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]
-onym Suffix used in linguistics The suffix -onym (from Ancient Greek: ὄνυμα, lit. 'name') is a bound morpheme, that is attached to the end of a root word, thus forming a new compound word that designates a particular class of names. In linguistic terminology, compound words that are formed with suffix -onym are most commonly used as designations for various onomastic classes. Most onomastic terms that are formed with suffix -onym are classical compounds, whose word roots are taken from classical languages (Greek and Latin). For example, onomastic terms like toponym and linguonym are typical classical (or neoclassical) compounds, formed from suffix -onym and classical (Greek and Latin) root words (Ancient Greek: τόπος / place; Latin: lingua / language). The English suffix -onym is from the Ancient Greek suffix -ώνυμον (ōnymon), neuter of the suffix ώνυμος (ōnymos), having a specified kind of name, from the Greek ὄνομα (ónoma), Aeolic Greek ὄνυμα (ónyma), "name". Words that end in -onym [edit]
Home | Santa Fe Institute Accidental Adversaries Accidental Adversaries is one of the ten system archetypes used in system dynamics modelling, or systems thinking. This archetype describes the degenerative pattern that develops when two subjects cooperating for a common goal, accidentally take actions that undermine each other's success. It is similar to the escalation system archetype in terms of pattern behaviour that develops over time.[1] Archetype description[edit] The archetype describes a pattern where two subjects have decided to work together because they will benefit from the alliance.[2] Each take actions believing that it will bring benefit to the other and if the cooperation works, they will both benefit from it. History[edit] The original set of system archetypes were published in The Fifth Discipline by Peter Senge. Description of the model[edit] Let's consider two parties A and B. Causal Loop diagram[edit] The causal loop diagram in Figure 1 shows the pattern dynamics of the system.[4] Figure 1. Stock and flow diagram[edit]
Fractal Figure 1a. The Mandelbrot set illustrates self-similarity. As the image is enlarged, the same pattern re-appears so that it is virtually impossible to determine the scale being examined. Figure 1b. The same fractal magnified six times. Figure 1c. Figure 1d. Fractals are distinguished from regular geometric figures by their fractal dimensional scaling. As mathematical equations, fractals are usually nowhere differentiable.[2][5][8] An infinite fractal curve can be conceived of as winding through space differently from an ordinary line, still being a 1-dimensional line yet having a fractal dimension indicating it also resembles a surface.[7]:48[2]:15 There is some disagreement amongst authorities about how the concept of a fractal should be formally defined. Introduction[edit] The word "fractal" often has different connotations for laypeople than mathematicians, where the layperson is more likely to be familiar with fractal art than a mathematical conception. History[edit] Figure 2.
Applications of artificial intelligence Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom reports.[1] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Computer science[edit] AI researchers have created many tools to solve the most difficult problems in computer science. Finance[edit] Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. Hospitals and medicine[edit] Heavy industry[edit] Music[edit]
Agent-Based Computational Economics (Tesfatsion) Growing Economies from the Bottom Up ACE Overview ACE Modeling Principles Real-world economies exhibit five essential properties. First, real-world economies consist of heterogeneous interacting entities encapsulating distinct states (data, attributes, methods). Taken together, these five essential properties imply that real-world economies are locally-constructive sequential games. Roughly defined, ACE is the computational modeling of economic processes (including whole economies) as open-ended dynamic systems of interacting agents. More precisely, the ACE modeling approach is characterized by the seven modeling principles listed below. (MP1) Agent Definition: An agent is a software entity within a computationally constructed world capable of acting on the basis of its own state, i.e., its own internal data, attributes, and methods. (MP2)Agent Scope: Agents can represent individuals, social groupings, institutions, biological entities, and/or physical entities. ACE Research Objectives
Anti-tech Revolution 2016 book by Ted Kaczynski Anti-Tech Revolution: Why and How is a 2016 non-fiction book by Ted Kaczynski.[1] Book structure[edit] There are four chapters and six appendices in the book:[2] Chapters: The Development of a Society Can Never Be Subject to Rational Human ControlWhy the Technological System Will Destroy ItselfHow to Transform a Society: Errors to AvoidStrategic Guidelines for an Anti-Tech Movement Appendices: In Support of Chapter OneIn Support of Chapter TwoStay on TargetThe Long-Term Outcome of Geo-EngineeringThurston's View of Stalin's Terror. Synopsis[edit] In the book, Kaczynski criticizes modern technological society (or "world-system" in Kaczynski's terminology) as a "self-propagating system" (which is a "self-propagating supersystem" consisting of various "self-propagating subsystems") that only seeks short-term benefits due to natural selection. Chapters 3 and 4 provide guidelines for an "anti-tech movement." Publication history[edit] See also[edit] Concepts References[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
Araucaria (software) The user interface is composed of a main window (diagramming), a schemes editor and the AraucariaDB online interface. While Araucaria helps identify the structure of an argument, it provides freedom of analysis resources. The scheme editor allows the user to create argumentation schemes, group them together and save them into a scheme set file. The scheme set is then applied to the diagram, entirely or in part. As an illustration, an argument scheme relying on symptoms could be applied to the following assertion: "The light has gone off. Therefore, the bulb must be broken", with critical questions intended to determine if the result could stem from another reason (such as "have all the lights in the flat gone off?"). The AraucariaDB Online Repository can be browsed to retrieve specific arguments to fit a diagram. Because it is based on XML, a standard widely used by developers, AML content can be accessed through other software that support XML.
System archetype System archetypes are patterns of behavior of a system. Systems expressed by circles of causality have therefore similar structure. Identifying a system archetype and finding the leverage enables efficient changes in a system. Circles of causality[edit] The basic idea of system thinking is that every action triggers a reaction. Reinforcing feedback (+)[edit] Reinforcing feedback (or amplifying feedback) accelerates the given trend of a process. Balancing feedback (−)[edit] Balancing feedback (or stabilizing feedback) will work if any goal-state exists. Delays[edit] Delays in systems cause people to perceive a response to an action incorrectly. Examples of system archetypes[edit] Balancing process with delay[edit] This archetype explains the system in which the response to action is delayed. Causal loop diagram "Balancing process with delay" Limits to growth[edit] The unprecedented growth is produced by a reinforcing feedback process until the system reaches its peak. Shifting the burden[edit]