Original reasons for adopting a belief may be tangled and forgotten, but if your differing beliefs lead to differing concrete predictions, you should be able to test them without diving into the original justifications.
This particular issue of how much people care about mathematical simplicity of models seems to be affecting things a lot, and people who disagree on that simply talk past each other.
For a concrete example just look at Robin’s recent Malthusian posts, which I (simple-model-skeptic) find utterly ridiculous due to their reliance on model with known false assumptions, conflicts with a lot of empirical data, and great uncertainty about the future, and Robin (simple-model-lover) basically says because we don’t have any better model that’s equally simply, this one must be true, and can be extrapolated as much as we feel like.
I feel somewhat better now, with Kaj clarifying it. I can imagine there are probably many other such cases where people completely disagree about their worldview.
For a concrete example just look at Robin’s recent Malthusian posts, which I (simple-model-skeptic) find utterly ridiculous due to their reliance on model with known false assumptions, conflicts with a lot of empirical data, and great uncertainty about the future, and Robin (simple-model-lover) basically says because we don’t have any better model that’s equally simply, this one must be true, and can be extrapolated as much as we feel like.
I tend to agree. I’ve got the distinct impression that the dubious assumptions you’ve mentioned are motivated by orthodoxy more than accuracy.
I find it useful (or at least interesting) to explore what things may be like if we follow certain assumptions to their conclusion. Robin’s ‘burning the cosmic commons’ analysis is an example I gleaned insight from. However when I am using such models to make actual predictions about the future I take far more care in my assumption selection.
Yes, you can also use new evidence to compare the intuitions, instead of going back to the old ones. I actually meant to say this in the post, but didn’t come across very clearly. Not that it’d make much of a difference—you’re still looking at general trends instead of anything tightly and narrowly defined, so you still need a small mountain of cases to test the predictions on.
Original reasons for adopting a belief may be tangled and forgotten, but if your differing beliefs lead to differing concrete predictions, you should be able to test them without diving into the original justifications.
This particular issue of how much people care about mathematical simplicity of models seems to be affecting things a lot, and people who disagree on that simply talk past each other.
For a concrete example just look at Robin’s recent Malthusian posts, which I (simple-model-skeptic) find utterly ridiculous due to their reliance on model with known false assumptions, conflicts with a lot of empirical data, and great uncertainty about the future, and Robin (simple-model-lover) basically says because we don’t have any better model that’s equally simply, this one must be true, and can be extrapolated as much as we feel like.
I feel somewhat better now, with Kaj clarifying it. I can imagine there are probably many other such cases where people completely disagree about their worldview.
I tend to agree. I’ve got the distinct impression that the dubious assumptions you’ve mentioned are motivated by orthodoxy more than accuracy.
I find it useful (or at least interesting) to explore what things may be like if we follow certain assumptions to their conclusion. Robin’s ‘burning the cosmic commons’ analysis is an example I gleaned insight from. However when I am using such models to make actual predictions about the future I take far more care in my assumption selection.
Yes, you can also use new evidence to compare the intuitions, instead of going back to the old ones. I actually meant to say this in the post, but didn’t come across very clearly. Not that it’d make much of a difference—you’re still looking at general trends instead of anything tightly and narrowly defined, so you still need a small mountain of cases to test the predictions on.