I haven’t had much explicit interaction with these inside/outside view concepts, and maybe I’m misunderstanding the terminology, but a couple of the examples of outside views given struck me as more like inside views: Yelp reviews and the advice of a friend are calibrated instruments being used to measure the performance of a restaurant, ie to build a model of its internal workings.
But then almost immediately, I thought, “hey, even the inside view is an outside view.” Every model is an analogy, e.g. an analogy in the sense of this thing A is a bit like thing B, so probably it will behave analogously, or e.g. 5 seconds ago the thing in my pocket was my wallet, so the thing in my pocket is probably still my wallet. It doesn’t really matter if the symmetry we exploit in our modeling involves translation through space, translation from one bit of matter to another, or translation through time: strictly speaking, it’s still an analogy.
I have no strong idea what implications this might have for problem solving. Perhaps there is another way of refining the language that helps. What I’m inclined to identify as the salient features of (what I understand to be) the inside view is that (subject to some super-model) there is a reasonable probability that the chosen model is correct, whereas for the outside view we are fairly certain that the chosen model is not correct, though it may still be useful. This strikes me as usefully linked to the excellent suggestions here regarding weighted application of multiple models. Perhaps the distinction between inside and outside views is a red herring, and we should concentrate instead on working out our confidence in each available model’s ability to provide useful predictions, acknowledging that all models are necessarily founded on analogies, with differing degrees of relevance.
Keynes in his “Treatise on probability” talks a lot about analogies in the sense you use it here, particularly in “part 3: induction and analogy”. You might find it interesting.
I haven’t had much explicit interaction with these inside/outside view concepts, and maybe I’m misunderstanding the terminology, but a couple of the examples of outside views given struck me as more like inside views: Yelp reviews and the advice of a friend are calibrated instruments being used to measure the performance of a restaurant, ie to build a model of its internal workings.
But then almost immediately, I thought, “hey, even the inside view is an outside view.” Every model is an analogy, e.g. an analogy in the sense of this thing A is a bit like thing B, so probably it will behave analogously, or e.g. 5 seconds ago the thing in my pocket was my wallet, so the thing in my pocket is probably still my wallet. It doesn’t really matter if the symmetry we exploit in our modeling involves translation through space, translation from one bit of matter to another, or translation through time: strictly speaking, it’s still an analogy.
I have no strong idea what implications this might have for problem solving. Perhaps there is another way of refining the language that helps. What I’m inclined to identify as the salient features of (what I understand to be) the inside view is that (subject to some super-model) there is a reasonable probability that the chosen model is correct, whereas for the outside view we are fairly certain that the chosen model is not correct, though it may still be useful. This strikes me as usefully linked to the excellent suggestions here regarding weighted application of multiple models. Perhaps the distinction between inside and outside views is a red herring, and we should concentrate instead on working out our confidence in each available model’s ability to provide useful predictions, acknowledging that all models are necessarily founded on analogies, with differing degrees of relevance.
Keynes in his “Treatise on probability” talks a lot about analogies in the sense you use it here, particularly in “part 3: induction and analogy”. You might find it interesting.