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Summary on adaptive systems
Topic: Adaptive systems
by Ratchapon, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on
As a definition of adaptive system is just a system that has an ability to adapt to change. A simple adaptive system is stable and has straightforward cause-and-effect relationships. Thus, we can easily predict what will happen using math and science or even not. In contrast, it is totally different when we look through its intrinsic components as complexity aspect.
Complex adaptive system
Figure 1: Complex adaptive system diagram received from (wikimedia.org, 2014)
Murray Gell-Mann believes that Complex adaptive system (CAS) should have general principles using for all such system across all interdisciplinary and have precise distinction among them. He also states that in such the CAS will have its own experience which will gain from ecological information, input (system behaviour) and output data (effects on the system). In CAS, schema, contains lots of experience, will provide information to the system for an action matter even for a case that has not been observed. There are also dynamic of networks of interaction in the internal system, used to exchange and gather information surrounding themselves (self-organized local relationship in figure 1) in order to be able to adapt in and evolve with its changing environment.
Additionally. there is also a feedback loop involved in the CAS from time to time to find best suitable action in schemata competition. The successful schema will be just tendency having high influence in the selection while demoted and even unsuccessful schema will remain in the system which may be useful to influence others or even again candidates under some circumstances. In a scientific enterprise, for instance, scientists will use theories (schemata) to predict or find a solution. In competition, theories are developed and explored to give an answer on a subject matter. Those older or unsuccessful will remain and may have some inspiration for other theories in the future.
In CAS, there are often that maladaptive features arise which is difficult to predict and define. This is because, in general, the CAS ecological consists of many coevolving internal systems and sub-systems. In each system, it will have different criterion on selection pressure. Furthermore, a mismatch in different timescales also can cause of the maladaptation which adaptation in such a system works in the past but maybe no longer applicable in different time scales. For example, an adaptive policy used to limit the size of a population in the past by peaceful methods adapt maladaptively in nowadays which people tend to use a destructive weapon.
As itself complexities including processes and operations, this subject matter is subjective. a CAS is interpreted differently in each discipline. Additionally, there is still less attention and it is too complicated to work with especially, to find a definition and distinction of the CAS. However, this is a great challenge to connect all level abstractions together while also finding their distinction for simplifying to understand more on how each universal system interacts. This is because such systems spawn other such as human thought give rise to computer-based CAS which has a tendency to give rise to other or even help other systems solving problems In technology nowadays, neuron networks in machine learning to give a solution which is not achievable by human or gives rise to other CAS by modelling or simulating.
Reference
Gell-Mann M. (1994). Complexity: Metaphors, Models, and Reality, Complex Adaptive System, p. 17-45
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Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on
As a definition of adaptive system is just a system that has an ability to adapt to change. A simple adaptive system is stable and has straightforward cause-and-effect relationships. Thus, we can easily predict what will happen using math and science or even not. In contrast, it is totally different when we look through its intrinsic components as complexity aspect.
Complex adaptive system
Figure 1: Complex adaptive system diagram received from (wikimedia.org, 2014)
Murray Gell-Mann believes that Complex adaptive system (CAS) should have general principles using for all such system across all interdisciplinary and have precise distinction among them. He also states that in such the CAS will have its own experience which will gain from ecological information, input (system behaviour) and output data (effects on the system). In CAS, schema, contains lots of experience, will provide information to the system for an action matter even for a case that has not been observed. There are also dynamic of networks of interaction in the internal system, used to exchange and gather information surrounding themselves (self-organized local relationship in figure 1) in order to be able to adapt in and evolve with its changing environment.
Additionally. there is also a feedback loop involved in the CAS from time to time to find best suitable action in schemata competition. The successful schema will be just tendency having high influence in the selection while demoted and even unsuccessful schema will remain in the system which may be useful to influence others or even again candidates under some circumstances. In a scientific enterprise, for instance, scientists will use theories (schemata) to predict or find a solution. In competition, theories are developed and explored to give an answer on a subject matter. Those older or unsuccessful will remain and may have some inspiration for other theories in the future.
In CAS, there are often that maladaptive features arise which is difficult to predict and define. This is because, in general, the CAS ecological consists of many coevolving internal systems and sub-systems. In each system, it will have different criterion on selection pressure. Furthermore, a mismatch in different timescales also can cause of the maladaptation which adaptation in such a system works in the past but maybe no longer applicable in different time scales. For example, an adaptive policy used to limit the size of a population in the past by peaceful methods adapt maladaptively in nowadays which people tend to use a destructive weapon.
As itself complexities including processes and operations, this subject matter is subjective. a CAS is interpreted differently in each discipline. Additionally, there is still less attention and it is too complicated to work with especially, to find a definition and distinction of the CAS. However, this is a great challenge to connect all level abstractions together while also finding their distinction for simplifying to understand more on how each universal system interacts. This is because such systems spawn other such as human thought give rise to computer-based CAS which has a tendency to give rise to other or even help other systems solving problems In technology nowadays, neuron networks in machine learning to give a solution which is not achievable by human or gives rise to other CAS by modelling or simulating.
Reference
Gell-Mann M. (1994). Complexity: Metaphors, Models, and Reality, Complex Adaptive System, p. 17-45