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Comment by dasil003 | Hacker News Reader

> It reminds me a lot of politics and I'm not sure why we're so resistant to nuance when our whole job is typically composed of nuance.

My theory is the increase in available information is overwhelming everyone's cognitive abilities, and so jumping to reductive conclusions is just a natural defense mechanism, leading to increased polarization and reduced listening/tolerance skills. Certainly software engineers have an above-average tolerance for nuance, but even we have to pick our battles or risk drowning in the flood.

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I agree and hold a similar theory. The human mind prefers to put things in boxes. And the simpler the box, the better.

There also seems to be a phenomena where the more complex the system the simpler the box must be. I'm kinda surprised by this as understanding complexities (especially around dynamic environments and future planning) seems to be one of the things that distinguishes humans from other animals. And opposable thumbs. I mean sure, there's other animals with those too but we stand out, literally.

If the box is simple, then it's easier to cast each individual complexity of the system, as needed, into an interpretation that fits and reinforces the box.

But don't you find it odd that my box is complex and that your box is simple? And that this holds for whoever you and I are?

This is probably related to the Fundamental Attribution Error.

https://en.wikipedia.org/wiki/Fundamental_attribution_error

I buy that one, but I think there's a coupling with metric availability.

Where we have measurements for pretty much everything but people don't know measurements are proxies and not always aligned with goals. Like how a ruler doesn't measure meters, but meters according to the ruler and only at the ruler's precision level. I can totally get how people who don't work with these tools don't understand the nuances, but it confuses me with experts. Isn't expertise, by definition, contingent upon understanding nuance?

It seems that the more metrics we have available to us, the less we care about understanding how those metrics work and what their limitations are. That they just become black boxes.

I mean there seems to be a very common belief that you can measure visual image quality by measuring some difference between an intermediate representation of a classification model. Or a belief that entropy measures quality of speech.

I'm really concerned that Goodhart's Law might be one of the Great Filters.

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