@davidmanheim @rlmcelreath I'd argue models, as the name implies, should be restricted to concept of representationpic.twitter.com/apz64izPAk
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@davidmanheim @rlmcelreath I'd argue models, as the name implies, should be restricted to concept of representationpic.twitter.com/apz64izPAk
@LifeOfMyMoon @rlmcelreath ES Quade (1972); So represent for purpose of choice; good model depends on which choice.pic.twitter.com/ANndxKM63r
@davidmanheim @rlmcelreath Complicated way of stating that a function from choice to outcome should be modeled well. Still descriptive.
@davidmanheim @rlmcelreath Interested in a choice? Make it a variable x. Find f(inputs, x) -> output. f is your model, x can be optimized.
@LifeOfMyMoon @rlmcelreath That's unreasonable; you never have perfect models, you make tradeoffs between accuracy about different things.
@LifeOfMyMoon @rlmcelreath If you need f(inputs, x, y, z) to optimize x, or y, your best model is different. (Like the optimizer's curse).
@davidmanheim @rlmcelreath Yes, now the system is larger. Your model is higher fidelity in one part than another. Still a representation.
@davidmanheim @rlmcelreath A Taylor series is a model of a function; you've *chosen* a point to approximate locally, but still has veracity.
@LifeOfMyMoon @rlmcelreath Right; those are explicitly different models; which model you use depends on purpose - neither is "false".
@LifeOfMyMoon @rlmcelreath Actually, the no free lunch theorem effectively says explicitly that no model is best for all questions.
@davidmanheim @rlmcelreath Definitely agree here. There is a degeneracy of states: multiple models have the same veracity, different focus
@davidmanheim @rlmcelreath Except, NFL doesn't really apply: they hold information constant. Can form better model by obtaining perfect info
@LifeOfMyMoon @rlmcelreath Yes. Unfortunately, models exist to represent imperfectly, usually given available data.
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