Thanks, that’s quite clear. Should I reference abandonment of fundamental objects as the major feature of a paradigm shift?
In fact, in every case of inexpressibility that we know of, it’s been because one of the ways of thinking about the world didn’t give correct predictions.
Yes, every successful paradigm shift. Proponents of failed paradigm shifts are usually called cranks. :)
My position is that the repeated pattern of false fundamental objects suggest that we should give up on the idea of fundamental objects, and simply try to make more accurate predictions without asserting anything else about the “accuracy” of our models.
Predictions about the world are only possible to the extent the world controls the predictions, to the extent considerations you use to come up with the predictions correspond to the state of the world. So it’s not possible to make useful predictions based on considerations that don’t correspond to reality, or conversely if you manage to make useful predictions, there must be something in your considerations that corresponds to the world. See Searching for Bayes-Structure.
Isn’t “makes accurate predictions” synonymous with “corresponds to reality in some way” ? If there was absolutely no correspondence between your model and reality, you wouldn’t be able to judge how accurate your predictions were. In order to make such a judgement, you need to compare your predictions to the actual outcome. By doing so, you are establishing a correspondence between your model and reality.
Thanks, that’s quite clear. Should I reference abandonment of fundamental objects as the major feature of a paradigm shift?
Yes, every successful paradigm shift. Proponents of failed paradigm shifts are usually called cranks. :)
My position is that the repeated pattern of false fundamental objects suggest that we should give up on the idea of fundamental objects, and simply try to make more accurate predictions without asserting anything else about the “accuracy” of our models.
How can you make accurate predictions while at the same time discarding the notion of accuracy ?
I have no reason to expect that our models correspond to reality in any meaningful way, but I still think that useful predictions are possible.
Predictions about the world are only possible to the extent the world controls the predictions, to the extent considerations you use to come up with the predictions correspond to the state of the world. So it’s not possible to make useful predictions based on considerations that don’t correspond to reality, or conversely if you manage to make useful predictions, there must be something in your considerations that corresponds to the world. See Searching for Bayes-Structure.
Isn’t “makes accurate predictions” synonymous with “corresponds to reality in some way” ? If there was absolutely no correspondence between your model and reality, you wouldn’t be able to judge how accurate your predictions were. In order to make such a judgement, you need to compare your predictions to the actual outcome. By doing so, you are establishing a correspondence between your model and reality.