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{{Short description|System that can adapt to the environment}}
{{Refimprove|date=November 2008}}
{{Refimprove|date=November 2008}}


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, in a way analogous to either continuous physiological [[homeostasis]] or evolutionary [[adaptation]] in [[biology]]. [[Feedback loops]] represent a key feature of adaptive systems, such as [[ecosystems]] and individual [[organisms]]; or in the human world, [[communities]], [[organizations]], and [[families]].
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, in a way analogous to either continuous physiological [[homeostasis]] or evolutionary [[adaptation]] in [[biology]]. [[Feedback loops]] represent a key feature of adaptive systems, such as [[ecosystems]] and individual [[organisms]]; or in the human world, [[communities]], [[organizations]], and [[families]]. Adaptive systems can be organized into a hierarchy.


Artificial adaptive systems include [[robots]] with [[control system]]s that utilize [[negative feedback]] to maintain desired states.
Artificial adaptive systems include [[robots]] with [[control system]]s that utilize [[negative feedback]] to maintain desired states.
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==The law of adaptation==
==The law of adaptation==


The law of adaptation can be stated informally as:
The law of adaptation may be stated informally as:
{{quote|Every adaptive system converges to a state in which all kind of stimulation ceases.<ref>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. [https://dx.doi.org/10.1007/s11047-008-9096-6 doi]</ref>}}
{{quote|Every adaptive system converges to a state in which all kind of stimulation ceases.<ref>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. [https://dx.doi.org/10.1007/s11047-008-9096-6 doi]</ref>}}


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==Benefit of self-adjusting systems==
==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 “[[edge of chaos|adaptation to the edge of chaos]]” or ability to avoid [[chaos theory|chaos]]. Practically speaking, by heading to the [[edge of chaos]] without going further, a leader may act spontaneously yet without disaster. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications.<ref>Hübler, A. & Wotherspoon, T.: "Self-Adjusting Systems Avoid Chaos". Complexity. 14(4), 8 – 11. 2008</ref> Physicists have shown that [[adaptation]] to the [[edge of chaos]] occurs in almost all systems with [[feedback]].<ref>{{cite journal|last1=Wotherspoon|first1=T.|last2=Hubler|first2=A.|title=Adaptation to the edge of chaos with random-wavelet feedback|journal=J Phys Chem A|doi=10.1021/jp804420g}}</ref>
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 “[[edge of chaos|adaptation to the edge of chaos]]” or ability to avoid [[chaos theory|chaos]]. Practically speaking, by heading to the [[edge of chaos]] without going further, a leader may act spontaneously yet without disaster. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications.<ref>Hübler, A. & Wotherspoon, T.: "Self-Adjusting Systems Avoid Chaos". Complexity. 14(4), 8 – 11. 2008</ref> Physicists have shown that [[adaptation]] to the [[edge of chaos]] occurs in almost all systems with [[feedback]].<ref>{{cite journal|last1=Wotherspoon|first1=T.|last2=Hubler|first2=A.|title=Adaptation to the edge of chaos with random-wavelet feedback|journal=J Phys Chem A|volume=113|issue=1|pages=19–22|doi=10.1021/jp804420g|pmid=19072712|year=2009|bibcode=2009JPCA..113...19W}}</ref>

==Practopoiesis==
''Practopoiesis,'' a term due to its originator Danko Nikolić, is a reference to a kind of adaptive or self-adjusting system in which [[autopoiesis]] of an ''organism'' or a ''cell'' occurs through [[allopoiesis|allopoietic]] interactions among its ''components''.<ref name=Nikolic2015>{{cite journal|url=http://www.sciencedirect.com/science/article/pii/S002251931500106X|title=Practopoiesis: Or how life fosters a mind. |author=Danko Nikolić|date=2015|doi=10.1016/j.jtbi.2015.03.003|volume=373|journal=Journal of Theoretical Biology|pages=40–61}}</ref> The components are organized into a [[poiesis|poietic]] hierarchy: one component creates another. The theory proposes that living systems exhibit a hierarchy of four such poietic operations in total:

''evolution'' (i) &rarr; ''gene expression'' (ii) &rarr; ''non gene-involving homeostatic mechanisms (anapoiesis)'' (iii) &rarr; ''cell function'' (iv)

Practopoiesis challenges current neuroscience doctrine by asserting that mental operations primarily occur at the anapoietic level (iii) &mdash; i.e., that minds emerge from fast homeostatic (adaptive) mechanisms. This contrasts the widespread belief that thinking is synonymous with neural activity ('cell function' at level iv).

Each lower level contains knowledge that is more general than the higher level; for example, genes contain more general knowledge than anapoietic mechanisms, which in turn contain more general knowledge than cell functions. This hierarchy of knowledge enables the anapoietic level to directly store concepts, which are necessary for the emergence of mind.


==See also==
==See also==
{{Portal|Evolutionary biology}}
{{Portal|Evolutionary biology}}
{{div col|colwidth=22em}}
{{div col|colwidth=22em}}
* [[Autopoiesis]]
* [[Adaptive immune system]]
* [[Adaptive immune system]]
* [[Artificial neural network]]
* [[Artificial neural network]]
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* [[Diffusion of innovations]]
* [[Diffusion of innovations]]
* [[Ecosystems]]
* [[Ecosystems]]
* [[Gaia hypothesis]]
* [[Gene expression programming]]
* [[Gene expression programming]]
* [[Genetic algorithms]]
* [[Genetic algorithms]]
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==References==
==References==
* {{cite journal
* {{cite journal
| last = Martin H. | first = Jose Antonio. | authorlink = Jose Antonio Martin H.
| last = Martin H. | first = Jose Antonio | author-link = Jose Antonio Martin H.
| author2 = [[Javier de Lope]]; [[Darío Maravall]]
| author2 = Javier de Lope
| author2-link = Javier de Lope
| author3 = Darío Maravall
| author3-link = Darío Maravall
| title = Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature
| title = Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature
| journal = Natural Computing
| journal = Natural Computing
| volume = 8 | issue = 4
| volume = 8 | issue = 4
| pages = 757–775
| pages = 757–775
| publisher = Springer
| date = 2009
| date = 2009
| doi = 10.1007/s11047-008-9096-6
| doi = 10.1007/s11047-008-9096-6
| s2cid = 2723451 }}
}}


==External links==
==External links==
{{Wiktionary | anapoiesis}}
{{Wiktionary | anapoiesis}}
{{Wiktionary | practopoiesis}}
{{Wiktionary | practopoiesis}}
* Funny [https://www.youtube.com/watch?v=WIzsz03X8qc animated video] explaining the theory of practopoiesis, made by Mind & Brain.
*Practopoiesis offers solutions to [http://www.danko-nikolic.com/long-standing-problems-solved-by-practopoiesis/ nine long-standing problems] in neuroscience and philosophy of mind

[[Category:Control engineering]]
[[Category:Control engineering]]
[[Category:Cybernetics]]
[[Category:Organizational cybernetics]]
[[Category:Systems theory]]

Latest revision as of 08:22, 30 October 2024

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, in a way analogous to either continuous physiological homeostasis or evolutionary adaptation in biology. Feedback loops represent a key feature of adaptive systems, such as ecosystems and individual organisms; or in the human world, communities, organizations, and families. Adaptive systems can be organized into a hierarchy.

Artificial adaptive systems include robots with control systems that utilize negative feedback to maintain desired states.

The law of adaptation

[edit]

The law of adaptation may be stated informally as:

Every adaptive system converges to a state in which all kind of stimulation ceases.[1]

Formally, the law can be defined as follows:

Given a system , we say that a physical event is a stimulus for the system if and only if the probability that the system suffers a change or be perturbed (in its elements or in its processes) when the event occurs is strictly greater than the prior probability that suffers a change independently of :

Let be an arbitrary system subject to changes in time and let be an arbitrary event that is a stimulus for the system : we say that is an adaptive system if and only if when t tends to infinity the probability that the system change its behavior in a time step given the event is equal to the probability that the system change its behavior independently of the occurrence of the event . In mathematical terms:

  1. -
  2. -

Thus, for each instant will exist a temporal interval such that:

Benefit of self-adjusting systems

[edit]

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 “adaptation 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. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications.[2] Physicists have shown that adaptation to the edge of chaos occurs in almost all systems with feedback.[3]

See also

[edit]

Notes

[edit]
  1. ^ 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. doi
  2. ^ Hübler, A. & Wotherspoon, T.: "Self-Adjusting Systems Avoid Chaos". Complexity. 14(4), 8 – 11. 2008
  3. ^ Wotherspoon, T.; Hubler, A. (2009). "Adaptation to the edge of chaos with random-wavelet feedback". J Phys Chem A. 113 (1): 19–22. Bibcode:2009JPCA..113...19W. doi:10.1021/jp804420g. PMID 19072712.

References

[edit]
[edit]