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PaperclipPerfector  1yr ago (text post)   28112 thread views

Culture War Roundup for the week of September 18, 2023

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I feel like the unexamined assumption in both this post and many of the replies is "WTF does it even mean to be <quote>accurate<\quote> in this context?"

And even if some autist were to attempt to codify it, the Engineer's Hymn strikes me as the only appropriate response.

The careful text-books measure
Let all who build beware
of the shock, the load, the pressure
a material can bear
So, when the buckled girder
lets down the grinding span
The blame of loss, or murder
is laid upon the man
Not on the Steel - the Man!
But, in our daily dealings
with stone and steel, we find
The Gods have no such feelings
of justice toward mankind
To no set gauge they make us
for no laid course prepare
In time they overtake us
with loads we cannot bear
The prudent text-books give it
in tables at the end
The stress that shears a rivet
or makes a tie-bar bend
What traffic wrecks macadam
what concrete should endure
But we poor Sons of Adam
Have no such literature

In short, If your goal is to come up with some sort of systematic means of judging group membership so you don't have to put the effort into judging individual merit, you're inevitably going to get a garbage result because garbage in is garbage out.

Even shorter, you're asking the wrong questions.

Edit:formatting

I don’t believe anyone operates this way in real life.

We all use models that are GIGO if said model provides a slightly better view of the world and it is very costly to acquire a better model. It’s called a heuristic.

So yes, I’m going to mind my P&Qs more when I see generic male black youths walking in my direction compared to generic Asian girl youths.

If I am approving someone for say a loan, then I have harder data and therefore the cost to acquire a better model is rather limited.

If the girls were all tatted-up and the guys were dressed like this would that change your calculus any?

A pet theory I've been playing around with is that the reason that the intellectually inclined introverts seem to gravitate towards intersectionality/HBD is that for whatever reason they never developed the ability to read any social signal more discrete than "looks like me = friend, looks like other = threat".

And all these pet theories you have are Bulverism, derived by working backwards from "The HBD believers are wrong, bad, and stupid, let's try to come up with a reason 'why' in a way that insults them". Which is fine if you're into insults but not a good way of determining anything about the world.

Are you also going to argue that Kepler's equations are wrong or not worth studying because he arrived at them by working backwards from a belief that the academic consensus of his day was incorrect?

Edit to add: Contra the popular consensus here, the measure of a theory's quality is not in it's intellectual or social merit, but a combination of how well it explains observed reality and how well it predicts future behavior.

I think I defined it fairly unambiguously:

Let's suppose that we know with certainty that people in group X have a statistically higher rate of bad feature Y compared to the average population, whether that be criminality, laziness, low intelligence, or are just unpleasant to be around. I'm taking the fact that this is accurate as an axiom. The actual proportion of people in group X with feature Y is objectively (and known to you) higher than average, but is not universal. That is, Y is a mostly discrete feature, and we have 0 < p < q < 1 where p is the probability of a randomly sampled member of the public has Y, and q is the probability that a randomly sampled member of q has Y.

It's "accurate" in that the literal proportion of people with trait Y in the general population and the group, in real life are p and q respectively, with p < q, and we also believe this to be true. As opposed to an inaccurate stereotype representing a false belief. In-so-far as Y actively impacts merit, then membership in X does provide a real signal correlated with merit.

Obviously actually measuring merit directly is superior to imperfect correlations, but if you are, for instance, hiring someone for a job, imperfect correlations are the only thing you have up until you actually hire someone and watch them perform the job. Literally everything you judge on is going to be an imperfect correlation of some form, so it's just a question of which ones you use and how much weight you put on each.

It's "accurate" in that the literal proportion of people with trait Y in the general population and the group, in real life are p and q respectively..."

The thing is that you haven't explained why that should matter.

Why are you trying to avoid measuring merit? Is it Laziness? or is it lack of ability/bandwidth?

In many cases it's where merit is difficult to measure up front. If you are looking at job applications, you can't literally perceive merit until you've already hired someone, and thus excluded the other candidates. If you're trying to avoid rapists, you can't perceive merit until they've literally attempted or succeeded at raping someone. If you're looking for romantic partners, a 1 minute analysis based on group membership is 120 times cheaper than going on a 2 hour date, and thus potentially worthwhile if the amount of information you can extract from it is 1% as much.

The optimal Bayesian thing to from a purely selfishly rational perspective seems to be using immediately identifiable group membership as a first screening pass (establishing the prior) and then update with more direct merit measures as/if they become available.

In many cases it's where merit is difficult to measure up front.

...And yet I don't think it's as hard as people make it out to be. To use the hiring example just last week I was in the position of vetting a bunch of potential new hires. Our organization uses the abbreviated (30 question) Wonderlic in conjunction with an internally generated field-specific aptitude/skill test. I feel like between the test scores and simply talking to each candidate individually for 15 - 20 minutes we were able to get a pretty good sense of each one's "vibe" and sort the ones we wanted to call back, from the ones we don't.

While I will grant that this method may not be practical for the sort of "all we need is a warm body" job that @FarNearEverywhere refers to I can't help but wonder how much of that is a product of the attitude that "all we need is a warm body". I recognize that I am fortunate to have a dozen applicants for 3 open slots. I can afford to be picky. At the same time there is price to be paid for not being picky. I'd rather be in the field myself with my guys collecting overtime because we're shorthanded than hire some shit-wit who's going to make a mess of things and potentially get someone killed.

Okay but you don't seem to be arguing against categorizing people, you're mostly just suggesting that accurate categories are superior to inaccurate categories. I'm not especially familiar with Wonderlic, but some quick Googling suggests it's an employment-specific intelligence test. Which means it's is not literally measuring merit at a job, it's categorizing people based on questions that it thinks are a proxy for job skill (unless the job literally consists of answering Wonderlic questions). People don't go around politically identifying with in discrete groups based on their intelligence, but screening out unintelligent people is still a form of grouping people up and discriminating based on something that isn't directly merit, but is strongly correlated with it.

Because measuring merit means hiring them for the job and seeing how well they do. By the time you've done this, if it turns out they have no merit, you've paid a big cost. The worst case is if "merit" doesn't just mean "will do the job well" but also includes things like "won't embezzle funds". It's hard to measure whether someone's going to embezzle funds other than by either using proxies, or waiting until they actually embezzle the funds and taking the hit.

All models are wrong, but some models are useful.

Less than you might think.

If so regarding modern cognitive models that undergird the HBD debate firms should be able to make a killing hiring math students from CSU-Monterrey and foregoing kids from Stanford, Cal Tech, and Berkeley and foregoing those poorly modeled higher salary demands.

I'm not sure what that has to do with HBD, but you can in fact do that. Except the "making a killing" part. Any major salary difference between similar employees with different educations will only last a year or two. And balanced against that are the increase search costs -- it may be you can hire randomly from Cal Tech and have a 95% chance of getting a good employee, but from CSU-Monterrey it's more like 5%. So you need to filter more, which costs you money up front. It also increases the chance of getting a dud, since your filters aren't perfect, and duds are expensive.

The 5% vs. 95% claim is self-rebutting. If the SAT is, indeed, crap, then CSU-Monterrey students are as good as Cal-Tech students AS A RULE. I didn't say you could make a killing by hiring CSU-Moneterrey grads that graduated at the top of their class and had full academic scholarships vs. random hiring at Cal Tech. I made an absolute statement that you could hire randomly from CSU-Mont and do just as well, while paying pennies on the dollar.

Funny you should mention that...

Some

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