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Frag City

As the accumulation of knowledge in a disciplinary domain advances, the knowledge base naturally starts fragmenting into sub-disciplines and turf battles break out all over: “my sub-discipline is more reflective of reality than yours, damn it!“.

In a bit of irony, the systems thinking discipline, which is all about holism, has followed the well worn yellow brick road that leads to frag city. For example, compare the disparate structures of two (terrific) books on systems thinking:

Thank Allah there is at least some overlap, but alas, it’s a natural law: with growth, comes a commensurate increase in complexity. Welcome to frag city…

  1. PNeumiller
    January 17, 2012 at 9:15 am

    I would argue that “Systems Thinking” is still a relatively new en-devour, as compared to something like Mathematics or Physics or Chemistry. The real science is still being eked out as our new found super powerful computing facilities are just beginning to be harnessed in this field. Many of these types of books are very anecdotal which is a reflection of their relative immaturity IMHO. That’s just my opinion, and I am almost always wrong.

    • January 17, 2012 at 11:00 am

      Yeah, you’re right relative to the more “vertical” disciplines. Hard systems is pretty mathematically rigorous, but the soft systems “forks” started when people realized that modeling people (who have their own personal agendas relative to dumb/mechanistic parts) in math didn’t turn out to well for predicting socio-technical system behavior. 🙂

      You’re not wrong. I’m the one who’s always wrong.

  2. January 17, 2012 at 2:00 pm

    In a way, this is what “Architecture of the Problem” is about. There is an hourglass-shaped fan-out which goes something like this: Contexts | Domain Knowledge |Solutions.

    The first and third are broadest, and they tend narrow to #2

    Our culture seems to be “solution happy”. Somebody spots a context for a problem, and they solve it. Somebody else spots a different context, and they solve that. Frequently they come up with an orthogonal solution; sometimes they borrow ideas from the original, before coming up with a variant solution. But it takes a long time for domain knowledge to jell.

    Sometimes the disciplines never converge. For example, in software control of electromechanical systems, voltages must be read and written to I/O ports attached to sensors and actuators. Deterministic scheduling is critical. Hence the need for a RTOS. But several things need to happen at once. Hence the need for threads, interrupts and concurrency control mechanisms like mutexes and semaphores. But to make it all work in electromagnetic control systems, you also need thread priorities and priority inversion control mechanisms.

    Switching contexts, DBMS systems also have their own scheduling problems. Several transactions being executed concurrently need ACID properties (atomic, concurrent, isolated and durable). People want to be sure that their paycheck is deposited before their rent or mortgage bill is paid.

    Yet, how long did it take for what we learned about realtime control and databases to mix and blend? And why is it so hard for an electromagnetic system to roll back from an unexpected error, when DBMS do this routinely? And why is it so hard for complex financial transactions (like a trade of 500 related securities) to settle as a unit?

    In order to learn things about the world,quickly (and given the rate at which the world changes, it is critical to learn quickly), we need to stop jumping so quickly from narrow contexts to solutions. We need to learn to pause in the middle, and understand the challenges.

    One of the important things about challenges is that the are additive. Solutions rarely are.


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