There’s still a couple related fallacies that Bayesians can commit.
Most related to the “ludic fallacy” as you’ve described it: if you treat both epistemic (lack of knowledge) and aleatory (lack of predetermination) uncertainty with the same general probability distribution function framework, it becomes tempting to try to collapse the two together. But a PDF-over-PDFs-over-outcomes still isn’t the same thing as a PDF-over-outcomes, and if you try to compute with the latter you won’t get the right results.
Most related to the “ludic fallacy” as I inferred it from Taleb: if you perform your calculations by assigning zero priors to various models, as everybody does to make the calculations tractable, then if evidence actually points towards one of those neglected priors and you don’t recompute with it in mind, you’ll find that your posterior estimates can be grossly mistaken.
There’s still a couple related fallacies that Bayesians can commit.
Most related to the “ludic fallacy” as you’ve described it: if you treat both epistemic (lack of knowledge) and aleatory (lack of predetermination) uncertainty with the same general probability distribution function framework, it becomes tempting to try to collapse the two together. But a PDF-over-PDFs-over-outcomes still isn’t the same thing as a PDF-over-outcomes, and if you try to compute with the latter you won’t get the right results.
Most related to the “ludic fallacy” as I inferred it from Taleb: if you perform your calculations by assigning zero priors to various models, as everybody does to make the calculations tractable, then if evidence actually points towards one of those neglected priors and you don’t recompute with it in mind, you’ll find that your posterior estimates can be grossly mistaken.