How can the mistakes rationalists are making be expressed in the language of Bayesian rationalism? Priors, evidence, and posteriors are fundamental to how probability works.
The mistakes can (somewhat) be expressed in the language of Bayesian rationalism by doing two things:
Talking about partial hypotheses rather than full hypotheses. You can’t have a prior over partial hypotheses, because several of them can be true at once (though you can still assign them credences and update those credences according to evidence).
Talking about models with degrees of truth rather than just hypotheses with degrees of likelihood. E.g. when using a binary conception of truth, general relativity is definitely false because it’s inconsistent with quantum phenomena. Nevertheless, we want to say that it’s very close to the truth. In general this is more of an ML approach to epistemology (we want a set of models with low combined loss on the ground truth).
How can the mistakes rationalists are making be expressed in the language of Bayesian rationalism? Priors, evidence, and posteriors are fundamental to how probability works.
The mistakes can (somewhat) be expressed in the language of Bayesian rationalism by doing two things:
Talking about partial hypotheses rather than full hypotheses. You can’t have a prior over partial hypotheses, because several of them can be true at once (though you can still assign them credences and update those credences according to evidence).
Talking about models with degrees of truth rather than just hypotheses with degrees of likelihood. E.g. when using a binary conception of truth, general relativity is definitely false because it’s inconsistent with quantum phenomena. Nevertheless, we want to say that it’s very close to the truth. In general this is more of an ML approach to epistemology (we want a set of models with low combined loss on the ground truth).