The Scalar Fallacy (2011)

>But, though we have to do it all the time, no one has ever found a perfect way of assigning scalars to vectors and no one ever will.

Write the vector (x, y) in [0,1]^2 in its binary representation as (x0.x1x2..., y0.y1y2...), resolving any repeating sequences of 1s, and let f(x, y) = 0.x0y0x1y1x2y2...

Then f is injective and therefore bijective onto its image.

(I know this is incredibly pedantic and f isn't continuous/doesn't preserve whatever mathematical structure, but I think you're inviting this comparison when you write a blog post that is just saying "You can't represent everything 1 dimensionally" which everyone knows but unnecessarily couched in mathematical jargon)

> Let's say we have formulas for deriving two metrics, L and Q. L correlates very well with longevity; Q correlates very well with quality of life. For most questions about health policy, you will get similar answers with either metric, but there are cases where the two diverge sharply. Both L and Q are good measures of health, but their usefulness depends on the question you need answered.

We've all just lived through this. Governments and many people have such a tunnel vision for L that they completely neglect Q. Lockdowns, masks, remote learning, capacity restrictions etc. Keep grandma as safe as possible at all costs. Keep even young kids away from covid at any cost to their social, mental, or physical development, despite their low risk even without a vaccine. Force everyone to sit on a plane with a stuffy mask for hours regardless of how safe and well-ventilated it is and how well vaccines work. Safety from theoretical death above all else.

Next the author will attempt to convince us that our favorite linear assumptions are somehow hinky.

It's always that one spoiler guy.

I’d rate the article an 8 out of 10.