User:Anthony.Sebastian/CAS
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
• Ability to evolve
• Aggregate behavior—emergent behavior
• Ability to anticipate—learning
• Self-organizing and self-maintaining
Notes
References
- ↑ 1.0 1.1 Holland JH. (1992) Complex Adaptive Systems. Daedalus 121(1):17-30. | Clicking on title will download full-text PDF.
- ↑ 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
"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."
Abstract Holland lecture
Short Course on "Steering Complex Adaptive Signals, Boundaries and Niches " Short Course on "Steering Complex Adaptive Signals, Boundaries and Niches" 08-Apr-2011 - 13-Apr-2011 1400hrs - 1600hrs VENUE : Auditorium, Level 2, Nanyang Executive Centre Contact Information : To register, please e-mail Ms Goy Hsu Ann [email protected] Dates : 8 April 2011 (Lecture 1)
11 April 2011 (Lecture 2)
13 April 2011 (Lecture 3)
Time : 2 to 4 pm venue : Auditorium, Level 2, Nanyang Executive Centre Admission is free. To register, please e-mail Ms Goy Hsu Ann [email protected] Lecture Abstract:
Complex adaptive systems (cas) are systems with a diverse array of agents that adapt and evolve, or learn, as they interact. Markets, biological cells, ecosystems, immune systems, and language communities are familiar examples. Several difficult questions confront us when we try to understand cas:
• What are typical steps in the formation of communities of interacting agents (such as niches in an ecosystem, metabolic networks in a biological cell, or “production lines” in an economy)?
• What kinds of agent interactions promote robustness in the face of shocks (consider the current worldwide “banking crisis”)?
• What are the mechanisms that yield complex agent hierarchies with sustainable diversity?
To answer such questions and improve our ability to steer cas, we must extract the mechanisms that underpin the origin and development of cas agents.
The human immune system provides a clear example of the difficulty of steering a cas. The immune system acts by parsing the surface signals of invading antigens, such as the flu virus. The flu virus continually adapts to the immune system, changing its surface signals so readily that a new flu vaccine must be provided each year, while staphylococcus finds a permanent home in hospitals. To compensate for these changes the immune system provides continually adapting agents -- antibodies -- that co-evolve with the changing boundaries that encapsulate the antigens. As another example, biological cells use a complex hierarchy of semi-permeable membranes to selectively pass certain protein signals that activate different parts of the cell's metabolic network.
In all cas, we can find semi-autonomous bounded compartments that act as agents -- organisms in an ecosystem, organelles in a biological cell, firms in an economy, and so on. The agent boundaries act as conditional filters that pass certain signals and deny others. The conditions that determine passage through a boundary typically look for tags that characterize the incoming signals that are to be admitted (or denied). Tags are exemplified by headers on internet messages, active sites on proteins in a biological cell's metabolic network, and the like. In all cases, we can find an "alphabet" (e.g. amino acids in the case of proteins) for construction of both tags and the boundaries that recognize them. The object then is to develop a "grammar" that determines how the letters of the "alphabet" can be combined to form useful tags. In more mathematical terms, we are looking for a set of generators (the "alphabet") and a set of rules for combining the generators (the "mechanisms"), much as one would proceed to define a finitely-generated mathematical group.
One way to accomplish this task is to provide a general formal language for defining agents. To be relevant for studying cas, the agents must exhibit three levels of activity: (i) performance -- a program or set of rules that determines the agent’s actions under any current situation; (ii) credit assignment -- determination of which performance rules are helpful and which are harmful, and (iii) rule discovery -- provision of plausible new rules for replacing harmful rules. There are rule systems, called classifier systems, that satisfy these requirements.
It is possible to use a generalization of the urn models of probability theory to illustrate the foregoing points. This generalization will be presented and it will be tied to Markov Processes as a way of proving theorems about the interaction of signals and boundaries.
About the Speaker:
John H. Holland is professor of computer science and engineering and professor of psychology at the University of Michigan; he is also external professor and member of the executive committee of the board of trustees at the Santa Fe Institute.
Professor Holland was made a MacArthur fellow in 1992 and is a fellow of the World Economic Forum. He serves on the Advisory Board on Complexity at the McDonnell Foundation.
Professor Holland has been interested for more than 40 years in what are now called complex adaptive systems (CAS). He formulated genetic algorithms, classifier systems, and the Echo models as tools for studying the dynamics of such systems. His books Hidden Order (1995) and Emergence (1998) summarize many of his thoughts about complex adaptive systems.