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Notes -
Part of the problem though that I keep coming back to has to do with modeling solutions. We all model reality whether it’s by simple or more complicated means: equations, statistical inference, physical laws, human intuition, etc., but these are only as good as the faulty human beings can make them. Models are virtual representations of real world phenomena. We run models in the virtual space of our minds all the time.
When you select a model though, you have to choose one that has all the relevant parameters. But one thing they always teach you as you learn this is that the more parameters you add, you increase the scope of uncertainty exponentially. The worst of these problems happen when there are so many degrees of freedom in your equations you can find an excellent fit for the data using the wrong model. Von Neumann taught us this in the 20th century:
“With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.”
The classic way of attempting to mitigate this is sensitivity analysis (studying how the output of the model is related to changes in the input parameters). The other problem works in reverse of this example and that’s when people think that “simple” means “inaccurate.” It’s why politicians and decision makers are irrationally skeptical of you if you can predict where a trend is going with only a few parameters. But even if you have an accurate model? You have to know how to use it.
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