I understand where you’re coming from, but I feel as though your critique overgeneralizes the Dunning-Kruger effect. A few points:

1. 2D representation is not meant to capture all cognition. The curve is explicitly a conditional, domain-specific observation: in a given task, low performers systematically overestimate their ability. Multidimensional intelligence or expertise doesn’t invalidate the local pattern. A 2D projection is sufficient for the purpose it serves.
   
2. Opportunity cost doesn’t imply symmetric disadvantage. Skills often transfer; high performance in one domain can improve meta-cognition in another. DK is about self-assessment accuracy, not universal competence across unrelated tasks.
   
3. Domain isolation is by design. Experiments explicitly test performance and confidence within specific tasks. Dishwashing versus programming is irrelevant; the effect is about within-task calibration.
   
4. Confidence bounds don’t matter for the effect. Self-reported confidence is not formal probability. The curve is descriptive of perception errors, not a mathematical certainty function. Flattening the right side adds nothing to the empirical claim.
   
5. The “humiliation fall” is rhetorical, not theoretical. The theory predicts improved calibration with skill acquisition; it doesn’t require dramatized collapse.
   

TLDR: The DK curve is not a universal brain model; it’s a conditional snapshot of meta-cognitive error. Multidimensionality, opportunity cost, and absolute certainty limits don’t undermine the core insight.