Like how it's easy to progress a game by just updating the state each time, but if the client then asks for an undo feature, then that "perturbation" forces you to redesign your program around keeping a state history, or calculate state from a list of events. Stuff like that.
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If we could figure out some common perturbations, then we can determine if a given system is fragile, in that the customer is very likely to ask for a feature that forces us to start over. Fex "show entries in group" might lead to a request for "show all groups containing entry"
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In that case it's way easier to incorporate that request if you have a relational model than if you're schemaless Then you might have something like "show all categories these two entries share" which makes things worse So SQL would be more stable under common perturbations
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On the whole we accept that requirements often change, but we don't really study it as a phenomena, just treat it like an act of god or something I still like the idea but I can't quite make the essay as watertight as I'd like so it's mouldering in draft hell
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That's a dangerous path to go down because when taken too far it leads to far more rigid, overly engineered systems because we can never know the unknown unknowns. It's oftentimes best to build the absolute minimum, so as to not fall into the sunk cost fallacy.
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Why do we have to take it too far? Why can't we say "99 out of a 100 times the client will at some point ask for feature X, so let's make sure that adding feature X later won't be a nightmare death march"
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YAreGNSQL
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What are you doing here Get off this hellsite
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Reminds me of the idea that if you use a formula f(x) where x is your specific case, it's helpful to also measure f(x+delta) to see how different the outcome could be if the situation changed.
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I cribbed the term from perturbation theory in physics
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