Complexity - Comp...
7.3K views | +1 today

 Rescooped by Bernard Ryefield from Papers onto Complexity - Complex Systems Theory

# How Can the Study of Complexity Transform Our Understanding of the World?

The “study of complexity” refers to the attempt to find common principles underlying the behavior of complex systems—systems in which large collections of components interact in nonlinear ways. Here, the term nonlinear implies that the system can’t be understood simply by understanding its individual components; nonlinear interactions cause the whole to be “more than the sum of its parts.”

How Can the Study of Complexity Transform Our Understanding of the World?

Melanie Mitchell

https://www.bigquestionsonline.com/content/how-can-study-complexity-transform-our-understanding-world

Via Complexity Digest
António F Fonseca's curator insight,

Wonderful and clarifying text.

Lorien Pratt's curator insight,

One of my favorite complexity authors.  An excerpt: "In the past it was widely assumed that such phenomena are hard to predict because the underlying processes are highly complex, and that random factors must play a key role.  However, Complex Systems science—especially the study of dynamics and chaos—have shown that complex behavior and unpredictability can arise in a system even if the underlying rules are extremely simple and completely deterministic.  Often, the key to complexity is the iteration over time of simple, though nonlinear, interaction rules among the system’s components."

This insight is at the core of Decision Intelligence, which adds an understanding of these emergent behaviors to the usual big data/predictive analytics/optimization stack.

# 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)
 Scooped by Bernard Ryefield

## Defining chaos

In this paper, we propose, discuss, and illustrate a computationally feasible definition of chaos which can be applied very generally to situations that are commonly encountered, including attractors, repellers, and non-periodically forced systems. This definition is based on an entropy-like quantity, which we call “expansion entropy,” and we define chaos as occurring when this quantity is positive. We relate and compare expansion entropy to the well-known concept of topological entropy to which it is equivalent under appropriate conditions. We also present example illustrations, discuss computational implementations, and point out issues arising from attempts at giving definitions of chaos that are not entropy-based.
No comment yet.
 Scooped by Bernard Ryefield

## Sociology and Complexity Science blog: New 2015 Version of Map of the Complexity Sciences

No comment yet.
 Scooped by Bernard Ryefield

## Principles of deception-perception | CSL4D

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…
No comment yet.
 Scooped by Bernard Ryefield

## Modelling Tools for Dealing with Environmental Complexity

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.
No comment yet.
 Scooped by Bernard Ryefield

## Evolution, You’re Drunk - Issue 9: Time - Nautilus

Amoebas are puny, stupid blobs, so scientists were surprised to learn that they contain 200 times more DNA than Einstein did. Because…
No comment yet.
 Scooped by Bernard Ryefield

This article explores the concept of the Complex Adaptive Systems and see how this model might apply in various walks of life.
David Poveda's curator insight,

Supply chains are Complex Adaptive Systems. In order to manage them well, this basic fact must be recognized. And they are certainly not well managed with forecasts and ERP´s.

 Rescooped by Bernard Ryefield from Libros y Papers sobre Complejidad - Sistemas Complejos

## Introduction to the Modeling and Analysis of Complex Systems | Open SUNY Textbooks

Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.

No comment yet.
 Rescooped by Bernard Ryefield from Systems Thinking

## Why Systems Thinking is Not a Natural Act

Competence in systems thinking is implicitly assumed among the population of engineers and managers — in fact, most technical people claim to be systems ...

Via Ides De Vos, Jürgen Kanz
No comment yet.
 Rescooped by Bernard Ryefield from CASR3PM

## FOCUS: Complexity and the failure of quantitative social science

Photo: Walter Baxter - licensed for reuse CC BY-SA 2.0 Brian Castellani (Kent State University)   When I attended university in 1984 as a psychology undergraduate in the States, the pathway to...

Via Christophe Bredillet
Christophe Bredillet's curator insight,

I love this map (this one and previous versions) and use(d) it quite often

 Scooped by Bernard Ryefield

## COMPLEXITY, PROBLEM SOLVING, AND SUSTAINABLE SOCIETIES, by Joseph A. Tainter, 1996

Historical knowledge is essential to practical applications of ecological economics. Systems of problem solving develop greater complexity and higher costs over long periods. In time such systems either require increasing energy subsidies or they collapse. Diminishing returns to complexity in problem solving limited the abilities of earlier societies to respond sustainably to challenges, and will shape contemporary responses to global change. To confront this dilemma we must understand both the role of energy in sustaining problem solving, and our historical position in systems of increasing complexity.

No comment yet.
 Scooped by Bernard Ryefield

## Creating an Artificial World with a New Kind of Cellular Automata

This paper describes a new concept of cellular automata (CA). XCA consists of a set of arcs (edges). These arcs correspond to cells in CA. At a definite time, the arcs are connected to a directed graph. With each next time step, the arcs are exchanging their neighbors (adjacent arcs) according to rules that are dependent on the status of the adjacent arcs. With the extended cellular automaton (XCA) an artificial world may be simulated starting with a Big Bang. XCA does not require a grid like CA do. However, it can create one, just as the real universe after the big bang generated its own space, which previously did not exist. Examples with different rules show how manifold the concept of XCA is. Like the game of life simulates birth, survival, and death, this game should simulate a system that starts from a singularity, and evolves to a complex space.

No comment yet.
 Scooped by Bernard Ryefield

## Decoding the Remarkable Algorithms of Ants | Quanta Magazine

The biologist Deborah Gordon has uncovered how ant colonies search efficiently without central organization, an insight that might improve computer networks.
No comment yet.
 Rescooped by Bernard Ryefield from Papers

## Lévy walks

Random walk is a fundamental concept with applications ranging from quantum physics to econometrics. Remarkably, one specific model of random walks appears to be ubiquitous across many fields as a tool to analyze transport phenomena in which the dispersal process is faster than dictated by Brownian diffusion. The Lévy-walk model combines two key features, the ability to generate anomalously fast diffusion and a finite velocity of a random walker. Recent results in optics, Hamiltonian chaos, cold atom dynamics, biophysics, and behavioral science demonstrate that this particular type of random walk provides significant insight into complex transport phenomena. This review gives a self-consistent introduction to Lévy walks, surveys their existing applications, including latest advances, and outlines further perspectives.

Lévy walks
V. Zaburdaev, S. Denisov, and J. Klafter
Rev. Mod. Phys. 87, 483

http://dx.doi.org/10.1103/RevModPhys.87.483

Via Complexity Digest
No comment yet.
 Rescooped by Bernard Ryefield from Papers

## 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

Via Complexity Digest
No comment yet.
 Scooped by Bernard Ryefield

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

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.

No comment yet.
 Rescooped by Bernard Ryefield from Papers

## 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 &frac34;. 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 ;

Via Complexity Digest
 Scooped by Bernard Ryefield

## A Theory of Cheap Control in Embodied Systems

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.
No comment yet.
 Rescooped by Bernard Ryefield from Talks

## Civilization Far From Equilibrium - Energy, Complexity, and Human Survival

Human societies use complexity -- within their institutions and technologies -- to address their various problems, and they need high-quality energy to create and sustain this complexity. But now greater complexity is producing diminishing returns in wellbeing, while the energetic cost of key sources of energy is rising fast. Simultaneously, humankind's problems are becoming vastly harder, which requires societies to deliver yet more complexity and thus consume yet more energy. Resolving this paradox is the central challenge of the 21st century. Thomas Homer-Dixon holds the CIGI Chair of Global Systems at the Balsillie School of International Affairs in Waterloo, Canada, and is a Professor at the University of Waterloo.

Via Complexity Digest
No comment yet.
 Scooped by Bernard Ryefield

## Making Decisions in a Complex Adaptive System

No comment yet.
 Rescooped by Bernard Ryefield from Systems Thinking

## Using Chaos Theory to Predict and Prevent Catastrophic 'Dragon King' Events

Stop a stock trade and avoid a catastrophic global financial crash. Seal a microscopic crack and prevent a rocket explosion. Push a button to avert a citywide blackout.

Though such situations are mostly fantasies, a new analysis suggests that certain types of extreme events occurring in complex systems – known as dragon king events – can be predicted and prevented.

Via Claudia Mihai, Complexity Digest, Bill Aukett, Jürgen Kanz
Ali Anani's curator insight,

Can we control  the uncontrollable?

 Scooped by Bernard Ryefield

## 'Leaders and lifters' help ants move massive meals - BBC News

In a new study, physicists reveal how ants co-operate to carry huge chunks of food back to their nests.
No comment yet.
 Scooped by Bernard Ryefield

## complexity map by Brian Castellani - map of complexity science

Feb 09 2015

No comment yet.
 Scooped by Bernard Ryefield

## Human and nature dynamics (HANDY): Modeling inequality and use of resources in the collapse or sustainability of societies

Highlights

HANDY is a 4-variable thought-experiment model for interaction of humans and nature.

The focus is on predicting long-term behavior rather than short-term forecasting.

Carrying Capacity is developed as a practical measure for forecasting collapses.

A sustainable steady state is shown to be possible in different types of societies.

But over-exploitation of either Labor or Nature results in a societal collapse.

No comment yet.
 Scooped by Bernard Ryefield

## Dealing with Multi-Level Governance and Wicked Problems in Urban Transportation Systems: The Case of Palermo Municipality

Italian New Public Management (NPM) has been mainly characterized by a political orientation toward power decentralization to local governments and privatization of public companies. Nowadays, local utilities in Italy are often run by joint stock companies controlled by public agencies such as Regional and Municipal Administrations. Due to this transformation, these companies must comply with a set of diverse expectations coming from a wide range of stakeholders, related to their financial, competitive and social performance. Such fragmented governance increases the presence of “wicked” problems in the decision-making sphere of these entities. Given this multi-level governance structure, how do these agents influence public services performance? In recent years, coordination and inter-institutional joint action have been identified as possible approaches for dealing with governance fragmentation and wicked problems deriving from it. How can we adapt a performance management perspective in order to help us reform the system and so have a better collaboration between the stakeholders involved? In order to address and discuss these research questions, a case study will be developed. The case concerns AMAT, the local utility providing the public transportation service in the Municipality of Palermo (Italy). The result of this study is a dynamic model including a set of performance indicators that help us in understanding the impact of the governing structure on the system’s performance.
No comment yet.
 Scooped by Bernard Ryefield

## Exploiting chaos for applications

We discuss how understanding the nature of chaotic dynamics allows us to control these systems. A controlled chaotic system can then serve as a versatile pattern generator that can be used for a range of application. Specifically, we will discuss the application of controlled chaos to the design of novel computational paradigms. Thus, we present an illustrative research arc, starting with ideas of control, based on the general understanding of chaos, moving over to applications that influence the course of building better devices.
No comment yet.