Are smart people's personal experiences biased against general intelligence?

There's a pretty famous paper showing that math and verbal intelligence trade off against each other. It was done on students at a mid tier college; the actual cause was that students who were good at both went to a better school. So the effect definitely exists.

That said, I'm not convinced it's the dominant cause here. Being known for being aggressive and mean while blocking people who politely disagree pollutes your data in all kinds of ways.

Suppose I am a smart person and I prefer to associate with smart people. As a result of this, I see evidence everywhere that being smart is anticorrelated with all the other things I care about. (And indeed that they are all anticorrelated with one another.) As a result of this, I come to have a "bias against general intelligence".

What happens then? Aren't I, with my newly-formed anti-g bias, likely to stop preferring to associate with smart people? And won't this make those anticorrelations stop appearing?

First, I don't necessarily know that this bias is strong enough to counteract other biases like self-serving biases, or to counteract smarter people's better ability to understand the truth.

But secondly, I said that it creates the illusion of a tradeoff between g and good things. But g still has things that gives it advantages in the first place, since it'd still generally correlate to smart ideas outside of whatever thing one is valuing.

But also, I don't know that people are being deliberate in associating with similarly intelligent people. It might also have happened as a result of stratification by job, interests, class, politics, etc.. Some people I know who have better experience with how social networks form across society claim that this is more what tends to happen, though I don't know what they are basing it on.

First, there's nothing very special about g here. If I tend to associate with smart people, socialists and sadomasochists[1], doesn't this argument suggest that as well as starting to think that smart people tend to be sexually repressed fascists I should also start to think that socialists are sexually repressed and stupid, and that sadomasochists are stupid fascists? Shouldn't this mechanism lead to a general disenchantment with all the characteristics one favours in one's associates?

[1] Characteristics chosen for the alliteration and for being groups that a person might in fact tend to associate with. They aren't a very good match for my actual social circles.

There's the special thing that there is assortment on g. That special thing also applies to your examples, but it doesn't apply to most variables.

I think I can reconcile your experiences with Taleb's (and expand out the theory that tailcalled put forth). The crux of the extension is that relationships are two-way streets and almost everyone wants to spend time with people who are better than them in the domain of interest.

The consequence of this is that most people are equally constrained by who they want in their social circle and also the who wants them in their social circle. While most people would like to hang out with the super smart or super domain-competent (which would induce the negative correlation), those people are busy because everyone wants to hang out with them, and so they hang out with their neighbor instead. Since Taleb is extraordinary, almost everyone wants to hang out with him, so the distribution of people he hangs out with is equal to his preference criteria. For most people near the center of the cluster, the bidirectionality of relationships leads them to social circles that are, well, circle shaped. Taleb, in a fashion true to himself, is dealing with the tail end of a multivariate distribution.

Since normal people are dealing with social Circles instead of social Tails, they do not experience the negative correlation that Taleb does.

The standard example I've always seen for the "collider bias" is that we have a bunch of restaurants in our hypothetical city, and it seems like the better their food, the worse their drinks and visa-versa. This is (supposed to be) because places with bad food and drinks go out of business and there is a cap on effort that can be applied to food or drinks.

How would the self-defeating thing play in here? I don't see yet why it shouldn't, but I also don't recognize a way for it to happen, either. Could you walk me through it?

You can't stop hanging out with yourself, and "yourself" is the most important biasing sample.

What this suggests is that the oft touted connection between altruistic behavior and intelligence may have a root in self preservation. Intelligent people are much more capable of serious undetected dishonesty and so have to make obvious sacrifices against violence, crime, impoliteness, etc. to make up for it.

"The Tails Coming Apart As Metaphor For Life" is a classic Slate Star Codex post about it, based on the 2014 LessWrong post "Why the tails come apart" [LW · GW]. Both of them use the phrasing "tails coming apart" to refer to the bias, since in the graph it seems like there are two separate "tails" of people while they are both a subset of the larger circular group of people.

Interestingly, neither posts refer to the term "collider bias", but they definitely all are talking about the same concept.

https://en.wikipedia.org/wiki/Berkson%27s_paradox

I also liked this numberphile video about it: Link

I’d assume that most things people value are associated with IQ.

I'm not sure that's true. E.g. it doesn't seem to apply to personality or politics. (Or well, politics is associated, but people can value eother side of politics, and so it can just as well be negatively correlated as positively correlated.)

If that association is strong enough, then I believe it still shows up as a positive correlation when you exclude the people who don’t make the cut.

Yes, but collider bias can lead to a surprisingly strong negative bias in the correlation. E.g. in my simulation, g and black swan awareness were initially positively correlated.

So ... smart people are worse than average at the task of evaluating whether or not smart people are worse than average at some generic task which requires intellectual labor to perform, and in fact smart people should be expected to be better than average at some generic task which requires intellectual labor to perform?

Isn't the task of evaluating whether or not smart people are worse than average at some generic task which requires intellectual labor to perform, itself a task which requires intellectual labor to perform?  So shouldn't we expect them to be better at it?

I mentioned this as a bias that a priori very much seems like it should exist. This does not mean smart people can't get the right answer anyway, by using their superior skills. (Or because they have other biases in favor of intelligence, e.g. self-serving biases.) Maybe they can, I wouldn't necessarily make strong predictions about it.

Consider the hypothetical collider from the perspective of somebody in the middle; people in the upper right quadrant have cut them off, and they have cut people in the lower left quadrant.  That is, they should observe exactly the same phenomenon in the people they know.  Likewise, a below average person.  Thus, the hypothetical collider should lead every single person to observe the same inverted relationship between IQ and black swan awareness; the effect isn't limited to the upper right quadrant.  That is, if smart people are more likely to believe IQ isn't particularly important than less-smart people, this belief cannot arise from the hypothetical collider model, which predicts the same beliefs among all groups of people.

I think it depends on the selection model.

The simulation in the main post assumed a selection model of black_swan_awareness + g > 3. If we instead change that to black_swan_awareness - g**2 * 0.5 > 1, we get the following:

This seems to exhibit a positive correlation.

For convenience, here's the simulation code in case you want to play around with it:

import numpy as np
import matplotlib.pyplot as plt

N = 10000
g = np.random.normal(0, 1, N)
black_swan_awareness = np.random.normal(0, np.sqrt(1-0.3**2), N) + 0.3 * g
selected = black_swan_awareness - g**2 * 0.5 > 1 #g + black_swan_awareness > 3
iq = g * 15 + 100

plt.scatter(iq[~selected], black_swan_awareness[~selected], s=0.5, label="People Taleb fanboys avoid")
plt.scatter(iq[selected], black_swan_awareness[selected], s=0.5, label="People Taleb fanboys hangs out with")
plt.legend()
plt.xlabel("IQ")
plt.ylabel("black swan awareness")
plt.title("Hypothetical collider")
plt.show()

↑ comment by ACrackedPot · 2022-04-22T14:29:54.822Z · LW(p) · GW(p)

Yes, it does depend on the selection model; my point was that the selection model you were using made the same predictions for everybody, not just Taleb.  And yes, changing the selection model changes the results.

However, in both cases, you've chosen the selection model that supports your conclusions, whether intentionally or accidentally; in the post, you use a selection model that suggests Taleb would see a negative association.  Here, in response to my observation that that selection model predicts -everybody- would see a negative association, you've responded with what amounts to an implication that the selection model everybody else uses produces a positive association.  I observe that, additionally, you've changed the labeling to imply that this selection model doesn't apply to Taleb, and "smart people" generally, but rather their fanboys.

However, if Taleb used this selection model as well, the argument presented in the main post, based on the selection model, collapses.

Do you have an argument, or evidence, for why Taleb's selection model should be the chosen selection model, and for why people who aren't Taleb should use this selection model instead?

Replies from: tailcalled

↑ comment by tailcalled · 2022-04-22T17:21:35.378Z · LW(p) · GW(p)

However, if Taleb used this selection model as well, the argument presented in the main post, based on the selection model, collapses.

No, if I use this modified selection model for Taleb, the argument survives. For instance, suppose he is 140 IQ - 2.67 sigma above average in g. That should mean that his selection expression should be black_swan_awareness - (g-2.67)**2 * 0.5 > 1. Putting this into the simulation gives the following results:

Replies from: ACrackedPot

↑ comment by ACrackedPot · 2022-04-22T18:46:22.153Z · LW(p) · GW(p)

You have a simplification in your "black swan awareness" column which I don't think it is appropriate to carry over; in particular you'd need to rewrite the equation entirely to deal with an anti-Taleb, who doesn't believe in black swans at all.  (It also needs to deal with the issue of repricocity; if somebody doesn't hang out with you, you can't hang out with them.)

You probably end up with a circle, the size of which determines what trends Taleb will notice; for the size of the apparent circle used for the fan, I think Taleb will notice a slight downward trend with 100-120 IQ people, followed by a general upward trend - so being slightly smart would be negatively correlated, but being very smart would be positively correlated.  Note that the absolute smartest people - off on the far right of the distribution - will observe a positive correlation, albeit a weaker one.  The people absolutely most into black swan awareness - generally at the top - likewise won't tend to notice any strong trends, but it will tend to be a weaker positive correlation.  The people who are both very into black swan and awareness, and also smart, will notice a slight downward correlation, but not that strong.  People who are unusually black swan un-aware, and higher-but-not-highest IQ, whatever that means, will instead notice an upward correlation.

The net effect is that a randomly chosen "smart person" will notice a slight upward correlation.

Replies from: tailcalled

↑ comment by tailcalled · 2022-04-22T18:57:04.276Z · LW(p) · GW(p)

I'm not sure what you are saying, could you create a simulation or something?

Replies from: Daniel V

↑ comment by Daniel V · 2022-04-24T19:05:52.033Z · LW(p) · GW(p)

Selection-induced correlation depends on the selection model used. It is valuable to point out that tailcalled implicitly assumes a specific selection model to generate a charitable interpretation of Taleb. But proposing more complex (/ less plausible for someone to employ in their life) models instead is not likely to yield a more believable result.

Replies from: tailcalled

↑ comment by tailcalled · 2022-04-24T19:16:51.757Z · LW(p) · GW(p)

Did you mean to write your comment in response to ACrackedPot, rather than to me?

Basically, the idea is that e.g. if you are smarter at solving math tests where you have to give the right answer, then that will make you worse at e.g. solving math "tests" where you have to give the wrong answer. So for any task where intelligence helps, there is an equal and opposite task where intelligence hurts.

As far as I know, there's no correlation between g and niceness/forgiveness/etc.. Just bringing up the most immediate study on the topic I can think of (not necessarily the best), this study finds a correlation of approximately zero (-0.06) between being nice (Agreeableness) and IQ (ICAR):

I could make up various possible explanations for why you'd see this effect, but I don't know enough about your life to know which ones are the most accurate.

Perhaps his claim is that he's not very smart in the way that IQ tests measure?