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. Related: `test 1022 • `test 1022

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. It is also called complex systems theory, complexity science, study of complex systems, sciences of complexity, non-equilibrium physics, and historical physics. 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 1. Americas Europe

Comment fonctionne le cerveau quand on apprend ? Le comportement du cerveau pendant l’apprentissage demeure aujourd’hui un grand mystère pour nombre d’éducateurs. Cependant, si l’émergence de la neuro-pédagogie a déjà permis de lever le voile sur certains des aspects du fonctionnement du système nerveux et cérébral de l’être humain pendant le processus d’apprentissage, plusieurs zones d’ombre demeurent encore. Comment fonctionne véritablement le cerveau pendant le processus d’apprentissage ? De quelle manière l’environnement et les différents milieux de vie de l’apprenant influencent-ils son processus d’apprentissage ? De la diversité des cerveaux et de leurs modes de fonctionnement Les théories sur le fonctionnement du cerveau et de ses hémisphères sont légions, se contredisant parfois les unes les autres. « Pour apprendre – et enseigner – il est nécessaire de créer des schèmes structuraux solides entre les neurones. Une thèse que semble confirmer les travaux de nombre de chercheurs.

Comment fonctionne le cerveau quand on apprend ? Le comportement du cerveau pendant l’apprentissage demeure aujourd’hui un grand mystère pour nombre d’éducateurs. Cependant, si l’émergence de la neuro-pédagogie a déjà permis de lever le voile sur certains des aspects du fonctionnement du système nerveux et cérébral de l’être humain pendant le processus d’apprentissage, plusieurs zones d’ombre demeurent encore. Comment fonctionne véritablement le cerveau pendant le processus d’apprentissage ? De la diversité des cerveaux et de leurs modes de fonctionnement Les théories sur le fonctionnement du cerveau et de ses hémisphères sont légions, se contredisant parfois les unes les autres. « Pour apprendre – et enseigner – il est nécessaire de créer des schèmes structuraux solides entre les neurones. Une thèse que semble confirmer les travaux de nombre de chercheurs. L’environnement ambiant comme facteur déterminant à l’issue du processus L’évolution du cerveau dépendrait donc énormément des diverses influences de notre environnement.

Complex response A complex response refers to an environmental reaction to change that occurs at multiple levels to multiple objects, and can induce a chain reaction of responses to a single initial change. It is akin to the butterfly effect: one small event (change) can cascade through a given system creating new agents of change, and operating at several levels. The term is most commonly used in fluvial geomorphology, or the study of river systems and changes within those systems.[1] complex system

Cascading failure An animation demonstrating how a single failure may result in other failures throughout a network. A cascading failure is a failure in a system of interconnected parts in which the failure of a part can trigger the failure of successive parts. Such a failure may happen in many types of systems, including power transmission, computer networking, finance and bridges. Cascading failures usually begin when one part of the system fails. When this happens, nearby nodes must then take up the slack for the failed component. Cascading failure in power transmission[edit] Cascading failure is common in power grids when one of the elements fails (completely or partially) and shifts its load to nearby elements in the system. This failure process cascades through the elements of the system like a ripple on a pond and continues until substantially all of the elements in the system are compromised and/or the system becomes functionally disconnected from the source of its load. Examples[edit] History[edit]

Emergence In philosophy, systems theory, science, and art, emergence is a process whereby larger entities, patterns, and regularities arise through interactions among smaller or simpler entities that themselves do not exhibit such properties. Emergence is central in theories of integrative levels and of complex systems. For instance, the phenomenon life as studied in biology is commonly perceived as an emergent property of interacting molecules as studied in chemistry, whose phenomena reflect interactions among elementary particles, modeled in particle physics, that at such higher mass—via substantial conglomeration—exhibit motion as modeled in gravitational physics. Neurobiological phenomena are often presumed to suffice as the underlying basis of psychological phenomena, whereby economic phenomena are in turn presumed to principally emerge. In philosophy, emergence typically refers to emergentism. In philosophy[edit] Main article: Emergentism Definitions[edit] Strong and weak emergence[edit]

Complex systems biology Complex systems biology (CSB) is a branch or subfield of mathematical and theoretical biology concerned with complexity of both structure and function in biological organisms, as well as the emergence and evolution of organisms and species, with emphasis being placed on the complex interactions of, and within, bionetworks,[1] and on the fundamental relations and relational patterns that are essential to life.[2][3][4][5][6] CSB is thus a field of theoretical sciences aimed at discovering and modeling the relational patterns essential to life that has only a partial overlap with complex systems theory,[7] and also with the systems approach to biology called systems biology; this is because the latter is restricted primarily to simplified models of biological organization and organisms, as well as to only a general consideration of philosophical or semantic questions related to complexity in biology. Network Representation of a Complex Adaptive System Telomerase structure and function

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]

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. The concept of self-organization is central to the description of biological systems, from the subcellular to the ecosystem level. 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] Examples[edit]