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Adaptive System

Adaptive system


Biological adaptation

The term adaptation is used in biology in relation to how living beings adapt to their environments, but with two different meanings. First, the continuous adaptation of an organism to its environment, so as to maintain itself in a viable state, through sensory feedback mechanisms. Second, the development (through evolutionary steps) of an adaptation (an anatomic structure, physiological process or behavior characteristic) that increases the probability of an organism reproducing itself (although sometimes not directly).

General definition

Generally speaking, an adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts. Feedback loops represent a key feature of adaptive systems, allowing the response to changes; examples of adaptive systems include: natural ecosystems, individual organisms, human communities, human organizations, and human families. Some artificial systems can be adaptive as well; for instance, robots employ control systems that utilize feedback loops to sense new conditions in their environment and adapt accordingly.

The Law of Adaptation

Every adaptive system converges to a state in which all kind of stimulation ceases.


Benefit of Self-Adjusting Systems

In an adaptive system, a parameter changes slowly and has no preferred value. In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”. One of the most important qualities of self-adjusting systems is its “adaption to the edge of chaos” or ability to avoid chaos. Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster.



Adaptation across levels of organization

A theory of how systems adapt across different levels of organisation is called practopoiesis. According to that theory, the purpose of an adaptation processes at each lower level of organisation is creation of the adaptation mechanism at the next higher level of organisation. For a living system such as an animal or a person, a total of three such hierarchical steps of adaptation are needed — and such systems are denoted as T3:

  1. At the lowest level of a T3-system lay gene expression mechanisms, which, when activated, produce machinery that can adapt the system at higher levels of organization.
  2. The next higher level corresponds to various physiological structures other than gene expression mechanisms. In the nervous system, these higher mechanisms adjust the properties of the neural circuitry such that they operate with the pace much faster than the gene expression mechanisms. These faster adaptive mechanisms are responsible for e.g., neural adaptation.
  3. Finally, at the top of that adaptive hierarchy lays the electrochemical activity of neuronal networks together with the contractions of the muscles. At this level the behaviour of the organism is generated.

When an entire species is considered as an adaptive system, one more level of organization must be included: the evolution by natural selection—making a total of four adaptive levels, or a T4-system.

Artificial Systems

In contrast, artificial systems such as machine learning algorithms or neural networks are adaptive only at two levels or organizations (T2). According to practopoiesis, this lack of a deeper adaptive hierarchy of machines is the main limitation factor for their capability to achieve intelligence.


Linguistic derivation

The term Adaptive is derived from the Latin verb adaptāre, which is a combination of the prefix ad- meaning "to; at" + verb aptāre meaning "to fit".
The term System is derived from Latin systēma, which may originate from the Greek word sustēma (σύστημα), which is a combination of the prefix syn- meaning "with; together" + verb histanai meaning "to cause; to stand".


External Sources


José Antonio Martín H., Javier de Lope and Darío Maravall: "Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature" Natural Computing, December, 2009. Vol. 8(4), pp. 757-775.





Autopoiesis refers to a system that is capable of creating, maintaining and reproducing itself. Autopoietic mechanisms can operate as self-generating feedback systems.

Historical Frame

The term was introduced in 1972 by Chilean biologists Humberto Maturana and Francisco Varela to define the self-maintaining chemistry of living cells. Since then the concept has been also applied to the fields of systems theory and sociology.

Autopoiesis was originally presented as a system description that was said to define and explain the nature of living systems. A canonical example of an autopoietic system is the biological cell. The eukaryotic cell, for example, is made of various biochemical components such as nucleic acids and proteins, and is organized into bounded structures such as the cell nucleus, various organelles, a cell membrane and cytoskeleton. These structures, based on an external flow of molecules and energy, produce the components which, in turn, continue to maintain the organized bounded structure that gives rise to these components.

Autopoiesis: Components - Boundary - Processes


Related concepts

Allopoietic system

An autopoietic system is to be contrasted with an allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized structure) which is something other than itself (the factory). However, if the system is extended from the factory to include components in the factory's 'environment', such as supply chains, plant / equipment, workers, dealerships, customers, contracts, competitors, cars, spare parts and so on, then as a total viable system it could be considered to be autopoietic. Thus, an autopoietic system is a closed topological space that continuously generates and specifies its own organization. It maintains this through its operation as a system of production of its own components, and does this in an endless turnover of components. Autopoietic systems are thus distinguished from allopoietic systems, which have as the product of their functioning something different from themselves.


A theory of how autopoietic systems operate is named Practopoiesis (praxis + poiesis, meaning creation of actions). The theory presumes that, although the system as a whole is autopoietic, the components of that system may have allopoietic relations. For example, the genome combined with the operations of the gene expression mechanisms create proteins, but not the other way around; proteins do not create genomes. In that case poiesis occurs only in one direction. Practopoietic theory presumes such one-directional relationships of creation to take place also at other levels of system organisation.

Self-organizing Intelligence

Many scientists have often used the term autopoiesis as a synonym for self-organization. An autopoietic system is autonomous and operationally closed, in the sense that there are sufficient processes within it to maintain the whole. Autopoietic systems are "structurally coupled" with their medium, embedded in a dynamic of changes that can be recalled as sensory-motor coupling. This continuous dynamic is considered as a rudimentary form of knowledge or cognition and can be observed throughout life-forms. Autopoiesis would be the process of the emergence of necessary features out of chaotic contingency, causing contingency's gradual self-organisation, thus leading to the gradual rise of order out of chaos.

Linguistic derivation

The term Autopoiesis is derived from ancient Greek words auto- (αὐτo-) meaning "self", and poiesis (ποίησις), meaning "creation" or  "production". 

External sources


Book: Maturana, H., & Varela, F. (1992). The tree of knowledge: The biological roots of human understanding. Boston: Shambhala.


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