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.


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