Stating up front that I am not well informed about intelligence (IQ) testing, its accuracy, reliability, or its predictability of outcomes. This discussion is from a perspective of a data skeptic classifying different types of data. IQ is a data measurement, and I’m interested in all types of measurements. More specifically, I’m interested in assigning a property to the data so that I know how to treat it relative to other forms of data.
For this discussion, I’m building on one set of claims on IQ, and this is the set is consistent with my ill-informed understanding. This set of claims include that IQ can be measured accurately and precisely through testing, where this testing is among the best performing testing of all psychological testing. IQ testing results in consistent results for individuals through multiple tests of different types and at different ages. Also, IQ testing has a high degree of predictability of life outcomes across a wide variety of objectives such as occupational success or quality of life.
Data with the qualities described above would place it in what I called bright data. Bright data is precise data that is very close to describing something. In this case, the goodness of description is its predictive success.
However, in this particular case, it seems insufficient to classify IQ as bright data, in the same category as radar or x-ray imagery. Unlike the other bright data examples, IQ data invokes an emotional or moral reaction. We are indifferent to x-ray data, but there are a wide variety of reactions to IQ data. Some people like IQ, others hate it, some think it can be the basis for good decision making, while others think it immoral to base decisions on IQ.
I like IQ, and I unconsciously make decisions based on my IQ assessment of others. If challenged on it, I would defend the morality of using IQ to inform decisions, although I would not be eager to make that argument. To the extent that I make decisions, I want to persuade others that my decision is on solid ground. Bringing up IQ will not be very productive in the persuasion task.
I do not think I am an IQ elitist. I don’t recall ever having taken an official IQ test and I don’t know my IQ. I guess my IQ is about average, judging by the number of people who impress me as being far more intelligent. In addition, I’m generous in assigning intelligence to others, including animals (even cephalopods) and even non-living things even though IQ itself is human-specific.
The point of this discussion is that (given the above assumptions about IQ) IQ is bright data, but this is not sufficient to properly categorize this type of data. IQ is something else in addition to being bright. For the purposes of analysis, IQ also carries an emotional element. There needs to be a category to describe the fact that the bright-data of IQ is both liked and disliked among different populations or even with the same population depending on the context. The bright characteristic makes it very eligible for use in algorithms, and yet the emotional response requires us to use it cautiously or enhance it with dimmer or even dark data.
My taxonomic approach is to compare data to various adjectives describing light. Bright data is very good illumination while dark data provides no illumination, and dim data is somewhat in between. There is also spark data that distracts us, or accessory data that is never useful even if it is bright.
To capture this new dimension required for the use of IQ data, I need another approach to describe it. For now I’ll choose wavelength. Both long-wave and short-wave light can be bright without being universally useful. In addition shorter wavelength light is more energetic. Generally higher energy implies that it is more dangerous, where very high energy (very short wavelength) light required special protections to be in place.
In this context, I would describe IQ is bright but very energetic data. The implication is that it may be preferable to base a decision on less energetic data even if that data is less bright. An alternative implication is that the choice to use IQ for data is a choice to accept higher risk.
IQ data is similar to bright ultraviolet light: it can provide very good illumination (with compatible sensors) but it must be used with abundant caution.
The context of this discussion is in current political discussion in many contexts that I do not want to get into here except to say that there are policy implications when we accept IQ as reliable and predictive.
I will bring up a topic that I have been discussing recently, including the phenomena of workforce non-participation, human hives, and MGTOW.
Within the MGTOW philosophers, there is some discussion about sex-based differences between men and women. Some claim that men and women on average have about the same IQ, but the distribution is broader for men thus making more men than women at both extremes of high and low IQ. Others claim there is a difference in means.
For my discussion here, I will assume identical distributions for both men and women, as well as for any other sub-population of sufficient number to satisfy the law of large numbers. This is my protective gear for handling IQ data, I’m shifting the wavelength to something less dangerous.
Even when we consider no IQ difference for any of the large-number qualified group characteristics, there remains the problem of the standard deviation that is undeniably present.
Despite my attempt to defang IQ by assuming identical distributions, the tails of the IQ distribution remains energetic. It’s been decades since I worked with electromagnetic spectrum physics, but I recall techniques that shift wavelengths will have some spillover of the original wavelength. The technique emphasizes (or is tuned to) the center of the distribution and as a result there is more spillage at the tails that need to be filtered out. Using this analogy, my taming of IQ data by assuming identical distributions still leaves me with the energetic tails.
Any use of IQ data will seem to require accepting the implications it would have on the tails.
Modern democratic governments like USA strives to satisfy the majority. We design policies that benefit the majority of the population. Within a normal distribution, the natural way to maximize the population that benefits is to target the mean with some standard deviation large enough to include enough to be politically satisfactory.
To the extent policy is informed by IQ, there will be some point along the tails where the policy would not benefit. Those at the tails will experience some sense of alienation, of being left out. Assuming that IQ means what it claims to mean, the impact will be quite different at the opposite tails. The upper tail of high IQ will very likely deduce being neglected.
There is likely a good case for members at the tails of the IQ distribution to feel excluded from the popular culture. Some amount of labor force drop-outs may be occurring at the IQ tails that feel excluded from culture standards.
The excluded tails may partially explain the current widespread trend to drop out or drastically scale back on economic participation, particularly when it comes to committed relationships that lead to building households that drives consumerism.
At least up to now, the general wealth of the government does produce policies that should accommodate a large portion of the population, leaving a very small population at the tails that fall outside of the benefits of the policies. The number of people who are not participating is larger than what can be explained by the tails alone.
In particular, I am thinking about avoiding relationships such as the characteristics of MGTOW. Based on my incomplete observations of various authors of MGTOW topics, it seems to me that most are within one or two standard deviations of the mean of IQ. They do not appear to be at the extreme tails of the supported cultural norms.
From personal experience, I notice that within activities, there is very noticeable differences among various ranges of IQ.
Our highly technological and specialized economy has made small differences in IQ to be noticeable. For example, people with an IQ of .5-.75 standard deviations above the mean can recognize being intelligently different from people with IQ within .25 sigmas from mean, and different from people with IQ over 1 sigma over the mean.
My hypothesis is that people are detecting in-group differences along IQ lines. As they are doing so, they may begin to discriminate based on location one falls in the IQ curve. In terms of relationships, discriminating based on narrow ranges of IQ will greatly reduce the eligible population that qualify.
Here I am just imagining a scenario that compatibility depends on narrow ranges of IQ. An obvious scenario is that the couple would both have to reside in the narrow range. At least that’s the scenario that I think applies to me. I would feel someone who has similar intelligence, someone who is smart but not too smart.
Along more stereotypical scenarios may be women seeking men smarter than themselves, or men seeking less smart women. For these, I suggest that there is still a narrower range than applied in earlier times. A man may want a woman who is less smart, but not too much less.
In either case, the range of IQ is narrow enough that a suitable match is increasingly unlikely. If that range departs significantly from the local average, there may very little opportunities to encounter a suitable partner.
Here I’m talking from my perspective of never having found a partner, but I imagine the same could apply to marriages that later fail. Part of the reason for failed marriage is the unbridgeable gap in terms of preferred IQ range.
Perhaps this phenomena of increased discrimination on IQ and narrowing of range of acceptable IQ, could help to explain modern trends for marriages to fail. For marriages that do fail, the trend is for the marriages to fail earlier than before. Both may be explained by the increased priority on IQ for partner compatibility. That’s a hypothesis for a future discussion.
I recently encountered an alternative acronym, SPI, to describe the same philosophy as MGTOW. SPI stands for self-prioritized individual. I prefer this because it can apply to both men and women and I think my personal perspective could be equally valid for women. The concept is that the individual makes decisions and commitments based on only his internal goals. Clearly, SPI overlaps with the concept of selfish, but I see a SPI segment that makes decisions that are not selfish, not because he sees selflessness as a goal, but instead because his goals are not selfish. I imagine I fall into that group, but outsiders may disagree, and that’s fine by me.
To the extent that SPI may be a thing, this may be another expression of the above theory of IQ-range discrimination for successful partnerships, romantic or otherwise. If a person prioritizes using only consideration of his own goal, those goals will be guided and limited by the range of IQ he fall into. Those goals will be incomprehensible to someone in an IQ range substantially different from his. The smarter person will recognize the folly of the misconceptions. The less smart person will not be able to appreciate the validity of the good reasons for making a decision.
A partnership of two SPIs is possible or is lasting if they have compatible IQ ranges. That is very hard to find.
On the other hand, the SPI is self-motivated so that a partnership is optional. They can flourish without a relationship.
With continuing trend of automation eliminating jobs that can be performed by lower IQs, more of the employment will go to people at higher IQs. I suspect that the modern job market has similar discrimination as among couples. Jobs need people who are smart, but not too smart. Similarly, a person will last in an assignment that requires smarts but not too much smarts.
Within my experience, I have noticed the narrow range of IQ describes my jobs: the job needed smart but not too smart and I was smart enough but not overly smart. The jobs got done.
But also within my job, I have worked with other roles that had different IQ ranges and those jobs also were a good mutual fit. My observation, though, is that it seems increasingly difficult to bond as a team with people who should work as a team. The difficulty could come from incompatible IQ. As noted at the start, this is just a weekend armchair speculation, but it seems at least plausible.
Despite having a job that is well suited for my IQ, I have difficulty working with people I need to work with. That difficulty seems to be related to differences in ability to communicate abstract ideas, and the frustration goes in both directions. Neither of us are able to fully grasp the depth or limitation of the ideas expressed by the other.
However, the people I work with generally have to have above average IQ to qualify for the job, and most clearly have high IQ. I suspect what I’m experiencing is a discrimination of narrow ranges of IQ. A smart person with an IQ of 120 has difficulty working with someone like myself with an IQ closer to 110 or like others with an IQ above 130.
Fine-resolution IQ discrimination must result in major implications for the culture, if that discrimination is occurring. I believe it is occurring with the emerging social specialization of an emerging human hive.
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