User:Anthony.Sebastian/CAS

From Citizendium
< User:Anthony.Sebastian
Revision as of 14:39, 16 March 2013 by imported>Anthony.Sebastian
Jump to navigation Jump to search

Lede

'Complex adaptive systems' refer to numerous types of complex systems characterized by their ability to “change and reorganize their component parts to adapt themselves to the problems posed by their surroundings[1], exploiting one or more of the many types of adaptation, including Darwinian natural selection.

Examples of complex adaptive systems include biological organisms, the immune system, economic systems, ant colonies, ecosystems, developing embryos, developing biological organ systems, computerized virtual species, social systems, the brain in function and development, the stock market, language.

Such complex adaptive systems comprise a self-organized system of interacting components (or agents) that can change and learn in an adaptive way, a way that enables them to persist, with modification, through indefinite time, despite changing environmental conditions, in particular conditions that put the system’s endurance at risk. Pioneer elucidator, John Holland, describes them as similar in the sense of having an “evolving structure”.[1]

Background

 • Systems

The New Oxford American Dictionary defines ‘system’ as:

a set of connected things or parts forming a complex whole, in particular: a set of things working together as parts of a mechanism or an interconnecting network: the state railroad system | fluid is pushed through a system of pipes or channels.[2]

The human or animal body as a whole thus constitutes a system, as do a set of organs in the body with a common structure or function, for example the ‘endocrine system’ with its common function of hormonal modulation of target organ physiology.[2]

Scientists distinguish systems from the surroundings, or environment, that embeds them, implying some kind of boundary for the system, physical or virtual, across which interactions may occur with the environment depending on the nature of the system. An isolated system cannot interact with its environment, so sealed it cannot exchange information in the form of matter or energy with its surroundings.

An ‘open’ system can exchange matter and energy with its surroundings, possibly with selectivities, as in the case of the human body, which cannot exchange cannonballs or such large objects. We will see that complex adaptive systems qualify as open systems.

 • Complex systems

 • Adaptation

Shared characteristics of complex adaptive systems

 • Ability to evolve

 • Aggregate behavior—emergent behavior

 • Ability to anticipate—learning

 • Self-organizing and self-maintaining

Notes

References

  1. 1.0 1.1 Holland JH. (1992) Complex Adaptive Systems. Daedalus 121(1):17-30. | Clicking on title will download full-text PDF.
  2. 2.0 2.1 New Oxford American Dictionary (3 ed.) Edited by Angus Stevenson, Christine A. Lindberg. Oxford University Press. Current Online Version: 2012. eISBN 9780199891535.



My miscellany

Holland, Signals and boundaries

[1]



"Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other cas. Despite a wealth of data and descriptions concerning different cas, there remain many unanswered questions about "steering" these systems. In Signals and Boundaries, John Holland argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering cas through the mechanisms that generate their signal/boundary hierarchies. Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes."