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Thread: Chaos and CAS

  1. #1 Chaos and CAS 
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    I was not sure what section to put this in. I am interested in application of complex system principles to human systems. However, a lot of the original work on chaos came from
    meteorologists, so I chose this section as opposed to "psychology". I am trying to understand, and hopefully explain, the key differences between a chaotic and an adaptive system. Which differences would you view as most important?

    What I think is important are:

    1. Chaotic systems are more likely to incubate catastrophe. An analogy would be rogue waves emerging from an ocean storm, or catastrophic failure in a human high hazard industry that has been repeatedly associated with "rule noncompliance". I think this goes back to the "Butterfly Effect".

    2. In an adaptive system, the agents follow simple rules that cannot be violated with impunity. In an adaptive system, there is a feedback mechanism to "improve the rules".
    Finally, an adaptive system has a "reward system" for "rule selection", so the best rules are chosen. Reward systems may also be present for agents to "follow the rules, and participate in "feedback".

    I am looking at high hazard industries and the 3 elements in #2 are generally present in these industries. If any of you guys have background in chaos or complex adaptive systems (CAS), your thoughts would be helpful, even if you think I am completely off base.


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  3. #2  
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    The reason most of the work you refer to has come from meteorology is that weather is chaotic. Meteorologists have had to do a lot of work identifying initial conditions at a point in time to get as close as they do for predictions for the immediately following days for farming, shipping and the like. The big issue is that chaotic weather occurs within climate which is highly determined - principally by physics, with a basis in astronomy.

    I suppose the big difference for analysis is that to work with weather your biggest task is to establish the initial conditions - so that you can do a prediction of any real value to people wanting to know when or whether they should leave port or start seeding or harvesting. Maybe the same issues apply to the kind of high hazard industries you're thinking of. I wouldn't know.

    For climate, all you need to get started are the standard radiative transfer equations and a couple of dozen other bits from previously established work, like Hadley cells, ENSO, IOD or thermohaline circulation and off you go. Depends what you want to find out which observations or statistics or historical/ paleo records are vital (or superfluous) for the investigation you're interested in. For weather you want every recent observation you can lay your hands on, as well as a good statistical history for the region of your prediction.

    I have no idea whether this kind of simple distinction applies anywhere else at all, let alone in industry.


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  4. #3  
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    Quote Originally Posted by adelady View Post
    The reason most of the work you refer to has come from meteorology is that weather is chaotic. Meteorologists have had to do a lot of work identifying initial conditions at a point in time to get as close as they do for predictions for the immediately following days for farming, shipping and the like. The big issue is that chaotic weather occurs within climate which is highly determined - principally by physics, with a basis in astronomy.

    I suppose the big difference for analysis is that to work with weather your biggest task is to establish the initial conditions - so that you can do a prediction of any real value to people wanting to know when or whether they should leave port or start seeding or harvesting. Maybe the same issues apply to the kind of high hazard industries you're thinking of. I wouldn't know.

    I have no idea whether this kind of simple distinction applies anywhere else at all, let alone in industry.
    Thanks Adelady:

    It is hard to know whether a principle from a nonliving system will apply to a living system. You are right that "sensitivity to initial conditions" is one of the key descriptors of a chaotic system; however, I am not sure how this would work in a human system. It also seems that much of the thinking about "adaptive systems" comes from studying insect colonies, that are leaderless and adapt from "bottom-up" feedback. In a human system, you need a leader, whether that is a person or a group of people, in order for someone to be able to make rules. Nevertheless, feedback from the front seems to be a key principle in human systems that succeed. A good example would be a companies that are highly innovative such as Gortex or 3M. I read a book that used Gortex as an example where they described how Gortex flattens hierarchy to maximally enable feedback. They felt that this was the source of their creativity, in addition to rewarding creative workers.

    So it seems that some principles are important, and others won't help you understand a human system.
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