Theory | Scoop.it
This course provides an introduction to the study of environmental phenomena that exhibit both organized structure and wide variability—i.e., complexity. Through focused study of a variety of physical, biological, and chemical problems in conjunction with theoretical models, we learn a series of lessons with wide applicability to understanding the structure and organization of the natural world. Students also learn how to construct minimal mathematical, physical, and computational models that provide informative answers to precise questions. This course is appropriate for advanced undergraduates. Beginning graduate students are encouraged to register for 12.586 (graduate version of 12.086). Students taking the graduate version complete different assignments.
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Chaos Theory in Ecology Predicts Future Populations | Quanta Magazine

Chaos Theory in Ecology Predicts Future Populations | Quanta Magazine | Complexity - Complex Systems Theory | Scoop.it
Complex natural systems defy analysis using a standard mathematical toolkit, so one ecologist is throwing out the equations.
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Sociology and Complexity Science blog: New 2015 Version of Map of the Complexity Sciences

Sociology and Complexity Science blog: New 2015 Version of Map of the Complexity Sciences | Complexity - Complex Systems Theory | Scoop.it
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Principles of deception-perception | CSL4D

Principles of deception-perception | CSL4D | Complexity - Complex Systems Theory | Scoop.it
An introductory conclusion The Systems Approach: principles        A few years ago I wrote a blog post about anti-planning as an alternative to the systems approach. Part of the post was devoted to a number of principles of deception-perception. Churchman discusses their importance in the concluding chapter of The Systems Approach (TSA), which…
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Modelling Tools for Dealing with Environmental Complexity

Modelling Tools for Dealing with Environmental Complexity | Complexity - Complex Systems Theory | Scoop.it
The nexus concept aims at extending ‘integrated management thinking’, which has been applied with varying success in diverse disciplines and has become especially popular in water resources management. UNU-FLORES developed an interactive platform, the Nexus Tools Platform, for inter-model comparison of existing modeling tools related to Water-Soil-Waste Nexus providing detailed model information and advanced filtering based on real-time visualizations.
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