this seems like a solid empirical generative representation but I don’t feel comfortable assuming it is a causally accurate generative model. it appears overparameterized without causal justification to me. certainly we can fit known data using this, but it explicitly bakes in an assumption of non-generalization. perhaps that’s the only significant claim being made? but I don’t see how we can even generalize that the sharpness of the breaks is reliable. ethan says come at me, I say this is valid but does not refine predictive distribution significantly and that is itself the difficult problem we’d hope to solve in the first place.
[humor] could one use this method to represent the convergence behavior of researchers with crackpots as the size of the cracks in crackpots’ pot decreases and the number of objects colliding with respected researchers’ pots increases?
this seems like a solid empirical generative representation but I don’t feel comfortable assuming it is a causally accurate generative model. it appears overparameterized without causal justification to me. certainly we can fit known data using this, but it explicitly bakes in an assumption of non-generalization. perhaps that’s the only significant claim being made? but I don’t see how we can even generalize that the sharpness of the breaks is reliable. ethan says come at me, I say this is valid but does not refine predictive distribution significantly and that is itself the difficult problem we’d hope to solve in the first place.
[humor] could one use this method to represent the convergence behavior of researchers with crackpots as the size of the cracks in crackpots’ pot decreases and the number of objects colliding with respected researchers’ pots increases?