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Complex systems

Complex systems
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.[1] Such systems are used to model processes in computer science, biology,[2] economics, physics, chemistry,[3] and many other fields. The key problems of complex systems are difficulties with their formal modelling and simulation. For systems that are less usefully represented with equations various other kinds of narratives and methods for identifying, exploring, designing and interacting with complex systems are used. Overview[edit] History[edit] A history of complexity science Typical areas of study[edit] Complexity management[edit] Complexity economics[edit] Complexity and modeling[edit] Complexity and chaos theory[edit] 1. Institutes and research centers[edit]

Systemtheorie Die Systemtheorie ist sowohl eine allgemeine und eigenständige Disziplin als auch ein weitverzweigter und heterogener Rahmen für einen interdisziplinären Diskurs, der den Begriff System als Grundkonzept führt. Es gibt folglich sowohl eine allgemeine „Systemtheorie“ als auch eine Vielzahl unterschiedlicher, zum Teil widersprüchlicher und konkurrierender Systemdefinitionen und -begriffe. Es hat sich heute jedoch eine relativ stabile Reihe an Begriffen und Theoremen herausgebildet, auf die sich der systemtheoretische Diskurs bezieht. Geschichte[Bearbeiten] Der Begriff Allgemeine Systemtheorie geht auf den Biologen Ludwig von Bertalanffy zurück. Kulturgeschichtlich geht der Systembegriff bis auf Johann Heinrich Lambert zurück und wurde unter anderem von Johann Gottfried Herder übernommen und ausgearbeitet. Die moderne Systemtheorie beruht auf unabhängig voneinander entwickelten Ansätzen, die später synthetisiert und erweitert wurden: Der Begriff Systemtheorie bzw. Kybernetik[Bearbeiten]

Self-organization Self-organization occurs in a variety of physical, chemical, biological, robotic, social and cognitive systems. Common examples include crystallization, the emergence of convection patterns in a liquid heated from below, chemical oscillators, swarming in groups of animals, and the way neural networks learn to recognize complex patterns. Overview[edit] The most robust and unambiguous examples[1] of self-organizing systems are from the physics of non-equilibrium processes. Self-organization is also relevant in chemistry, where it has often been taken as being synonymous with self-assembly. Self-organization usually relies on three basic ingredients:[3] Strong dynamical non-linearity, often though not necessarily involving positive and negative feedbackBalance of exploitation and explorationMultiple interactions Principles of self-organization[edit] History of the idea[edit] Sadi Carnot and Rudolf Clausius discovered the Second Law of Thermodynamics in the 19th century. Developing views[edit]

Noam Chomsky Avram Noam Chomsky (/ˈnoʊm ˈtʃɒmski/; born December 7, 1928) is an American linguist, philosopher,[21][22] cognitive scientist, logician,[23][24][25] political commentator and anarcho-syndicalist activist. Sometimes described as the "father of modern linguistics",[26][27] Chomsky is also a major figure in analytic philosophy.[21] He has spent most of his career at the Massachusetts Institute of Technology (MIT), where he is currently Professor Emeritus, and has authored over 100 books. He has been described as a prominent cultural figure, and was voted the "world's top public intellectual" in a 2005 poll.[28] Born to a middle-class Ashkenazi Jewish family in Philadelphia, Chomsky developed an early interest in anarchism from relatives in New York City. Chomsky has been a highly influential academic figure throughout his career, and was cited within the field of Arts and Humanities more often than any other living scholar between 1980 and 1992. Early life Childhood: 1928–45

Complex adaptive system They are complex in that they are dynamic networks of interactions, and their relationships are not aggregations of the individual static entities. They are adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events.[1][2] Overview[edit] The term complex adaptive systems, or complexity science, is often used to describe the loosely organized academic field that has grown up around the study of such systems. The fields of CAS and artificial life are closely related. The study of CAS focuses on complex, emergent and macroscopic properties of the system.[3][11][12] John H. General properties[edit] What distinguishes a CAS from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. Characteristics[edit] Some of the most important characteristics of complex systems are:[14] Robert Axelrod & Michael D.

Complex Adaptive Systems: 9 Cellular Automaton + Related Videos - Mashpedia In this video we are going to discuss cellular automata, we will firstly talk about what they are before looking at a classical example, we will then discuss individually the different classes of patterns that cellular automata can generate before wrapping-up with a talk about their significance as a new approach to mathematical modeling. For full courses, transcriptions & downloads please see: Twitter: Facebook: Transcription Excerpt: Cellular automata are algorithmic models that use computation to iterate on very simple rules, in so doing these very simple rules can create complex emergent phenomena through the interaction between agents as they evolve over time. To illustrate the functioning of a cellular automaton we will take an example from probably the most famous algorithm called the Game Of Life devised by the mathematician John Conway.

Donald Cox, The Economics of "Believe-It-Or-Not" "To believe or not to believe? Economics provides a simple, almost trivial sounding, answer: believe something when the benefits of believing outweigh the costs, otherwise don't." Have you heard this one? A senior at Harvard had just written perfect answers to the first two questions of a three-essay history exam but found himself completely stumped by the third. Hmmm... Why do these stories always feature a student from Harvard, and never Brandeis, Northeastern, or University of Illinois? Stories like this are fodder for folklorist Jan Harold Brunvand, who makes a living debunking these "urban legends." Whether we believe that particular story is probably no big deal. To believe or not to believe? Good. right now, you and I each have something on our "top-ten" list of beliefs that's not true it's probably a comparatively "low stakes" belief; that is, the price we pay for believing it is relatively low we might change our minds as soon as it became worth it. That was back in 1984.

Cellular automaton The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. The primary classifications of cellular automata as outlined by Wolfram are numbered one to four. Overview[edit] The red cells are the von Neumann neighborhood for the blue cell, while the extended neighborhood includes the pink cells as well. A torus, a toroidal shape History[edit]

Audio time-scale/pitch modification - Wikipedia These processes are used, for instance, to match the pitches and tempos of two pre-recorded clips for mixing when the clips cannot be reperformed or resampled. (A drum track containing no pitched instruments could be moderately resampled for tempo without adverse effects, but a pitched track could not). They are also used to create effects such as increasing the range of an instrument (like pitch shifting a guitar down an octave). Resampling[edit] Frame-based approach[edit] Frame-based approach of many TSM procedures In order to preserve an audio signal's pitch when stretching or compressing its duration, many TSM procedures follow a frame-based approach.[1] Given an original discrete-time audio signal, this strategy's first step is to split the signal into short analysis frames of fixed length. . . . The strategy of how to derive the synthesis frames from the analysis frames is a key difference among different TSM procedures. Frequency domain[edit] Phase vocoder[edit] Basic steps: SOLA[edit]

Industrial Revolution Iron and Coal, 1855–60, by William Bell Scott illustrates the central place of coal and iron working in the industrial revolution and the heavy engineering projects they made possible. The Industrial Revolution was the transition to new manufacturing processes in the period from about 1760 to sometime between 1820 and 1840. This transition included going from hand production methods to machines, new chemical manufacturing and iron production processes, improved efficiency of water power, the increasing use of steam power, and the development of machine tools. It also included the change from wood and other bio-fuels to coal. Textiles were the dominant industry of the Industrial Revolution in terms of employment, value of output and capital invested; the textile industry was also the first to use modern production methods.[1] The Industrial Revolution marks a major turning point in history; almost every aspect of daily life was influenced in some way. Etymology Textile manufacture Chemicals

Encyclopedia of Complexity and Systems Science Assembles for the first time the concepts and tools for analyzing complex systems in a wide range of fields Reflects the real world by integrating complexity with the deterministic equations and concepts that define matter, energy, and the four forces identified in nature Benefits a broad audience: undergraduates, researchers and practitioners in mathematics and many related fields Encyclopedia of Complexity and Systems Science provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. The science and tools of complexity and systems science include theories of self-organization, complex systems, synergetics, dynamical systems, turbulence, catastrophes, instabilities, nonlinearity, stochastic processes, chaos, neural networks, cellular automata, adaptive systems, and genetic algorithms. Content Level » Research Show all authors

article This article was first published in Software Developer's Journal 4/2006 and SDJ Extra 4/2006 Magazines by Software Wydawnictwo. Article reprinted online by original author in courtesy of Software Developer's Journal. Available also in German as Audio-Zeit-und-Pitch-Skalierung. by Olli Parviainen Introduction Anyone who's used a nowadays obsolete tape recorder or a vinyl disc player is likely familiar with effects of playing the recording back at different speed than it was originally recorded: Playing the recording at double speed reduces the playtime to half, at the same time causing a side-effect of the sound pitch jumping up by an octave which amusingly converts human voices to sound like cartoon figures. In old days of analog audio recording technology this modified playback speed/time/pitch effect was easy to produce by applying incorrect playback speed setting. Applications Finally, people with questionable plots may wish to alter pitch of their voice to conceal their true identity.

Adam Smith 18th-century Scottish moral philosopher and political economist Adam Smith FRSA (16 June [O.S. 5 June] 1723[4] – 17 July 1790) was a Scottish economist, philosopher and author as well as a moral philosopher, a pioneer of political economy and a key figure during the Scottish Enlightenment,[5] also known as ''The Father of Economics''[6] or ''The Father of Capitalism''.[7] Smith wrote two classic works, The Theory of Moral Sentiments (1759) and An Inquiry into the Nature and Causes of the Wealth of Nations (1776). The latter, often abbreviated as The Wealth of Nations, is considered his magnum opus and the first modern work of economics. In his work, Adam Smith introduced his theory of absolute advantage.[8] Smith studied social philosophy at the University of Glasgow and at Balliol College, Oxford, where he was one of the first students to benefit from scholarships set up by fellow Scot John Snell. Smith laid the foundations of classical free market economic theory. Biography[edit]

Percolation threshold Percolation threshold is a mathematical term related to percolation theory , which is the formation of long-range connectivity in random systems. Below the threshold a giant connected component does not exist; while above it, there exists a giant component of the order of system size. In engineering and coffee making , percolation represents the flow of fluids through porous media, but in the mathematics and physics worlds it generally refers to simplified lattice models of random systems or networks (graphs), and the nature of the connectivity in them. [ edit ] Percolation models The most common percolation model is to take a regular lattice, like a square lattice, and make it into a random network by randomly "occupying" sites (vertices) or bonds (edges) with a statistically independent probability p . In the systems described so far, it has been assumed that the occupation of a site or bond is completely random—this is the so-called Bernoulli percolation. [ edit ] 2-Uniform Lattices

Schubin Cafe » All-Mobile Video implicit 3D eyewear range at NAB 2010 As I roamed the exhibits at the NAB show this month, I kept wondering what other year it seemed most like. And I was not alone. There were plenty of important issues covered at the show, from citizen journalism to internet-connected TV. And then there was the elephant in the room. It would be a lie to say that 3D technologies could be found at every booth on the show floor. In acquisition technology, for example, LED lighting was near ubiquitous, with focusable instruments, such as the Litepanels Sola, sometimes painfully bright. In storage technology, Cache-A, For-A, IBM, and Sony all showed in new offerings that tape is not dead. Cinedeck looks like a viewfinder but includes built-in storage and editing capability. In wireless distribution, there was VµbIQ’s 60 GHz uncompressed transmitter on a chip and Streambox’s Avenir for bonding up to four cellular modems to create a 20 Mbps channel. The same thing was said of HD, however, in its early days. In

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