‘Complex’ and ‘complicated’: If a system—despite the fact that it may consist of a huge number of components—can be given a complete description in terms of its individual constituents, such a system is merely complicated. #Cilliers
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In a complex system, the interaction among constituents of the system and the interaction between the system and its environment, are of such a nature that the system as a whole cannot be fully understood simply by analysing its components.
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<- Moreover, these relationships are not fixed, but shift and change, often as a result of self-organisation. This can result in novel features, usually referred to in terms of emergent properties. The brain, natural language and social systems are complex.
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If something is really complex, it cannot be adequately described by means of a simple theory.
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A complex system is not constituted merely by the sum of its components, but also by the intricate relationships between these components. In ‘cutting up’ a system, the analytical method destroys what it seeks to understand.
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The rise of powerful technology is not an unconditional blessing. We have to deal with what we do not understand, and that demands new ways of thinking.
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Unfortunately the concept [‘complexity’] remains elusive at both the qualitative and quantitative levels. One useful description, by Luhmann states that complexity entails that, in a system, there are more possibilities than can be actualised.
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<- This can hardly serve as definition, but perhaps one should not be surprised if complexity cannot be given a simple definition.
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The distinction between ‘simple’ and ‘complex’ is not as sharp as we may intuitively think. ->
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Many systems appear simple, but reveal remarkable complexity when examined closely (e.g. a leaf). Others appear complex, but can be described simply, e.g. some machines, such as the internal combustion engine.
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In developing a description of the characteristics of complex systems, I offer the following list:
#Cillierspic.twitter.com/HOEWCVIbIa
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The ten characteristics proposed here help us to talk about complexity in a qualitative way, but do not provide us with a method to measure complexity. We would like to be able to deal with complexity in a more quantitative way, especially when modelling complex systems.
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Despite the elegance of the entropy theory, it is not an adequate model for the understanding of complex systems like human cognition (see Katz and Dorfman 1992), where the intricate structure certainly cannot be equated with ‘randomness’.
#Cillierspic.twitter.com/npKj7BEPIc
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A complex system cannot be reduced to a collection of its basic constituents, not because the system is not constituted by them, but because too much of the relational information gets lost in the process.
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To describe a complex system you have, in a certain sense, to repeat the system.
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In order to respond appropriately to its environment, a complex system must be able to gather information about that environment and store it for future use. Meaning, the structure of the system cannot consist of a random collection of elements; they must have some meaning.
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A complex system, such as a living organism or a growing economy, has to develop its structure and be able to adapt that structure in order to cope with changes in the environment.
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Claiming an external designer (or Designer) merely begs the question of the origin of complexity, and we have to find mechanisms by which a system can acquire and adapt its internal structure on an evolutionary basis. The key concept here is the notion of self-organisation.
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<- This does not imply that a complex system contains some form of internal ‘subject’ that controls the behaviour of the system; in fact, the whole notion of central control becomes suspect.
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One can say that science without philosophy is blind, and philosophy without science is paralysed.
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On the one hand, models of complexity will only become successful in scientific practice once we begin to understand more about the nature of complexity. On the other hand, our understanding will only improve if we actually test our models scientifically.
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Can a system like natural language or the human brain really be reduced to a set of rules? Classical AI claims that it can. [
#Cilliers doesn't think so] Before turning to another approach, let me summarise the main characteristics of rule-based systems:pic.twitter.com/NHXzyVis0v
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In general there is a split between the supporters of strong AI, who claim that formal systems provide an adequate model for all aspects of human intelligence, and the supporters of weak AI, who merely see formal systems as a powerful tool.
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It should be clear that I wish to argue that connectionist models are more useful for the understanding and modelling of complex systems than are rule-based models.
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Traditional rule-based and analytical approaches to complex systems are flawed, and insights from postmodern and post-structural theory can help us to find novel ways of looking at complexity.
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