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Feedback Loops.md

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Feedback Loops

  • Every action creates an equal and opposite reaction. When reactions loop back to affect themselves, a feedback loop is created.
  • There are two types of feedback loops: positive and negative.
    • Positive feedback amplifies system output, resulting in growth or decline.
    • Negative feedback dampers output, stabilizes the system around an equilibrium point.
  • [[Network Effects|Things are connected]]. Changing one variable in a [[Systems|system]] will affect other variables in that system and other systems. This is important because it means that designers must not only consider particular elements of a design, but also their relation to the design as a whole and to the greater environment.
  • All complex [[systems]] are subject to positive and negative feedback loops whereby A causes B, which in turn influences A (and C), and so on – with higher-order effects frequently resulting from continual movement of the loop.
  • Feedback loops vary in their accuracy.
    • Accurate feedback means that it reliably and clearly tells you when you do something right. If you get the quadratic formula wrong, you can check the right formula and know what was wrong.
    • Inaccurate feedback loop means that the results of the evaluation phase are “noisy” and contain significant variance, so the next cycle will need to take that into account. E.g: playing bowls without a coach.
      • Learning under conditions of noisy data starts with world construction. Imagine a possible future, and repeat this to generate hundreds of possible future worlds. The main skills and resources required are creativity, [[slack]], and equanimity. Creativity leads to a higher rate of idea generation and [[slack]] gives us more time to generate ideas. Equanimity is important because it allows us to persevere in the absence of tangible feedback.
  • The shorter and more accurate the feedback loop, the easier it is to learn. The tighter your feedback loop, the better your work.
    • Fast and accurate loops might get you on a local maximum or might not work when the underlying system is noisy.

Examples