Sure. When I say “arbitrary”, I mean not based on evidence, or on any kind of robust reasoning. I think that’s the same as your conception of it.
The “conclusion” of your model is a recommendation between giving now vs. giving later, though I acknowledge that you don’t go as far as to actually make a recommendation.
To explain the problem with arbitrary inputs, when working with a model, I often try to think about how I would defend any conclusions from the model against someone who wants to argue against me. If my model contains a number that I have simply chosen because it “felt” right to me, then that person could quite reasonably suggest a different number be used. If they are able to choose some other reasonable number that produces different conclusions, then they have shown that my conclusions are not reliable. The key test for arbitrary assumptions is: will the conclusions change if I assume other values?
Otherwise, arbitrary assumptions might be helpful if you want to conduct a hypothetical “if this, then that” analysis, to help understand a particular dynamic at play, like bayesian probability. But this is really hard if you’ve made lots of arbitrary assumptions (say 10-20); it’s difficult to get any helpful insights from “if this and this and this and this and........, then that”.
So yes, we are in a bind when we want to make predictions about the future where there is no data. Who was it that said “prediction is difficult, especially about the future”? ;-) But models that aren’t sufficiently grounded in reality have limited benefit, and might even be counterproductive. The challenge with modelling is always to find ways to draw robust and useful conclusions given what we have.
Sure. When I say “arbitrary”, I mean not based on evidence, or on any kind of robust reasoning. I think that’s the same as your conception of it.
The “conclusion” of your model is a recommendation between giving now vs. giving later, though I acknowledge that you don’t go as far as to actually make a recommendation.
To explain the problem with arbitrary inputs, when working with a model, I often try to think about how I would defend any conclusions from the model against someone who wants to argue against me. If my model contains a number that I have simply chosen because it “felt” right to me, then that person could quite reasonably suggest a different number be used. If they are able to choose some other reasonable number that produces different conclusions, then they have shown that my conclusions are not reliable. The key test for arbitrary assumptions is: will the conclusions change if I assume other values?
Otherwise, arbitrary assumptions might be helpful if you want to conduct a hypothetical “if this, then that” analysis, to help understand a particular dynamic at play, like bayesian probability. But this is really hard if you’ve made lots of arbitrary assumptions (say 10-20); it’s difficult to get any helpful insights from “if this and this and this and this and........, then that”.
So yes, we are in a bind when we want to make predictions about the future where there is no data. Who was it that said “prediction is difficult, especially about the future”? ;-) But models that aren’t sufficiently grounded in reality have limited benefit, and might even be counterproductive. The challenge with modelling is always to find ways to draw robust and useful conclusions given what we have.