Complex adaptive system

A complex adaptive system is the system that is complex in that it is a dynamic network of interactions, but the behavior of the ensemble may non be predictable according to the behavior of the components. it is for adaptive in that the individual in addition to collective behavior mutate in addition to self-organize corresponding to the change-initiating micro-event or collection of events. It is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in format to adapt to the changing environment and increase their survivability as a macro-structure. The Complex Adaptive Systems approach builds on replicator dynamics.

The study of complex adaptive systems, a subset of nonlinear dynamical systems, is an interdisciplinary matter that attempts to blend insights from the natural and social sciences to setting system-level models and insights that permit for heterogeneous agents, phase transition, and emergent behavior.

Overview

The term complex adaptive systems, or living, adaptable, changeable systems. Complex adaptive systems may follow hard or softer approaches. hard theories usage formal language that is precise, tend to see agents as having tangible properties, and commonly see objects in a behavioral system that can be manipulated in some way. Softer theories use natural Linguistic communication and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties. Examples of tough complexity theories increase Complex Adaptive Systems CAS and Viability Theory, and a a collection of matters sharing a common assigns of softer image is Viable System Theory. many of the propositional consideration submitted in hard opinion are also of relevance to softer theory. From here on, interest will now center on CAS.

The discussing of CAS focuses on complex, emergent and macroscopic properties of the system. John H. Holland said that CAS "are systems that hold a large numbers of components, often called agents, that interact and adapt or learn".

Typical examples of complex adaptive systems include: climate; cities; firms; markets; governments; industries; ecosystems; social networks; power to direct or defining to direct or determine grids; animal swarms; traffic flows; social insect e.g. ant colonies; the brain and the immune system; and the cell and the developing embryo. Human social group-based endeavors, such as political parties, communities, geopolitical organizations, war, and terrorist networks are also considered CAS. The internet and cyberspace—composed, collaborated, and managed by a complex mix of human–computer interactions, is also regarded as a complex adaptive system. CAS can be hierarchical, but more often exhibit aspects of "self-organization".

The term complex adaptive system was coined in 1968 by sociologist Walter F. Buckley who submitted a service example of cultural evolution which regards psychological and socio-cultural systems as analogous with biological species. In the sophisticated context, complex adaptive system is sometimes linked to memetics, or proposed as a reformulation of memetics. Michael D. Cohen and Robert Axelrod however argue the approach is non social Darwinism or sociobiology because, even though the concepts of variation, interaction and choice can be applied to modelling 'populations of institution strategies', for example, the detailed evolutionary mechanisms are often distinctly unbiological. As such, complex adaptive system is more similar to Richard Dawkins's idea of replicators.

What distinguishes a CAS from a pure perturbation.

Other important properties are adaptation or homeostasis, communication, cooperation, specialization, spatial and temporal organization, and reproduction. They can be found on any levels: cells specialize, adapt and reproduce themselves just like larger organisms do. Communication and cooperation construct place on any levels, from the agent to the system level. The forces driving co-operation between agents in such a system, in some cases, can be analyzed with game theory.

Some of the nearly important characteristics of complex systems are:

Robert Axelrod & Michael D. Cohen identify a series of key terms from a modeling perspective:

Turner and Baker synthesized the characteristics of complex adaptive systems from the literature and tested these characteristics in the context of creativity and innovation. regarded and mentioned separately. of these eight characteristics had been shown to be present in the creativity and modern processes: