Aleatoric Uncertainty Is A Skill Issue
Viral essay claims most 'randomness' is just epistemic uncertainty—a skill issue in modeling.
Deep Dive
A viral LessWrong post by Florian_Dietz, co-written with Claude, argues that classical examples of aleatoric uncertainty (coin flips, dice rolls, thermal noise) are actually deterministic systems where our models lack resolution. Under deterministic interpretations like Many-Worlds or Bohmian mechanics, all uncertainty becomes epistemic. The post challenges the standard textbook distinction, suggesting labeling something 'aleatoric' often reflects epistemic humility deficits rather than ontological randomness.
Why It Matters
For AI/ML practitioners, this reframes how we model uncertainty and assign error, impacting probabilistic reasoning and system design.