Complex system
This article largely discusses complex systems as a subject of mathematics and the attempts to emulate physical complex systems with emergent properties. For other scientific and professional disciplines addressing complexity in their fields see the complex systems article and references. A complex system is a damped, driven system (for example, a harmonic oscillator) whose total energy exceeds the threshold for it to perform according to classical mechanics but does not reach the threshold for the system to exhibit properties according to chaos theory. History[edit] Although it is arguable that humans have been studying complex systems for thousands of years, the modern scientific study of complex systems is relatively young in comparison to conventional fields of science with simple system assumptions, such as physics and chemistry. Types of complex systems[edit] Chaotic systems[edit] For a dynamical system to be classified as chaotic, it must have the following properties:[2]
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Swarm intelligence
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Taxonomy of Swarm Intelligence Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Natural vs. Scientific vs. Natural/Scientific: Foraging Behavior of Ants Artificial/Scientific: Clustering by a Swarm of Robots Several ant species cluster corpses to form cemeteries. Natural/Engineering: Exploitation of collective behaviors of animal societies Artificial/Engineering: Swarm-based Data Analysis References E. J.
Mystery Science Theater 3000
The show mainly features a man and his robot sidekicks who are imprisoned on a space station by an evil scientist and forced to watch a selection of bad movies, as part of a psychological experiment, and frequently preceded by short public-domain educational films, newsreels, or serial dramas. To stay sane, the man and his robots provide a running commentary on each film, making fun of its flaws, and wisecracking their way through each reel in the style of a movie-theater peanut gallery. Each film is presented with a superimposition of the man and robots' silhouettes along the bottom of the screen. The film is interspersed with skits tied into the theme of the film being watched or the episode as a whole. Premise[edit] Joel and Mike have no control over when the movies start, because Joel utilized the parts that would've allowed him to do so to build the robots. Format[edit] An example of MST3K's Shadowrama effect used as the central motif for the show. Background and history[edit]
Systems thinking
Impression of systems thinking about society[1] A system is composed of interrelated parts or components (structures) that cooperate in processes (behavior). Natural systems include biological entities, ocean currents, the climate, the solar system and ecosystems. Systems Thinking has at least some roots in the General System Theory that was advanced by Ludwig von Bertalanffy in the 1940s and furthered by Ross Ashby in the 1950s. Systems thinking has been applied to problem solving, by viewing "problems" as parts of an overall system, rather than reacting to specific parts, outcomes or events and potentially contributing to further development of unintended consequences. In systems science, it is argued that the only way to fully understand why a problem or element occurs and persists is to understand the parts in relation to the whole.[3] Standing in contrast to Descartes's scientific reductionism and philosophical analysis, it proposes to view systems in a holistic manner.
Part 1: The essential collection of visualisation resources
This is the first part of a multi-part series designed to share with readers an inspiring collection of the most important, effective, useful and practical data visualisation resources. The series will cover visualisation tools, resources for sourcing and handling data, online learning tutorials, visualisation blogs, visualisation books and academic papers. Your feedback is most welcome to help capture any additions or revisions so that this collection can live up to its claim as the essential list of resources. This first part presents the data visualisation tools associated with conducting analysis, creating effective graphs and implementing business intelligence operations. Please note, I may not have personally used all tools presented but have seen sufficient evidence of their value from other sources. Also, to avoid re-inventing the wheel, descriptive text may have been reproduced from the native websites for some resources. Microsoft Excel Status: Ongoing (July 7, 2011) QlikView Gephi
Explaining biological strategy (2)
The first step in understanding To be able to understand how biological systems can create order out of disorder, it is necessary to first shake off all the preconceived ideas that have been programmed into our minds by conventional education. To do this we have to go back almost a century (1909), to an abstract model first proposed by the great German mathematician, David Hilbert (1862-1943). This sounds ridiculous, because it doesn't seem possible that anyone can visualize a space with infinite dimensions, but, dimensions can also be called parameters. How this creates an order in this space can be imagined if you take any single parameter and imagine every item with that same parameter as being strung out in a line along it. This would apply to all parameters, so you can think of the space as being crisscrossed by an infinite number of parameter lines that can intersect with one another. The importance of this mental model is the paradigm shift it brings about. Nature's algorithm
Satellite News
