I think it’s important that the overarching reasoning was some form of probabilism for “obvious” reasons I won’t go into.
I think it was important that it was Bayesianism in particular for a few reasons, some better than others.
Bayesianism allows probabilism to be applied in the most broad way. Frequentist and propensity interpretations of probability both hold that it’s inappropriate to apply probabilistic judgement in hypothesis testing. This makes it much more difficult to apply lessons from probabilistic reasoning, since you’re being restricted in where to apply them. (Of course, if that restriction were appropriate then it would be better to avoid applying the lessons of probability...)
Although vanilla Bayesianism is subjectivist about the prior, it offers a completely objective story about how reasoning should go once we’ve fixed the prior. I recently argued against this aspect of classical Bayesianism. However, I can see how this was an advantage in terms of memetics—a totally objective story for this part makes for strong dividing lines between correct and incorrect reasoning.
The addition of algorithmic information theory also offers a “more objective” story about the prior.
As I have recently argued, classical Bayesianism ends up sidelining some important “frequentist” properties, which we should also want. So, to an extent, my current perspective is a hybrid of Bayesianism and frequentism. But given a choice between the two, it seems much better that I started out Bayesian and had to figure out how to integrate frequentist ideas, rather than the other way around.
I think it’s important that the overarching reasoning was some form of probabilism for “obvious” reasons I won’t go into.
I think it was important that it was Bayesianism in particular for a few reasons, some better than others.
Bayesianism allows probabilism to be applied in the most broad way. Frequentist and propensity interpretations of probability both hold that it’s inappropriate to apply probabilistic judgement in hypothesis testing. This makes it much more difficult to apply lessons from probabilistic reasoning, since you’re being restricted in where to apply them. (Of course, if that restriction were appropriate then it would be better to avoid applying the lessons of probability...)
Although vanilla Bayesianism is subjectivist about the prior, it offers a completely objective story about how reasoning should go once we’ve fixed the prior. I recently argued against this aspect of classical Bayesianism. However, I can see how this was an advantage in terms of memetics—a totally objective story for this part makes for strong dividing lines between correct and incorrect reasoning.
The addition of algorithmic information theory also offers a “more objective” story about the prior.
As I have recently argued, classical Bayesianism ends up sidelining some important “frequentist” properties, which we should also want. So, to an extent, my current perspective is a hybrid of Bayesianism and frequentism. But given a choice between the two, it seems much better that I started out Bayesian and had to figure out how to integrate frequentist ideas, rather than the other way around.