[mindcrime]

It’s really not very good

Compared to what?

But my advice is, skip this one.

And read what instead?

Not trying to start an argument here, I'm genuinely curious, as I consider How To Measure Anything to be one of the best books I've ever read (and I read a lot of books), and I recommend it highly to, well, pretty much everybody. If you feel that there's a better resource out there that relates to these topics, I'd be curious to know about it.

[thanatropism]

I'm not very fond of Taleb, but well -- anything by Taleb.

Exercise books for Fermi estimates like Guesstimation, etc.

Further out, something in systems thinking, maybe Donella Meadows' "Thinking in systems". Further further out, maybe those Stafford Beer papers about the Viable Systems Model? At one point Beer and Allende thought they were about to implement Red Plenty.

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I understand the businessy logic that nothing is so fundamentally qualitative that it shouldn't be quantified. But you'll always be safer if you keep rich qualitative models and treat quantification as gravy on top of that.

The extreme opposite of rich qualitative models is the Soviet method of material balances. Halfway through there's the McNamara Fallacy:

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

[mindcrime]

I'm not very fond of Taleb, but well -- anything by Taleb

Yeah, Fooled by Randomness and The Black Swan were both pretty good. I haven't necessarily thought of them as significantly overlapping with the HTMA stuff up until this point, but now that you mention it I can see a connection. I should probably go back and re-read both, and read Antifragile.

maybe those Stafford Beer papers about the Viable Systems Model?

Hmm... never heard of "Viable Systems Model" before, so I'll have to go read up on that. Thanks for the pointer.

Exercise books for Fermi estimates like Guesstimation, etc

I'll take a look at Guesstimation. Thanks for the pointer on that as well.

But you'll always be safer if you keep rich qualitative models and treat quantification as gravy on top of that.

I can buy that. I'm a fan of using approaches like Hubbard's to quantify things to a point. I do think his approach can supply a bit of extra rigor and some useful bounds to things that otherwise seem impossible to quantify at all. But it's not a perfect system by any means. The two biggest risks, so far as I can tell, would be leaving a variable (or more than one) out of your model completely, or using the wrong probability distributions for the various variables when doing the simulation part.