Outside of mathematical logic, some familiar examples include:

• compactness vs. sequential compactness—generalizing from metric to topological spaces
• product topology vs. box topology—generalizing from finite to infinite product spaces
• finite-dimensional vs. finitely generated (and related notions, e.g. finitely cogenerated)—generalizing from vector spaces to modules
• pointwise convergence vs. uniform convergence vs. norm-convergence vs. convergence in the weak topology vs....—generalizing from sequences of numbers to sequences of functions

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.

Oh, I read Taleb as using 'ludic fallacy' to mean using distributions with light tails.