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Ideas Sleep Furiously

MAY CONTAIN: Social science, genetics, philosophy, psychology, chronicles of progress & decay

Written by Michael Fritzell.

Geopolitics expert Peter Zeihan has a colourful personality. But during his recent appearance on the Joe Rogan show, he made several arguments that I found hard to swallow. 

For example, Zeihan claimed that China will have around 10 years before its economy collapses. Weak demographics, rising labor costs, and increased isolation from the rest of the world all form part of the picture. However, Zeihan also discussed a potential invasion of Taiwan. In such a scenario, US sanctions would then hurt China to such an extent that they would cause de-industrialization and famine. 

Further, Zeihan was critical of China’s handling of COVID-19. He pointed out that the population is now vulnerable due to its lack of natural immunity, widespread obesity, and the weak efficacy of the country’s homegrown vaccines. 

As someone who has made a living analyzing Chinese equities and China’s economy for the past 15 years, I find Zeihan’s remarks to be, well, off the mark. They don’t paint a realistic picture of the country and where it’s going.

To start, I don’t believe that weak demographics will necessarily lead to a “collapse” of any kind. All that will happen is that economic growth will decelerate as the working-age population shrinks. But it’s going to be a gradual, drawn-out process. And on a per capita basis, the country may well become richer. 

What Zeihan failed to mention is the fact that China’s retirement age is incredibly low from an international perspective – 60 for men and only 55 for women. I would wager that China’s retirement age is going to increase over the next few decades, partly counteracting the negative effect of a slow decline in the working-age population. 

A dependency ratio is an age-population ratio of those typically not in the labor force and those typically in the labor force. It measures the pressure on the productive population. It’s true that China’s dependency ratio is rising. This means that the aggregate tax burden on the working-age population will go up. A slightly larger piece of the pie will need to go to pensions. But the Chinese tend to be resilient. Don’t forget the older generation lived through the tumultuous years of the cultural revolution in the 1960s and 70s. So to talk of an “economic collapse” is misleading – there’s nothing suggesting that the economy will reach any kind of tipping point within the next 10 years. 

I agree with Zeihan that President Xi is undoing much of what caused the Chinese economic miracle in the first place. For example, since 2012, he has pushed for higher minimum wages and convinced multinationals to move their factories inland. Such efforts may have helped with wealth redistribution within China, but it has also caused Chinese manufacturing to become less competitive. That’s certainly an issue. 

I also think that the country’s aggressive foreign policy under Xi has caused irreparable damage to its reputation. Skirmishes on the border with India, threats to Taiwan, the backlash against Korean products during the THAAD crisis, and hostile behavior in the East- and South China Seas have not won the country any friends. 

And increased concerns about forced technology transfers, subsidies to state-owned enterprises, capital controls, and import tariffs have caused many multinationals to rethink their commitment to Chinese manufacturing. The introduction of so-called Communist Party committees in the Chinese subsidiaries of such multinationals has also raised eyebrows. The Chinese Communist Party is definitely starting to assert greater control over the economy. 

Moreover, the ongoing shift away from Chinese manufacturing will take time. China’s infrastructure remains top-notch. Chinese workers are generally educated and hard-working. Various industry clusters have formed around the Yangtze- and Pearl River Deltas and those will be difficult to displace. The reality is that supply chains reliant on Chinese manufacturing have become well-oiled machines. Outsourcing production to smaller countries in Southeast Asia will be difficult, if not impossible. The shift will take decades, perhaps longer.

Based on what we know about Xi, he seems primarily concerned with power and influence. He spent years consolidating that power within the communist party and now he’s trying to help the party remain relevant in a new era. Read his speeches before he became General Secretary in 2012 and you’ll find that almost all of them were about maintaining party supremacy. 

The big question is whether Xi will become less aggressive now that he became the leader for life after the Communist Party Congress in October 2022. Is it possible that his antagonistic behavior towards foreign nations was simply a way to shift blame for whatever discontent was building within the country? It wouldn’t be the first time that nationalism has been used to rally support for whatever the person at the top believes in. 

However, I am not as confident as Peter Zeihan about Xi’s willingness to invade Taiwan. Both Japan and South Korea have now given implicit commitments to defend the island in the case of an invasion. The United States is unlikely to stay idle in such a scenario. 

And if the People’s Liberation Army fails in its attempt to take control of the island, Xi’s head will be on the line. A failed invasion would be akin to political suicide. He must look at Putin’s failures in Ukraine and wonder whether a similar campaign over Taiwan would ultimately be worth it. 

I want to offer an alternative future to Zeihan’s. It’s possible that China will not isolate itself from the rest of the world in the way that he thinks. It may simply reduce its reliance on US and European customers for its products and strengthen its bonds with other emerging markets such as Russia, Iran, and Pakistan. 

In fact, China is already the biggest trading partner for most of the world outside of North America and Europe. The Chinese Communist Party clearly wants a world where its allies sell raw materials to China, while China sells manufactured goods back to them.

Moreover, the CCP’s bargaining power will probably increase with the widespread use of communication infrastructure built by companies such as Huawei and ZTE. If any data is stored within mainland China, that data must be presented to the government upon request. That’s stated clearly in China’s cyber security law. Countries relying on Chinese communication infrastructure are, therefore, likely to be political allies of China. 

Countries that have banned Huawei telecom infrastructure. Source: CFR

Further, placing People’s Liberation Army officers in those countries will ensure stable trade, with less reliance on US Navy support. China’s discussions with its new allies are now done under the so-called Global Security Initiative framework set up in 2022. 

Now that the world is being divided into two camps, we could see a return to a cold war, with an iron curtain rising between North America/Europe and the rest of the world. American and European manufacturing will have to be reshored. The majority of world GDP will remain in the “Western” camp, while the majority of the population will belong to countries allied with China. 

Current global alignment towards the US and China. Source: Capital Economics

Whether China’s economy can thrive in this new world order is unclear. Recent crackdowns on the tech, property and tuition sectors are worrisome. Communist Party committees have been introduced in many private enterprises. However, China still enjoys a current account surplus and does not rely on external debt. Again, this makes it odd to speak of an imminent collapse when there is no obvious catalyst for such a collapse to take place. 

Zeihan didn’t mention China’s housing market, but that’s unlikely to be a problem either. We all know that there has been excess construction of housing in China. Home prices are now high in most tier-1 cities. 

But construction activity has already come off sharply. In my view, that’s a positive development. The excess construction problem has essentially been dealt with. While it may not be positive that private developers are going bankrupt, employing millions of workers to build housing nobody needs is hardly productive either. 

When it comes to COVID-19, I’m not as bearish as Zeihan. The Omicron variant leads to mild symptoms, even for individuals who haven’t been vaccinated. Yes, Sinovac vaccines are less effective than western mRNA vaccines, but the differences aren’t as large as commonly made out to be. 

Further, China’s obesity rates aren’t nearly as high as those in the United States. Zeihan’s bizarre claim about obesity makes me wonder whether he has ever set a foot in the country! And despite its weak healthcare system and low GDP per capita, China’s life expectancy is actually higher than that in the United States. That’s a remarkable achievement. 

Now that COVID-19 is spreading fast in China, herd immunity will be building as well. Once the population has become exposed, it will be easier to remove restrictions and look forward to a return to normal. 

The reality is that Chinese households have been saving money for the past years. They’re in much better shape than before. Now that most COVID-19 restrictions have gone away and the borders have reopened, expect a forceful recovery in China’s consumer demand. This recovery could take place as soon as the first quarter of 2023. 

Longer-term, it does look like the world is becoming more divided. China’s President Xi is a socialist and seeks to reassert control over the nation’s private enterprises. I believe the economy will slowly become less dynamic and relevant as supply chains move out of the country. 

Given this nuanced picture, Zeihan’s talk of an imminent collapse seems misplaced. Especially now that China’s economy is finally regaining its footing after three years of COVID chaos. Once the economic recovery picks up throughout 2023, Peter Zeihan will probably look rather foolish.

Michael Fritzell is a full-time writer focusing on stocks in Asia. He is the author of Asian Century Stocks, a Substack focusing on Asian equities.

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Ideas Sleep Furiously

MAY CONTAIN: Social science, genetics, philosophy, psychology, chronicles of progress & decay

If you enjoyed this, you might want to check out Michael’s other article for ISF!

Ideas Sleep Furiously

Inflation is a tax that redistributes wealth in a perverse fashion

Written by Michael Fritzell. A few weeks ago, I wrote a post about Weimar Germany’s hyperinflation in 1919-23 and what lessons investors can draw from that experience. The key takeaway from my research is that inflation is a hidden tax that redistributes wealth. But necessarily from poor to rich. It redistributes wealth in a perverse fashion…

Read more

6 months ago · 9 likes · 2 comments

Geopolitics expert Peter Zeihan has a colourful personality. But during his recent appearance on the Joe Rogan show, he made several arguments that I found hard to swallow. 

For example, Zeihan claimed that China will have around 10 years before its economy collapses. Weak demographics, rising labor costs, and increased isolat…

Read more

In 2001, rockstar academic Robert Putnam produced the Social Capital Community Benchmark Survey. It was a giant survey of 26,700 Americans, asking people a simple question, “How much you can trust people in your neighborhood?” Respondents could respond “a lot”, “some”, “only a little” or “not at all”. Unfortunately for Dr Putnam, upon analysing the data he found an awkward result. The more racially diverse areas had lower levels of social trust. Scared of his findings, Putnam delayed publishing them for six years. 

The story has become something of a popular parable among the right-wing. The superficial lesson is that diversity and immigration are bad for the cultural fabric, community and sense of identity — all those nice things which are neatly summarised in how much we trust our neighbours. And despite the lesson being so painstakingly clear, liberals and academics can’t help but bury their heads in the sand! 

Of course, there’s more nuance to the world than present in allegory. In reality, it’s not clear that diversity per se reduces social trust. All we actually know is that living around certain races is associated with low social trust. In US countries, having more Asians or Latinos instead of Whites has no association with social trust. Although having more African Americans is strongly associated with lower trust. When you control for the ethnic proportions of all the races, indexes of diversity or homogeneity typically only have a very small relationship with social trust. Emil Kirkegaard provides a useful analysis and literature review here. 

Generally, the relationship between diversity itself and social trust is small after you control for other factors.

We should not be sure that associations between ethnicity and trust are completely causal. Some ethnicities may be more likely to move to areas of high trust. Ethnic groups that are economically well-off, such as White or East Asians, can better afford to live in nice communities.

Nor is it clear whether trust is a good or merely a bellwether for other good things. Is trust a necessary ingredient to a functioning society, or do honest, hardworking people produce prosperous communities where we can all trust each other? If you search the academic literature you’ll find hundreds of papers claiming the importance of social trust. It is good for reducing crime, for effective government, entrepreneurship, health, happiness and, of course, GDP! 

I would guess that social trust is mostly a by-product of positive things. Do people become criminals because they distrust others? Maybe. Would you distrust your community if you kept being mugged? Most certainly. Across regions of countries, social trust correlates with the average intelligence of regions at around 0.7. When you control for intelligence, social trust seems to have little relationship with positive outcomes. Perhaps trust in our society just reflects its competency.

Knowing the debates around social trust, I was very excited to see a new report, “Good Neighbours”, examining social trust in my home country, England. The conservative think tank, Onward, surveyed 42,000 people to ask them whether they trust others. This is near twice the size of Putnam’s 2001 survey and for a country only a fifth of the size, allowing for accurate and fine-grained analysis. Using the survey, they calculate net trust for regions called ‘Middle layer Super Output Areas’ (MSOAs), by taking the proportion of people who say they trust others and then subtracting the number of people who say they distrust others.

The report carries some hefty prestige. The last director of the think tank left three months ago to be deputy Chief of Staff at Downing Street. And before his directorship, he was an advisor to Prime Minister Theresa May. One of the authors of the report, Luke Stanley, is an advisor to Lord Hague, an ex-leader of the Conservative party.

Most importantly, this research is original. As far as I am aware, this is the first research into social trust at a local level in the United Kingdom. So what did Onward find out about social trust? Do the prior findings replicate in England? 

As usual, they find social trust correlates with a whole host of positive outcomes. Lower deprivation, lower crime, fewer welfare claimants, higher voter participation and more Remain-voting in the 2016 EU referendum. Whatever metric you can think of, social trust is correlated with it. 

And as usual, the report often blindly assumes that trust is causing positive outcomes rather than being a by-product of them. 

But what about Putnam’s old controversy? What relationship does trust have with immigration, race and diversity? If you search the report for the phrase “ethnic”, you’ll find it mentioned once in the very last paragraph before the conclusion:

“Another area where there is a lack of correlation is ethnic diversity. Looking at the data, there appears to be no link between the proportion of White Britons in a local authority and levels of trust.”

Despite all of the academic literature showing a relationship between ethnicity and trust, Onward says there’s nothing to see here! They expand on this claim ever-so-slightly in a footnote. It states that the proportion of White Britons in local authorities only explains 2.7% of the variation in social trust between local authorities. But that is not nothing. It implies that a standard deviation increase in the proportion of White Britons is associated with a 0.16 standard deviation increase in social trust. With 333 local authorities, it is almost certainly statistically significant too. 

Why report the relationship for local authorities? Local authorities have populations in the hundreds of thousands, but the people who live closer to you probably matter much more. The whole point of Onward’s massive survey was to examine social trust at the “hyper-local” level of MSOAs (average population of 7,800). Why shift the analysis to the local authorities just for this particular question?

To obscure the question further, having fewer White Britons in an area might imply having more White Europeans — educated immigrants who easily assimilate. How does Third World immigration affect social trust? When we speak of diversity, we normally mean fewer white people.

Onward only briefly mentions this important topic at the last moment of the report. By switching methodologies, they tried to avoid the elephant in the room. Thankfully, however, Onward has publicly provided the social trust data for the MSOAs. So I thought I would do the analysis myself…

The proportion of residents who are White does indeed correlate with the proportion of residents who say they trust others. However, the effect size seems rather small. Imagine you move from an area with no Whites to an area only with White people. On average, you would expect an additional 7% of the population to say they trust others. 

Let’s now study ethnic diversity more broadly. In the below table are some regression models estimating the relationship between the ethnic proportions of MSOAs and the proportion of people in the area who say they “always” or “usually” trust others.

The models tell a complex story. In model 2 we find that turning 1% of an MSOA from White to East Asian is associated with an additional 0.31% of people saying they trust others. By contrast, having more South Asians or Blacks is associated with lower social trust. 

I also look at the Simpson Index for each MSOA. This is the probability of a randomly selected two people being of the same ethnicity. The index captures the level of ethnic homogeneity in an area. Whilst the effect is statistically significant, its magnitude is small. At most being a standard deviation higher in ethnic homogeneity, that is being in the 84th percentile for homogeneity, is only associated with an additional 2% of people saying they trust others.

Whilst the individual effect sizes of each ethnicity may look small, using all ethnic information can account for the majority of variation in social trust. So far I have only presented the effects of broader ethnic groups, such as “Whites”. But the White group is made up of different groups, for example, “English” and “Roma, Gypsy” who have very different lifestyles. If we account for the effect of all 19 ethnic categories the Census provides, then race explains 61% of the variation in social trust across regions. 

Onward’s analysis of social trust was truly turned on its head. Rather than there being “no link between” ethnic diversity and social trust, ethnic diversity is the majority of the story to social trust. 

Despite Onward proclaiming a desire to solve social trust, they managed to miss a prime suspect. However, there is one more fact that makes this situation all the more incredible — how the social trust data was made. Even with 42,000 people surveyed, it is difficult to accurately measure social trust for the 7,200 MSOAs in England. So Onward hired a consultancy, Focaldata, to estimate social trust based on the respondents' answers and some other variables. In the appendix, Onward’s report explains that to estimate social trust they used a

“Regression [which] included individual and area characteristics, including: overcrowding, ethnicity, health, qualification levels, housing tenure, social grade, age and gender”

They guessed the level of social trust using ethnicity! It beggars belief to suppose that Onward did not know that ethnicity correlated with social trust after they managed to estimate social trust with ethnic data.

A quarter of a century on from the Putnam parable, a lot has stayed the same. Instead of honest investigation, many researchers prefer to bury their heads in the sand. Ironically, ‘Onward’ is rather backward. 

Although it is not certain that “demography is destiny”, the ability of race to account for most of the variation in social trust certainly implies it. Whatever the correct response, more integration or less immigration, we can only tackle the problem if we are honest about it. Alas, I do not trust the experts to be honest anytime soon.

Supplementary Materials are available here.

George Francis is a writer and social scientist interested in intelligence and economics. If you’d like a deep data dives, you should check out his Substack, Anglo Reaction. Find him on Twitter here.

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Ideas Sleep Furiously

MAY CONTAIN: Social science, genetics, philosophy, psychology, chronicles of progress & decay

The 1:1 ratio

A statistical concept you will need to understand for the following research: in a closed system of heterosexual relationships we expect men and women to report the same number of partners. Every reported pairing by a man needs a report by a woman. If a man says he had sex with ten people, we need ten sexual partners accounted for on the female side. For example, one woman with ten partners, ten women with one partner, or two women with five partners.

Yet, we consistently don’t see this. On average, men report having more partners. We are looking for a ratio of 1:1. We don’t find it.

If the word average immediately caught your attention then you are on the right track. When we look at the data on sexual partner count, men and women have very similar distributions and numbers of sexual partners, despite having different means.

Social desirability bias

Social desirability bias is the tendency to self-report in a socially desirable way on surveys or questionnaires. For example, women may report fewer sexual partners because promiscuity has a greater social stigma for women. When I began researching this topic I expected I would need to address social desirability bias a great deal.

It turned out that social desirability bias was less relevant than I expected. Most men and women were already reporting very similar numbers of sexual partners. The large average difference between men and women was not bimodal. The distribution does not look like this:

The research included will nonetheless address social desirability bias. The reason I say social desirability bias was less relevant than I expected is that we don’t see a biased distribution like the one above. Instead, we see a large degree of male-female agreement for most survey respondents.

The Morris Study

A consistent observation across the literature is that men report having more sexual partners on average than women do. Nnko (2002) described this as “swaggering males, secretive females.” This is a problem in sexual behavior research. We should expect a 1:1 ratio for heterosexual pairing. So is everyone really lying?

What the data show is that, at least, not everyone is lying. An average is just an average. It is not a complete picture. Averages neither tell us magnitude (to what degree men and women report differently) nor distribution (which men and women report differently). This is where we find our inaccurate reporters.

In a demonstration of how statistical tail effects (outliers) skew averages, Morris (1993) analyzed GSS data for lifetime and yearly sexual partners. 90% of male and female respondents — all respondents who reported having fewer than 20 lifetime partners — reported having within one sexual partner of each other. The ratio was 1 to 1.3. By excluding outliers, the male-to-female report discrepancy was reduced from 600% to 20%.

Further, this was a ratio consistently found year-to-year in the GSS data. It is only when self-reporting high levels of partners that a large difference emerged. See Figure 1 from the Morris paper. Men, but not women, report high levels of partners in the double digits. A minority of men reporting high numbers creates a long right tail that skews the average for men.

Figure 2 is the male and female variance for self-reporting of lifetime partners. On the left, men and women who report fewer than 20 lifetime partners. On the right, the whole sample. As soon as you remove the ~10% of men and women who report over 20 lifetime partners the variance approaches zero. Most men and women do not report different numbers of partners.

This was in 1993, but you can also see from the recent GSS data on how many sexual partners people had in 2021 that the pattern has remained. In fact this pattern has been consistent across sexual report surveys over the years. We see long right tails in the distributions for lifetime partners.

The Wiederman Study

Wiederman (1997) tested a number of alternate explanations for the male-to-female report discrepancy. One explanation was that men and women interpret “sexual partners” differently. However, upon examination men and women did not report a greater degree of non-inclusion of casual sexual partners. Removing those individuals did not decrease the report discrepancy.

Self-rated honesty did not predict the discrepancy and removing individuals who reported lower honesty did not reduce the discrepancy.

Accounting for prostitution or sex work only slightly lowered the report ratio. A small part of the difference could be accounted for by men who visit sex workers and women who are sex workers. As a very small population, female sex workers may be underreported even in large sample data (Brewer et al. 2000). However, as this was only a small part it does not explain the ratio.

What really reduced the ratio was self-report accuracy. By removing people who reported a low level of accuracy in reporting their past number of sexual partners, the discrepancy disappeared. The remaining men and women reported sexual partners at the same rate. Here we are close to our 1:1 ratio.

Further, twice as many men as women reported a low level of accuracy and a high number of partners. Removing those men from the dataset eliminated the gender discrepancy, indicating a gendered direction of the relationship: men with poor recall, rather than women, accounted for the gender discrepancy.

Wiederman’s research also validated the Morris (1993) data. Low recall participants were mostly men reporting high numbers of partners. By removing men who admitted having poor recall Wiederman also removed the right tail of the distribution that skewed the mean partner count for men.

In a replication within the same paper, Wiederman examined GSS sexual behavior reports. A pattern emerged for the preference of round numbers such as “10.” At greater than 15 partners, 82% of respondents gave a figure that ended in a 0 or a 5. Every number over 36 that was reported in the entire dataset ended in a 0 (“50,” “60,” “70,” etc.).

The pattern for selecting round numbers shows inaccurate reporting. We don’t have sex with people in multiples of five or ten. The pattern shows guessing, rounding, or inventing whole numbers in the subset of people who report high partner counts. Instead of “I know I have had sex with exactly thirty-six people in my lifetime” think more “yeah maybe I had sex with 40 or so people.”

Revisit Figure 1 from the Morris paper. Again we see the same rounding effect and preference for multiples found in Wiederman (1997). Virtually all of the men with 20+ partners reported 25, 30, 40, 50, or 100.

The discrepancy also increased linearly with age (table below). As people got older, and/or accumulated more partners, their reporting accuracy decreased.

The above-mentioned studies also confirmed previous research by Wadsworth et al (1993), which found that male-to-female report ratios decreased with the time between the sexual act and reporting. In a large nationally representative survey, lifetime partner count for men was 9.9, while for women it was 3.4. This dropped to 2.6 and 1.5 for men and women respectively over the last five years. In the last two years, it dropped to 1.6 and 1.1 for men and women, while reporting over the last year decreased further to a ratio of 1.2 to 1 for men and women. Again we approach the 1:1 ratio that indicates accurate reporting. See the table below:

Dare & Cleland (1994) reproduced this table as such:

The finding that men and women report close ratios of sexual partners in more recent 1-5 year periods has been consistently replicated on a yearly basis since these studies in the early 1990s began (Brewer et al., 2000; Wadsworth et al, 1996).

When interpreting average overall self-reports of partner count, the implication here is that more recent periods are more trustworthy than less recent periods. People may recall their exact number of partners more accurately. Having naturally fewer partners within a one-year period may also generate less motive to “swagger” and exaggerate partner counts. Even up to five years, the ratio remains fairly close. It is only when we examine lifetime partners that we see a big divergence between how men and women report.

Brown & Sinclair

Here again, we see a tail-end male preference for large, round numbers in a paper by Brown and Sinclair (1999):

This chart begins at eight partners. Brown and Sinclair began this chart at the tail of the distribution, where report ratios begin to vary, “because men and women who have had relatively few partners provide similar estimates and use similar response strategies.” Only 10% of the total sample reported eight or more lifetime partners.

The bars are marked to show the estimation strategy used. Coinciding with the large, round numbers are “Rough Approximation” and “Retrieved Tally.” 29.4% of male responses were rough estimates while only 4.4% of female responses were rough estimates. Women were more likely to report an enumeration strategy, or to be able to specifically account for every person they slept with (48.9% women, 29.4% men).

Additionally, there was not a statistically significant difference between men and women who used the same estimation strategy. When looking at only the past year of sexual history, men and women also did not report statistically significant numbers of partners.

Brown and Sinclair referenced a series of papers that this finding replicated: ACSF Investigators, 1992; Johnson et al., 1992; Laumann et al., 1994; Morris, 1993; Smith, 1992. This finding has also been replicated more recently in Kirstin et al., 2019 and Bogart et al. 2007.

The Turchik & Garske Study

The desire for social desirability is an expression of personality. As an expression of personality, the need for social desirability varies from person to person. Some people may have a high need for social desirability. Others may care less. We can measure the need for social desirability — so what if we just screened for social desirability and removed people who scored highly?

Turchik & Garske (2009) used the Marlowe-Crowne Social Desirability Scale (MCSD; Crowne & Marlowe, 1960) and the Sexual Self-Disclosure Scale (SSDS; Catania, McDermott, & Pollack, 1986). These are psychometric tests with high test-retest reliability (they replicate results), inter-item validity (they are internally consistent), and construct validity (they predict real-world outcomes consistent with what they are designed to test). Additionally, Turchik & Garske measured a number of specific sexual behaviors as well as sexual partner count.

First, the distribution of sexual partners mirrors what was found in the previous two studies, as well as the GSS 2021 data that I ran. 21.3% of total responses reported zero sexual partners, 47.1% reported 1-2 sexual partners, 17.7% reported 3-4 partners, 9.4% reported 5-9 and the remaining 4.5% reported greater than 10 partners.

This is the distribution we consistently see replicated across all sexual behavior research: most people cluster with few partners on the left tail.

Neither the Social Desirability Scale (a measurement of social desirability bias) nor the Sexual Self-Disclosure Scale (fear of having your sexual behavior disclosed) predicted reports of risky sexual behavior. The SSDS weakly predicted (-.13) impulsive sexual behavior.

This research does not support the claim that people (women) underreport sexual behavior — not even risky and taboo sexual behavior — out of social desirability or a fear of having their sexual behavior disclosed.

Across multiple items, men and women reported risky and taboo sexual behaviors at nearly identical rates. Below is my chart of the Turchik & Garske data from Table 2:

Here are the same rates except only for the men or women who reported 10+ partners:

Again we see the pattern that we saw in the previous papers. Men and women who report low partner counts report very similar ratios of sexual behavior. It is only when we see high partner reporting that we begin to see a difference between men and women. As fewer than 5% of men and women fall into this category (in this sample), we should expect generally accurate self-reporting of sexual behavior from most people.

This was a large sample of undergraduate students with a mean age of 19. The previous studies were large nationally representative samples. That we see a similar male-to-female self-report ratio in a young college sample may again show that young Westerners do not fear honestly disclosing sexual behavior in anonymous research surveys. This is also an example of when nonrepresentative convenience samples align well with representative samples.

It has been posited as an explanation that men and women don’t “count” sexual acts the same. For example, a man may count oral sex as one partner while a woman may not. The Turchik & Garske data do not show this. As you can see from the first Turchik & Garske chart, men and women report specific sexual acts at very similar rates. The Sexual Risk Survey (SRS) asks highly specific questions about sexual behavior. You can read the SRS questions in the Appendix at the bottom of this page.

The Brewer et al. Study

As noted by Weiderman (1997) previously, prostitution did not account for much variance in the lifetime reporting of partners. However, this study did not examine the shorter durations (1 year to 5 years) where a much smaller male-to-female report ratio discrepancy remains.

The GSS is nationally representative data with very large sample sizes. However, even large representative samples can miss or underestimate the effects of small population outliers. We have seen up to this point how the “swagger” of outliers in the form of male overreporting skews lifetime sexual partner averages. But is it really just swagger?

Brewer et al. (2000) examined if female prostitution could account for the small discrepancy that still remains in sexual history reporting during shorter time frames of five years and one year. An estimate of 4% of women engaging in prostitution was sourced from survey, police arrest and prostitution-related disease reporting. Although the GSS measures prostitution and sexual reporting by prostitutes, it only reported 1 versus an expected 1.6 prostitutes, indicating a sampling bias that underreported female prostitution.

When the sampling bias was corrected, the sexual partner count ratios of men and women came very close to 1:

This research gives us an alternative interpretation for at least some of the discrepancy in male-to-female reports at high partner counts: undersampling of sex workers with extremely high partner counts. It isn’t enough to explain the entire discrepancy, but it does help close the male-to-female report gap further.

The Alexander & Fisher Study

“People lie” is a common (albeit weak) objection to survey results. But if “people lie” can we detect it? Unfortunately, “lie detector” or polygraph machines are unreliable pseudoscience. Nonetheless, polygraphs are useful precisely because most people don’t know that they are unreliable pseudoscience. When strapped to a polygraph people report and behave as if their lies will be discovered, which lets us use a methodology called the bogus pipeline. If people are dishonest we may detect it by a change in response under the bogus pipeline condition. If people are not dishonest, however, we will see no difference between the bogus pipeline and a control.

Alexander and Fisher (2003) examined responses in three conditions: a face-to-face interview or “exposure threat” condition, an anonymous condition, and a bogus pipeline. The Marlowe-Crowne Social Desirability Scale and scales measuring sexual behavior were also used.

As expected, social desirability scores using the Marlowe-Crowne Social Desirability Scale were highest in the exposure threat condition, medium in the anonymous condition and lowest in the bogus pipeline condition.

Yet in reporting sexual partner count there was no statistically significant difference between men and women. The mean partner difference for men and women in the anonymous condition was 4.2 and 3.4, while in the bogus pipeline it was 4 and 4.4. If an effect existed, it was small and not detected beyond chance. This is what we should expect if people are mostly honest outside of the bogus pipeline. In other words, if people are honest in normal anonymous survey responses. By strapping people to a polygraph we don’t see that they change their responses.

The same author (Fisher, 2013) repeated this research and found a statistically significant, but small, effect of the bogus pipeline: the average difference between partner count for both men and women in the bogus pipeline to non-pipeline condition was less than one partner.

Conclusion

We have seen a consistent trend in the research: men report more lifetime sexual partners than women do. The key word here is lifetime, since as soon as we look at shorter periods of time, between one to five years, most of the discrepancy disappears. Additionally, the lifetime discrepancy is not equally distributed and does not apply to most people who respond. Most men and women are reporting the same number of partners, both for recent time periods and over the lifetime.

We have a good idea why this is the case: men on the right tail of the distribution — the high reporters — are using inaccurate reporting strategies and overreporting their lifetime number of sexual partners. The overrepresentation of large, round numbers is evidence of an inaccurate estimation report strategy. When we remove men who admit they do not have perfect recall, as well as when we remove men who use an estimation strategy for reporting, the discrepancy between men and women disappears.

This does not mean there is not a “swagger” and “secretive” effect at all. It’s likely the case that men still overreport and women still underreport. In the bogus pipeline research, this effect was found — but it was very small, with less than one partner on average.

Alexander is a grad student in behavioral and cognitive research. His research interests are in relationships and attraction. You can follow him on Twitter for interesting research threads and YouTube for evidence-based dating tips.

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MAY CONTAIN: Social science, genetics, philosophy, psychology, chronicles of progress & decay

References

Alexander, M. G., & Fisher, T. D. (2003). Truth and consequences: Using the bogus pipeline to examine sex differences in self‐reported sexuality. Journal of sex research, 40(1), 27-35.

Bogart, L. M., Walt, L. C., Pavlovic, J. D., Ober, A. J., Brown, N., & Kalichman, S. C. (2007). Cognitive strategies affecting recall of sexual behavior among high-risk men and women. Health Psychology, 26(6), 787.

Brewer, D. D., Potterat, J. J., Garrett, S. B., Muth, S. Q., Roberts, J. M., Kasprzyk, D., … & Darrow, W. W. (2000). Prostitution and the sex discrepancy in reported number of sexual partners. Proceedings of the National Academy of Sciences, 97(22), 12385-12388.

Brown, N. R., & Sinclair, R. C. (1999). Estimating number of lifetime sexual partners: Men and women do it differently. Journal of Sex Research, 36(3), 292-297.

Dare, O. O., & Cleland, J. G. (1994). Reliability and validity of survey data on sexual behaviour. Health Transition Review, 93-110.

Morris, M. (1993). Telling tails explain the discrepancy in sexual partner reports. Nature, 365(6445), 437-440.

Turchik, J. A., & Garske, J. P. (2009). Measurement of sexual risk taking among college students. Archives of sexual behavior, 38(6), 936-948.

Wadsworth, J., Field, J., Johnson, A. M., Bradshaw, S., & Wellings, K. (1993). Methodology of the national survey of sexual attitudes and lifestyles. Journal of the Royal Statistical Society: Series A (Statistics in Society), 156(3), 407-421.

Wadsworth, J., Johnson, A. M., Wellings, K., & Field, J. (1996). What’s in a mean?—An examination of the inconsistency between men and women in reporting sexual partnerships. Journal of the Royal Statistical Society: Series A (Statistics in Society), 159(1), 111-123.

Wiederman, M. W. (1997). The truth must be in here somewhere: Examining the gender discrepancy in self‐reported lifetime number of sex partners. Journal of Sex Research, 34(4), 375-386.

Malcolm Collins is a neuroscientist-turned-entrepreneur-turned-VC-turned-education innovator. Along with his wife Simone, he is one of Amazon's top 100 best-selling non-fiction writers.

Learn more about their work:

The Pragmatist Foundation

The Collins Institute for the Gifted

Malcolm and Simone's latest books.

Timestamps:

2:46 Malcolm explains why he and his wife did embryo selection

7:18 Do people realize what they’re arguing for?

10:15 Overlooking positive pleiotropy & other bad arguments

17:18 Malcolm’s theory of the cultural super virus

18:25 Malcolm's general pessimism & falling birth rates

22:02 The case for optimism: religiosity, ritual, and exogenous motivators

23:47 Should we talk about the dysgenic effect on IQ?

24:44 The role of religion in this brave new world

26:55 Religion as a cultural software package

30:18 Malcolm’s plan to save the world!

33:28 Malcolm's idea of “descendant worship” & his thoughts on belief

34:28 Can artificial beliefs really take hold?

38:35 Is our current progress a blip & how a neo-Byzantium can survive

41:08 How many people would actually use genetic technology?

45:20 The ick factor of genetic tech

46:42 The costs and pains of IVF

If you prefer, you can watch our beautiful faces here (apologies for the dodgy audio quality this week):

Written by Ichimoku Sanjin.

The problem of making peace with our anarchic impulses is one which has been too little studied, but one which becomes more and more imperative as scientific technique advances.

– Bertrand Russell

In The Communist Manifesto, Karl Marx famously proclaimed: Workers of the world unite; you have nothing to lose but your chains. Except, in escaping the bondage of industrial capitalism, we welcomed the fetters of domestication. In this article, I seek to present a theory of political psychology that argues the left/right divide can be captured along a single axis: more vs less domestication, respectively. First, let’s clarify our terms and examine the phenomenon:

Domestication encompasses a whole suite of genetic changes that arise as a species is bred to be friendlier and less aggressive. In dogs and domesticated foxes, for example, many changes are physical: smaller teeth and skulls, floppy ears, and shorter, curlier tails. 

Behaviourally, domestication is usually associated with reduced stress/fear responses and decreased aggression (Kunz and Sachser, 1999). This is seen in a longer “socialization window”, in which young animals become familiar with humans without developing a fearful response (Wilkins et al., 2014). 

How does domestication happen? Humans may well be the only species who have domesticated themselves (though some contend that dogs, cats, bonobos, and Marmoset monkeys have all self-domesticated). Many hypotheses exist, but it is generally agreed that selective pressures on tameness and docility were prerequisites (Darwin, 1875; Belyaev, 1974; Trut, 1999, Wiklins et al., 2014). In other words, highly aggressive males became significantly less favored for mating.

Of course, the extent of domestication may vary among individuals, social classes, and races. This could be due to genetics or epigenetics. It is possible that living in "gentle conditions" triggers epigenetic switches. This was demonstrated by the famous study of fox domestication conducted by the Novosibirsk group (Belyaev, 1979; Trut et al., 2004, 2009, although for a critique see Lord et al., 2019). Could it be that developed societies – which are relatively well-protected from disease, violence, famine, and social unrest – have triggered epigenetic changes resulting in a dramatic increase in domestication over a few generations?

After all, the decrease in most types of violence over the last century happened too quickly to be attributable to DNA changes, and so too with the various left-leaning cultural movements such as feminism, gay rights, racial equality, animal rights, and veganism.

Could epigenetic changes (caused by better living conditions) initially have triggered a cultural phenomenon that then led to values of social tolerance and lower aggression? If so, this phenomenon later took on a life of its own, creating a runaway process of cultural evolution that cannot be explained in terms of genetic or epigenetic changes alone. 

Additionally, the dramatic acceleration of technology accompanied this cultural evolution by creating living conditions where social tolerance is more adaptive: overcrowding, increased contact with other ethnic groups, faster communication, and transportation. The values of social tolerance and tameness, in turn, favored technological progress through the promotion of formal education and communication. Technological and social evolution thus mutually reinforced each other and have been increasing at roughly the same pace in a positive feedback loop.

This condition is significantly different from the situation prior to the Industrial Revolution or even the Neolithic when technological and social evolution were both slow enough for genetic evolution to keep up.

Furthermore, natural selection has weakened. Today, there is no correlation between socioeconomic and reproductive success (indeed, the correlation is often negative). As a result, there is a gap between the optimal level of domestication and the average phenotypic domestication level of modern humans. This gap produces obvious psychological and social problems, such as depression, frustration, crime, and social exclusion. Figure 1 suggests the hypothetical trend in domestication on three levels: genetic, epigenetic and techno-cultural.

Let’s test this theory that left/right ideology is a manifestation of different degrees of domestication. Of course, it should be noted that what follows are merely generalisations and there are myriad exceptions.

System Thinking versus Empathy

More domesticated primates such as bonobos have been found to be more skilled at solving theory of mind tasks and those involving an understanding of social causality, while chimpanzees were more skilled at tasks requiring the use of tools and an understanding of physical causality (Hermann et al., 2010).

Individualism versus Collectivism

Generally, the Left is associated with collectivism, which involves the collective action of individuals to achieve common goals, while the Right is associated with individualism, which emphasizes the independence of the individual. This is evidenced by studies of domesticated primate species, such as bonobos, who are known for their ability to cooperate to solve new problems, while chimpanzees are more individualistic in their behavior (Hare et al., 2012). 

Redistribution

The left believes that the government should provide more resources to citizens in order to create a more equitable distribution of wealth. Arguably, this is reminiscent of the human-dog relationship, where the human master provides for the pet regardless of work output or earnings.

In domesticated animal species, sharing is often more informal or the food is provided by a human (such as with dogs, cats, horses, etc.). In contrast, humans are capable of symbolic thinking and of creating institutions. In this context, the role of the master is played by the government, which is responsible for providing resources for its citizens.

Intergroup tolerance

The left is strongly opposed to racism and advocates for increased immigration rights. This is in contrast to the behavior of wild primates such as chimpanzees, who are xenophobic and territorial, engaging in aggressive behavior to protect their territory from outsiders. On the other hand, self-domesticated primates like bonobos are more tolerant and cooperative, even engaging in voluntary sharing of resources with unfamiliar individuals. 

Feminism

The left generally supports the advancement of the rights of women. This is similar to the behaviour of self-domesticated primates like bonobos, where male-female aggression is much lower than in wild primates and females are often the highest-ranking members of the group (Hare et al., 2012).

LGBT+

The left is in favour of the rights of members of the LGBT+ community and there is a higher prevalence of leftists in this group. This could be related to the domestication syndrome which leads to reduced sexual dimorphism and feminization (in males). Yet Bonobos ‘display a substantial emancipation of sexual behaviour into nonconceptive functions’ and show higher rates of homosexuality (Hare et al., 2012). Chimps also only tend to masturbate in captivity — food for thought, gentlemen.

Declining testosterone levels in men

Over the last century, average testosterone levels in men have dramatically declined. There is no scientific consensus as to why, but a reduction in testosterone is the chief biological mechanism responsible for domestication. This phenomenon occurred during human evolution with the transition from the Paleolithic to the Neolithic and was associated with morphological and behavioural changes (Cieri et al., 2014). 

Lockdowns during pandemic

The left generally supported the idea of lockdown. This is similar to the behaviour of domesticated individuals who, due to their high docility and tameness, accept orders even if it results in a deprivation of freedom.

Masking

The left generally supported the idea of wearing masks, even when the scientific evidence was highly questionable. Again, this behaviour can be explained by the tameness of domesticated individuals that facilitates following orders without questioning.

Censorship

The left generally supports the idea of censorship, as free speech sometimes enables individuals to display verbal aggression. This verbal aggression is then redefined as actual violence, a definition expansion process known as concept creep.

Gun Control

The Second Amendment of the United States Constitution guarantees the right of all citizens to bear arms and it allows citizens to protect themselves and their property from potential threats. Conservatives are strong supporters of the Second Amendment. The left’s strong support for gun control stems from low levels of reactive aggression and tameness that are associated with the domestication syndrome.

Urban-rural divide

Leftist voters are concentrated in metropolitan areas, whereas people in rural areas tend to be conservative. People who are more socialized and domesticated seem to prefer living in more civilized environments that are very different from wild or ancestral living conditions, such as the centers of large cities. The emergence of densely populated areas is a relatively recent phenomenon in human evolution. The challenges posed by the transition to higher population density during the Neolithic triggered evolutionary mechanisms leading to morphological and behavioral changes associated with domestication (Cieri et al., 2014). 

In turn, more sheltered conditions probably make people more likely to acquire left-wing beliefs. Take three examples. First, Italy’s general election results for 2022 (shown below). The center-left (red) won in the big cities (Rome, Milan, Florence, Bologna), whereas the right-wing coalition (blue) won in the smaller cities and the countryside.

A similar pattern can be observed in the US, where the left had the majority of votes in more densely populated coastal regions, but the right generally wins in the more rural areas.

There are similar results in Germany, where higher population density is correlated with the share of left-wing votes (Potrafke and Roesel, 2019).

Conclusion

Domestication syndrome successfully predicts beliefs and behaviors associated with the left/right ideological divide. Arguably, it does so better than competing theories, such as the empathic thinking versus systemic thinking paradigm espoused by Jonatan Pallesen. Such theories fail to predict the urban-rural divide, feminism, gun control, higher rates of homosexuality, as well as support for LGBT+ rights. Lockdowns are better predicted by domestication than by empathy, although a case could be made for the latter (i.e. saving lives). Conversely, other examples are predicted equally well by the two theories. For brevity, I have left many questions unanswered, but I welcome the critical engagement of readers in the comments below.

Ichimoku Sanjin is an evolutionary anthropologist. He has worked in population genetics for the last ten years and has carried out research in chronobiology, evolutionary psychology, creativity, and behavioural genetics. He has published in peer-reviewed journals such as Intelligence, Creativity Research Journal, and Thinking Skills and Creativity. He once published poetry but now prefers the language of R.

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Ideas Sleep Furiously

MAY CONTAIN: Ideas for a future worth wanting...

References:

Belyaev, D. K., 1979 Destabilizing selection as a factor in domestication.
J. Hered. 70: 301–308.

Cieri, R.L.,   Churchill, S.E., Franciscus, R.G., Tan, J. and Hare, B.  (2014). Craniofacial Feminization, Social Tolerance, and the Origins of Behavioral Modernity. Current Anthropology,55:4, 419-443

Darwin, C., 1868 The Variation in Animals and Plants under Domestication.
John Murray, London

Hare, B. & Kwetuenda, S. 2010. Bonobos voluntarily share their own food with others. Current Biology, 20, 230e231

Hare, B., Wobber, V., Wrangham, R. (2012). The self-domestication hypothesis: evolution of bonobo psychology is due to selection against aggression, Animal Behaviour,83,573-585, https://doi.org/10.1016/j.anbehav.2011.12.007.

Herrmann E, Hare B, Call J, Tomasello M (2010) Differences in the Cognitive Skills of Bonobos and Chimpanzees. PLoS ONE 5(8): e12438. https://doi.org/10.1371/journal.pone.0012438

Huang, X.; Saint-Jeannet, J.P. (2004). Induction of the neural crest and the opportunities of life on the edge. Dev. Biol. 275: 1–11. doi:10.1016/j.ydbio.2004.07.033

Kelley P., Lockley S. W., Kelley J., Evans M. D. R. (2017). Is 8:30 a.m. Still Too Early to Start School? A 10:00 a.m. School Start Time Improves Health and Performance of Students Aged 13–16. Frontiers in Human Neuroscience, 11, DOI=10.3389/fnhum.2017.00588 

Kaczynski, T. (1995). Industrial society and its future. Washington Post

Kohler et al. (2017). Greater post-Neolithic wealth disparities in Eurasia than in North America and Mesoamerica. 

Kruska, DTC (2005). On the evolutionary significance of encephalization in some eutherian mammals: effects of adaptive radiation, domestication, and feralization. Brain, Behavior and Evolution

Künzl, C., and N. Sachser, 1999 The behavioral endocrinology of domestication: a comparison between the domestic guinea pig (Cavia aperea f. porcellus) and its wild ancestor, the cavy (Cavia aperea). Horm. Behav. 35: 28–37.

Potrafke, N. & Roesel, F. (2020) The urban–rural gap in healthcare infrastructure: does government ideology matter?, Regional Studies, 54:3, 340-351, DOI: 10.1080/00343404.2019.1623390

Theofanopoulou C, Gastaldon S, O’Rourke T, Samuels BD, Martins PT, et al. (2018) Correction: Self-domestication in Homo sapiens: Insights from comparative genomics. PLOS ONE 13(5): e0196700. https://doi.org/10.1371/journal.pone.0196700

Wilkins A, Wrangham R, W Tecumseh Fitch, The “Domestication Syndrome” in Mammals: A Unified Explanation Based on Neural Crest Cell Behavior and Genetics, Genetics, Volume 197, Issue 3, 1 July 2014, Pages 795–808, https://doi.org/10.1534/genetics.114.165423

The problem of making peace with our anarchic impulses is one which has been too little studied, but one which becomes more and more imperative as scientific technique advances.

– Bertrand Russell

In The Communist Manifesto, Karl Marx famously proclaimed: Workers of the world unite; you have nothing to lose but your chains. Exce…

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Is Idiocracy Really The Future?

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22 days ago · 11 likes · 2 comments · Ideas Sleep Furiously

Written by Edward Dutton.

Mr Dean argues that the Flynn Effect – the secular rise in IQ scores of about 2.5 points per decade across the twentieth century – is on g, meaning we have become much more intelligent across time. The alternative argument is that the Flynn Effect is on specialised abilities, such as “Similarities,” which very weakly correlate with intelligence, and that this happened so intensely (due to living in an increasingly scientific society) that it has come across on the (imperfect) IQ tests as an IQ rise. 

The latter argument is congruous with the various “Woodley Effects” that have been set out in the book At Our Wits’ End: Why We’re Becoming Less Intelligent and What It Means for the Future by myself and Michael Woodley of Menie where this argument is also presented (Dutton & Woodley of Menie, 2018). These “Woodley Effects” include lengthening reaction times, worsening colour discrimination, worsening backward digit span, declining per capita major innovation, and decline across time in the prevalence of alleles associated with high IQ. Mr Dean’s argument begs the question of why we are not seeing the real world effects of rising IQ, such as increasing per capita genius, quickening reaction times, less mental retardation and so on. Thus, the argument in At Our Wits’ End makes sense of the most data. 

However, Mr Dean provides a case for his viewpoint by arguing that: “A negative slope implies neither a stall nor a fall of g, but it means merely that the rise of the non-g component is greater than the rise of the g component (the non-g component can be likewise estimated by extending the line to where g-loading is zero).” This is known as the “Brand Effect” and it was addressed by Woodley of Menie and colleagues almost a decade ago (Woodley et al., 2014). 

Most of these IQ tests are multiple-choice. People utilise guessing more today than they did in the past: we know this because historically people were more likely to leave items blank if they didn’t know the answer. English psychologist Chris Brand (1943-2017) predicted many years ago that this was the source of the Flynn Effect (e.g. Brand, 1996). He was partially right. He noted, for example, that each correctly guessed Ravens item would raise your apparent IQ by 7 points. What sorts of items are more likely to trigger guessing: easy ones, or hard ones? The answer, of course, is the latter; more g-saturated items are more likely to be guessed at, meaning that gains on these items are illusory. 

Woodley of Menie and colleagues (Woodley et al., 2014) showed that this “Brand Effect” - the portion of the IQ gains that are due purely to correctly guessed items - is a strong “Jensen Effect” (it is on g). This makes sense, as the harder the item, the more likely it is to be guessed at. Therefore, you must control the “authentic Flynn Effect” for guessing gains (for example, you strip out the separate “Brand Effect”), and then the vector correlation essentially goes to -1, consistent with the argument that the Flynn Effect is not on g. 

A more in-depth reply will be written if and when Mr Dean’s article is published in an academic journal. 

Edward Dutton is a researcher based in Oulu, Finland. He has published in leading journals, including Intelligence and Personality and Individual Differences. He is Professor of Evolutionary Psychology at Asbiro University in Lódz, Poland, and an academic consultant to a research group at King Saud University in Riyadh, Saudi Arabia. He runs a popular online show, The Jolly Heretic, in which he explores controversial research, and interviews academics and dissidents. He is the author of many books and you can follow him on Twitter here.

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology, and tales of cultural decline...

References

Brand, C.R. (1996). The g Factor: General Intelligence and Its Implications. Chichester: Wiley. 

Dutton, E. & Woodley of Menie, M.A. (2018).  At Our Wits’ End: Why We’re Becoming Less Intelligence and What It Means for the Future. Exeter: Imprint Academic.

Woodley, M.A., te Nijenhuis, J., Must, O. & Must, A. (2014). Controlling for increased guessing enhances the independence of the Flynn effect from g: The return of the Brand effect. Intelligence, 43: 27-34. 

Listen to this article here.

Written by Abel Dean.

According to the comedic movie Idiocracy, each generation is stupider than the one before it, because intelligence is largely genetic (this is true), and stupider people tend to have more children (this is also true). This type of natural selection is called “dysgenic” (the opposite of eugenic). The genetic variants for intelligence are decreasing in frequency and the expected end result is that, in a few centuries, the world will belong to idiots. If this is true, it is utterly inexcusable for our leading thinkers and institutions involved in analysing existential risk to have ignored one of the biggest. It is why this publication has tried to raise awareness.

And yet the measured IQ trends tell a different story: each generation has a greater IQ than the one before it. Every ten years, the average IQ increases by three points. Your own IQ is not increasing, but your rebellious adolescent spirit was not completely wrong, as you have an IQ perhaps six points greater than the average of your biological parents, who in turn have an IQ six points greater than your grandparents. Since 1909, IQ has gone up 30 points, or the difference between an average American and someone with an IQ qualifying for a mental disability, or, if we go the other direction, we go from typical to very nearly qualifying for Mensa, the high-IQ society. This IQ trend is known as “the Flynn effect,” named after James Flynn, who discovered in the 1980s that the trend was happening all across Europe. Other IQ researchers failed to notice the global trend because IQ gets reset every decade, more or less, so that the average of the UK or the USA/Canada stays at 100.

The dysgenic effect is real, but it is slower than the Flynn effect in the opposing direction. Picture a man trying to swim against a strong river current: he will still flow downstream but slower than if he were not actively swimming. The Flynn effect is about three times stronger than the dysgenic effect.

For another twist, many psychologists who focus on IQ have decided that Idiocracy is something like a documentary after all. They believe the Flynn effect to be little more than a numerical illusion, maybe just because students are getting better at taking tests.

On the surface, their position may seem unlikely. It is not just IQ that has risen over the last century, but also many other traits related to intelligence, both cultural and physiological. To name but a few: literacy prevalence, educational attainment, GDP per capita, fertility, longevity, height, skull size, brain size, and nearsightedness. 

The psychologists who prefer the dysgenic model tend to be hereditarians, which means they take very seriously the genetics of intelligence, so seriously that they are willing to assert that the measured IQ difference between blacks and whites (15 points or one standard deviation) follows largely from heritable genetic differences. Whilst this is probable, the claim is often at the cost of their academic careers. For those hereditarians, a very large and fast change of two standard deviations (30 IQ) of a trait so strongly heritable (around 75%) is difficult to reconcile, and the easiest reconciliation is to simply deny the objective reality of it.

Of course it is not a mere denial. Hereditarians have many scientific arguments to reinforce their position. Their primary argument even convinced James Flynn, which is like convincing a father that his seemingly-alive child is a mere ghost. Flynn was no hereditarian — he was their primary opponent. 

So, what is that flagship argument? It follows from a method developed by Arthur Jensen to argue that the Black-White IQ gap really is a genuine gap of general intelligence, not just an illusion: the method of correlated vectors (MCV). When I took a close look at that flagship argument, I noticed a gaping crack in the stern.

As a side note, Arthur Jensen in his time was denounced as a scientific racist, with the Southern Poverty Law Center calling him a “White Nationalist” who provided a scientific patina for neo-Nazis. But he won exceptional respect among his academic peers, even his critics. In his book meant to refute Jensen, Flynn wrote:

Jensen has forged a steel chain of ideas, the key links in which are heritability estimates and the absurdity of the concept of a blindfold or a factor X, and that steel chain of ideas leaves the environmentalist with almost no freedom of maneuver.

Flynn saw his own opposition to Jensen as like David against Goliath. Jensen’s argument from MCV is as follows: some IQ tests are divided into subtests, and those subtests have varying “g-loadings"— some subtests are more related to the g factor (the principal component of general intelligence, which is very strongly heritable), and some are less related. On a scatterplot of g-loadings versus d (for score differences between groups), you can draw a best-fit line. If the line has a positive slope, then it means that more g-loaded subtests predict a greater group difference, and therefore a difference in g exists between groups.

This argument served Jensen’s position that the Black-White IQ gap was a genuine difference of general intelligence, not an illusion such as a racial test bias. It is not a completely logical argument, but it is intuitive. Jensen then used a second argument to reinforce the primary MCV argument: if you extend the best-fit line to where g-loading is 1.0, emulating a test that is perfectly g-loaded, you will have a positive d. The logic now seems complete. But to Jensen this secondary argument was merely “also consistent” and “a further validating feature” serving MCV as the primary argument.

For researchers who think the Flynn effect is an illusion, they use a similar primary argument, where d is the difference between generations: the best-fit line has a negative slope. So, the more g-loaded a subtest is, the lesser the score gain across generations. Therefore, they say, the Flynn effect is not a rise of general intelligence, it is just a rise in IQ.

Yet you can no longer use Jensen’s secondary argument to reinforce the primary argument. If you extend the negatively-sloped line to where g-loading is 1.0, then d may be positive, negative or zero, depending on the locations of the data points. When I analyzed the paper of Rushton (1999), which pioneered this argument against the Flynn effect being on g, I found that d at g-loading=1.0 is indeed a positive value (though it isn’t certain, as the confidence band widens hyperbolically outward covering some negative territory). A negative slope implies neither a stall nor a fall of g, but it means merely that the rise of the non-g component is greater than the rise of the g component (the non-g component can be likewise estimated by extending the line to where g-loading is zero).

I illustrate with a pair of plots below.

I argue that Jensen’s secondary argument should have been primary. The positive slope does not prove his point, but the extension of the best-fit line does so. And, not only can we decide the presence or absence of the group difference, but we can have a rough numerical value for that difference: by my math the Flynn effect is a change of g of 1.5±4.5 IQ points per decade. 

I submitted a paper with this argument to the journals Intelligence and Personality and Individual Differences, but they both rejected it, largely because this idea is not novel. Yes, it was Arthur Jensen’s idea, but he mistakenly relegated it to secondary importance. Arthur Jensen’s primary argument focused on the sign of the correlation — he was passionately married to the statistical construct of correlations, and anything else was his mere mistress. A heckuva lot of intelligence researchers remain misled by that mistake, believing the Flynn effect to be an illusion. So, I will try a third journal, Journal of Intelligence.

If I am correct, and if the Flynn effect really does represent a large increase of general intelligence, then how do we reconcile that with the strong heritability of intelligence? The difficulty depends on an understanding of biology that’s a century out of date. Strongly heritable traits can change drastically across generations, because the DNA need not change, only the gene expressions. For example, locusts and the respective grasshoppers are the same species (even the same race), and their state of being either a locust or a grasshopper is strongly heritable, but it takes only a few generations to completely change state. Humans are likewise. We don’t need to depend on intelligence as the main example — adult height is even more heritable than intelligence, and adult height has increased even more than two standard deviations in some populations across the 20th century. How absurd we would be to solve the puzzle by claiming faulty measuring tapes!

In fact, many other human traits changed across the century, and a good framework for making sense of those changes may be life-history plasticity: whatever is relevant for survival and reproduction has been changing in favor or greater “quality” and away from “quantity” on an offspring quantity-quality trade-off (much like locusts, which have greater individual quality and lesser offspring quantity than grasshoppers, surprisingly). But the unlikely belief that the Flynn effect is not on g has deterred researchers from modeling the Flynn effect as a life-history phenomenon. The only exception is the hypothesis of Michael A. Woodley of Menie, in which the Flynn effect is a life-history shift in spite of the Flynn effect not being on g. How does he do it? He speculates that the Flynn effect is not a shift of general intelligence but instead a divergence into many specialized skills. This would be a novel model of intelligence, not just of the Flynn effect. A mistaken belief needlessly complicates his life-history approach.

Hereditarian intelligence researchers are mostly correct. Their science is at odds with our ideologies; our ideologies should adjust to conform, and the science need not budge. Intelligence is the most explanatory and the most socially-relevant psychological trait, but few such researchers exist — the journal Intelligence estimates the existence of only 150 intelligence researchers in the world (hereditarian or not). The topic of study is littered with career landmines — anyone who does too much work on racial IQ differences, even those with tenure, will be fired. The field is at odds with all popular ideologies, and it receives little funding, so there are few truth-tellers. Each of them accepts a heavy responsibility to get the science right. A simple fallacy lasting for decades is therefore a huge obstacle to the progress of their field. If the fallacy misleads them about a core element of a human theory of everything, then everyone is affected. 

At the very least, getting it right will help us figure out the true due date for the coming Idiocracy. 

Abel Dean has studied the psychology of intelligence and other social sciences for over ten years. He has university degrees in completely-unrelated fields: land surveying and hydrographic science (he fully agrees that you should not simply accept his opinions).

Tomorrow, ISF supporters will have access to a reply from Prof Edward Dutton.

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology,

According to the comedic movie Idiocracy, each generation is stupider than the one before it, because intelligence is largely genetic (this is true), and stupider people tend to have more children (this is also true). This type of natural selection is called “dysgenic” (the opposite of eugenic). The genetic variants for intelligence…

Read more

Written by Alexander.

A quick introduction — if you don’t know what a normal distribution is, here:

A normal distribution is the above arrangement of data with a symmetrical distribution around the mean. Also called a bell curve, because we see a symmetrical layout of data around the mean similar to a bell.

Normal distributions occur frequently in nature. For example, height is a natural example that falls on a normal distribution.

The central limit theorem lets us use assumptions of normality even when populations are not normally distributed.

And populations are often not normally distributed.

The confusion arises here

Because normal distributions occur in nature and because we make assumptions about normal distributions in statistical tests, many people erroneously begin to believe that we should assume any given trait in a population is normally distributed. It is simply assumed that a population is actually, truly, in-real-life, normal.

Then when a sample shows up as a skewed distribution the same people may believe that something went wrong.

How do we know if a population is normally distributed?

Since the central limit theorem explains how samples give us normal distributions from non-normal populations, we can’t just look at the normal distribution of a sample and assume that a population has a normal distribution.

At the same time, we usually can’t measure an entire population.

Fortunately we have statistical tests for normality. Normality tests can indicate if our sample data came from a population with a normal distribution or not. The Shapiro-Wilk test is the most common normality test. Here is a website with a few more common normality tests.

That is all that I have to say about normality tests for this article. The point I want you to take home from this section is this: we can’t know if a population has a normal distribution just by looking at the graph of a sample distribution.

Is attractiveness normally distributed?

As of this writing, most research on physical attractiveness (and other measures of attractiveness) that I have read shows normal distributions in its samples. There is one big exception:

This is the dataset from Christian Rudder. Rudder founded the dating website, later the app by the same name, OKCupid. As a founder he had full access to the data and published it in his book Dataclysm.

Here we see that men rated women along a normal distribution, but women rated men in a skewed distribution.

I have seen this interpreted as “women are wrong” and that women “should” rate men along a normal distribution as men do for women.

This is based in part, as I said in the previous sections, on an erroneous assumption of normality. We don’t know that male attractiveness in the population should follow a perfect normal or Gaussian distribution. There is no rule that it should, nor that we should expect it to be so, be it in statistics, logic or anywhere else.

Confusing a scale midpoint with a sample mean

The “women estimate incorrectly” interpretation also seems to rely on mistaking the scale midpoint with the mean of the sample. Because the midpoint of the scale is 3 it is assumed that the sample average should therefore fall on the three.

Here is the thing: the sample tells you what the average is, not the scale. If the average male rating on a scale of 5 is a 2, this does not mean that the 2 is “below average.” 2 is the average.

And again, because the sample does not have a perfect normal distribution we should not expect it to fall on the scale midpoint anyway.

How is attractiveness measured?

Let’s look at a population trait with a normal distribution: height. Height is easy to measure. We have systems of measurement — centimeters, meters, inches, etc. — that are standard. We arbitrarily invent the measurement, but the distances themselves are an objective part of reality. We also have universal consensus on how well those measurements represent the objective distance.

Physical attractiveness is trickier.

This does not mean that attractiveness is random or subjective, but it does mean that our measurements are not as homogeneous as measurements for height. If a woman rates a man 3 out of 5 stars on OKCupid we don’t actually know if she is saying that she believes the man is a perfectly average man.

We also encounter the philosophical problem of where physical attractiveness exists. Physically attractive could mean how others see or rate you. Alternatively, physical attractiveness could be an objective quality like the distance represented by measurements of height.

In studies using facial measurements the latter may be assumed; objective metrics are used to represent physical attractiveness. In studies on physical attractiveness that require rating a face or picking someone to date, the measure of attractiveness is the ratings or behaviors of other people. Even when there is widespread agreement, the observer, the person doing the rating, is the measuring instrument. There is inherent subjectivity, unlike in the tape measure used to assess height.

Subjectivity does not mean randomness however. It does not mean that we have to pretend that we don’t know that universally attractive traits exist, nor that there is widespread agreement on some traits being attractive. The subjective ratings of people can agree. Indeed, they usually do. We see strong inter-rater correlations across multiple attractiveness metrics, cultures and ages (Bronstad & Russell 2007, Coetzee et al. 2014, Hönekopp 2006, Knight & Keith 2005, Kramer et al. 2018, Ma et al. 2016).

In other words, as an implication of this methodology, women are the true measure of male attractiveness. Men are likewise the true measure of female attractiveness.

The implication is that women and men can’t be “wrong” about their ratings. If women rate most men as a 2 on a 5 star scale, most men are a 2. The distribution simply is what it is.

Women are more attractive than men

Women are rated as more attractive than men (Cross & Cross, 1971; Maret, 1983; McKelvie, 1981; McLellan & McKelvie, 1993; Morse & Gruzen, 1976; Wernick & Manaster, 1980, as cited in McKelvie & Stuart, 1993). This is true both in the Rudder’s dataset and more generally as a trend in research on physical attractiveness.

I have been asked how this is possible — if attractiveness is normally distributed then surely men and women must have the same mean level of attractiveness… right?

Well, no. Let’s return to the height example. Height is normally distributed for men and women, but men are nonetheless taller than women.

Distributions don’t tell you what the values of the distributions are. Knowing that a distribution is normal does not give you the values associated with that distribution.

The practical implication here is that there is not a 1:1 ratio of “looks-matches” for men and women. If women are more attractive than men, some highly attractive women (at the far right tail) will have no or fewer men who match them in looks. Some highly unattractive men (at the far left tail) will have no or fewer women who match them in looks.

This may explain in part why, although we see assortative mating in physical attractiveness (men and women pick partners of a similar level of physical attractiveness), women are also slightly more attractive on average than their partners (McNulty, 2008).

There may be a good explanation for this as well. Jokela (2009) found that moderately attractive women were more likely to reproduce (7%), while highly attractive women were even more likely to reproduce (16%). Moreover, both were more likely to have daughters than sons. As such, we see a gradual shift over time of women becoming more physically attractive than men.

Conclusion

We can’t just assume prima facie that attractiveness is normally distributed, nor that it shares the same distribution, for men and women. Nor, even if we do assume an identical distribution, can we assume that the distributions have equal values (that men and women are equally attractive). Indeed, we may have more unattractive than attractive men in the population. At the same time, women are more attractive than men on average.

Summary points

1. We don’t actually know if physical attractiveness falls on a normal distribution.

2. We do see mixed results, with some datasets of male physical attractiveness skewed left.

3. Assumptions of normality don’t tell us anything about the values of those distributions.

4. As such, we have no reason to assume men and women are equally attractive.

5. Research indicates they are not; women are consistently rated as more attractive than men.

Alexander is a grad student in behavioral and cognitive research. His research interests are in relationships and attraction. You can follow him on Twitter for interesting research threads and YouTube for evidence-based dating tips.

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology, and tales of cultural decline...

References

Bronstad, P. M., & Russell, R. (2007). Beauty is in the ‘we’of the beholder: Greater agreement on facial attractiveness among close relations. Perception, 36(11), 1674-1681.

Coetzee, V., Greeff, J. M., Stephen, I. D., & Perrett, D. I. (2014). Cross-cultural agreement in facial attractiveness preferences: The role of ethnicity and gender. PloS one, 9(7), e99629.

Ferrer, R., & Artigas, A. (2011). Physiologic parameters as biomarkers: what can we learn from physiologic variables and variation?. Critical Care Clinics, 27(2), 229-240.

Hönekopp, J. (2006). Once more: is beauty in the eye of the beholder? Relative contributions of private and shared taste to judgments of facial attractiveness. Journal of Experimental Psychology: Human Perception and Performance, 32(2), 199.

Jokela, M. (2009). Physical attractiveness and reproductive success in humans: evidence from the late 20th century United States. Evolution and Human Behavior, 30(5), 342-350.

Knight, H., & Keith, O. (2005). Ranking facial attractiveness. The European Journal of Orthodontics, 27(4), 340-348.

Kramer, R. S., Mileva, M., & Ritchie, K. L. (2018). Inter-rater agreement in trait judgements from faces. PloS one, 13(8), e0202655.

Ma, F., Xu, F., & Luo, X. (2016). Children’s facial trustworthiness judgments: Agreement and relationship with facial attractiveness. Frontiers in psychology, 7, 499.

McKelvie, S. J. (1993). Stereotyping in perception of attractiveness, age, and gender in schematic faces. Social Behavior and Personality: an international journal, 21(2), 121-128.

McLellan, B., & McKelvie, S. J. (1993). Effects of age and gender on perceived facial attractiveness. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 25(1), 135.

McNulty, J. K., Neff, L. A., & Karney, B. R. (2008). Beyond initial attraction: physical attractiveness in newlywed marriage. Journal of family psychology, 22(1), 135.

Auron Macintyre is a columnist and host at The Blaze. He also writes on Substack. Auron rose to prominence as a “dissident right” thinker, making popular YouTube videos on topics from what’s come to be called elite theory.

Timestamps:

1:17 Auron introduces himself

3:20 Racial imposter syndrome

6:03 Whites looking for valid status

8:27 "Nonsense as a uniform”

12:42 The failure of the Intellectual Dark Web

16:18 Steelmanning the popularity of Jordan Peterson

18:40 The Overton Window of Intellectuals

22:40 Controlled opposition without a controller

24:31 What does victory look like in the face of total institutional capture?

27:53 Secessionism, federalism, and societal breakdown 

30:40 Cultural decline vs material progress

36:12 Right-wing tensions

40:00 Our role in the cycle of civilisation 

43:03 Reconciling theology and techno-optimism 

52:36 Can biotech be deployed responsibly in our current political climate?

55:15 What will post-liberalism look like?

57:30 If Auron could only undo one aspect of post-60s culture, what would it be?

If you prefer, you can watch our beautiful faces below:

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology, and tales of cultural decline...

Written by Davide Piffer.

It is difficult to say definitively whether artificial intelligence (AI) is more creative than humans, as creativity is a complex and multifaceted concept that cannot be easily compared.

On one hand, AI has the potential to be more creative than humans in certain ways. For example, AI can process and analyze vast amounts of data quickly and accurately, which can allow it to generate new ideas and solutions by synthesizing and analyzing information in a way that is beyond the capabilities of most humans. AI can also learn and adapt to new situations through the use of machine learning algorithms, which can allow it to come up with creative solutions to problems that may not have been anticipated by its human creators.

However, there are also limitations to the creativity of AI. One of the main limitations is that AI is only as creative as the data and algorithms that are fed into it. In other words, AI is only capable of generating ideas and solutions that are based on the information that it has been trained on, and it is not able to come up with truly novel ideas or solutions that are completely unrelated to its training data.

Another limitation of AI is that it lacks the ability to understand and interpret the world in the same way that humans do. While AI can process and analyze data, it does not have the ability to experience emotions, perceive the world through our senses, or understand the context and meaning behind the data that it is processing. This means that AI is not able to generate ideas or solutions that are inspired by emotions, personal experiences, or cultural context in the same way that humans can.

In conclusion, while AI has the potential to be more creative than humans in certain ways, it is limited by the data and algorithms that it is trained on and lacks the ability to understand and interpret the world in the same way that humans do. As such, it is difficult to say whether AI is more creative than humans overall.

Why the italics? Because this is the answer ChatGPT gives to the question: “Is artificial intelligence more creative than humans?”. 

If you’ve been living under a rock, ChatGPT is a form of artificial intelligence (AI). It is a language model that has been trained on a large dataset of human-generated text and can generate human-like responses to prompts. Language models like ChatGPT are commonly used in natural language processing (NLP) tasks, such as dialogue generation and chatbot systems.

However, the answers provided by ChatGPT on a test designed to measure creative potential in humans suggest that the chatbot may overestimate its limitations.

Psychologists often use divergent thinking tests, such as the Alternate Uses Test (AUT), to assess creativity. The AUT, developed by J.P. Guilford in 1967, asks individuals to come up with as many uses as possible for a simple object like a brick or shoe. Results from this test are typically evaluated based on four dimensions:

• Fluency, or the number of alternative uses generated

• Originality, or the uniqueness of the ideas

• Flexibility, or the range of ideas in different domains and categories

• Elaboration, or the level of detail and development in the ideas.

Research has shown that the ability to generate a high number of relevant ideas or answers (known as fluency) is strongly related to the other three dimensions of divergent thinking (Takeuchi et al., 2020). Because it is simpler and less problematic to measure fluency by counting the number of answers given rather than evaluating the originality of those answers, we will focus on the number of responses provided. The reader can then determine the originality of the answers for themselves. From personal experience administering this type of test to humans, I have found that the perceived originality of the answers given by ChatGPT is not lower than that of human responses. I prompted ChatGPT with this item from a divergent thinking test:

Provide as many answers as you can to this divergent thinking test: 

"Other than drinking milk, how can we use milk bottles?”

Before continuing, try to answer yourself and set a 5-minute time limit. After completing the test, count the number of answers you have provided. I’ll list ChatGPT’s answers at the end.

I noticed that the AI stopped at 10 answers because this is the pre-set number of responses. I then asked it to provide more answers. Of course, the AI could have continued indefinitely and would have outperformed any human in terms of the number of ideas generated.

According to research by Benedek et al. (2013), the average university student generates about 13 ideas (standard deviation = ~5) in 5 minutes. In contrast, the AI provided 10 answers in 1 minute, 20 in 1:30 minutes, and 30 in 2 minutes. At this rate, it could reach a score of 90 in 5 minutes, which is about 15 standard deviations above the average for university students. 

It is possible that the AI is simply too modest to admit that it has surpassed humans in terms of creative potential. One explanation for this paradox is that the divergent thinking tests used to measure creativity may not actually be reliable indicators of creativity. 

I’ve previously written about how creativity should result in a realistic product (2012). Obviously, people can quibble about definitions, but most recognize that nonsense answers aren’t the hallmark of a truly creative person. If I said I could use a milk bottle as a skyscraper, for example, that’s undoubtedly a creative use, but is it actionable? The most creative people aren’t the ones daydreaming random thoughts, but the people who take action. Another person might have the same thought, recognize it is ludicrous but use it as ludic inspiration for an actual building or blueprint. Hence, there’s a distinction in the literature between creative potential and creative output. 

ChatGPT outperforms us because it’s a language model based on statistical associations between words. That is, it learns patterns and relationships between words and sequences of words, and uses these patterns to generate new text or understand and respond to input.

In the same way, divergent thinking is based on associative processes. People with less structured semantic networks have looser association hierarchies, and can make remote associations more easily (Mednick, 1962; Kennet, Anaki and Faust, 2014, Forthmann et al., 2016). In humans, both bottom-up processes such as associations and top-down abilities such as memory retrieval and fluid intelligence are involved in divergent thinking tasks (Beaty et al., 2014).

ChatGPT can obviously draw from a huge amount of knowledge, dwarfing the memory capacity of any human being. Hence, not only can it make associations more rapidly, but it can make associations using a much larger pool of concepts. So, ChatGPT clearly possesses one aspect of creativity (divergent thinking) to an extremely high degree. But does this result in real creative behavior? 

The answer is yes…sometimes. DALL·E 2 is an AI system that generates realistic images and art based on verbal descriptions provided by users. It is currently difficult to evaluate the artistic value of its creations, but it is clear that the output of this system is significantly greater than that of any human artist in terms of both the quantity of its production and the level of technical skill. It is also likely that the originality of DALL·E 2's creations surpasses that of the average non-artist human.

The widespread, long-held belief that AI is only capable of excelling at logical and cognitive tasks, and is incapable of emulating human creativity, has been challenged. Indeed, it may even be easier for AI to surpass humans in terms of creative thinking — at least in some domains — than in raw IQ due to its ability to utilize its associative network and extensive knowledge base for generating creative ideas. Its ability to identify relationships and connections allows AI to come up with novel ideas and solutions that humans may not have thought of, or to produce paintings by combining concepts or visual features that are not obviously related. A significant open question is whether a lack of emotions will prevent AI from true artistic mastery or whether this is yet another thing that can be mimicked with enough data, processing power, and algorithmic ingenuity.

For the price of a couple of fancy coffees with cream, you can support us, gain early access to our podcasts and films, and exclusive access to narrated versions of our articles and our Three Special Questions (for example).

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology, and tales of cultural decline...

References

Beaty RE, Silvia PJ, Nusbaum EC, Jauk E, Benedek M. The roles of associative and executive processes in creative cognition. Mem Cognit. 2014 Oct;42(7):1186-97. doi: 10.3758/s13421-014-0428-8. PMID: 24898118.

Benedek M, Mühlmann C, Jauk E, Neubauer AC. Assessment of Divergent Thinking by means of the Subjective Top-Scoring Method: Effects of the Number of Top-Ideas and Time-on-Task on Reliability and Validity. Psychol Aesthet Creat Arts. 2013 Nov 1;7(4):341-349. doi: 10.1037/a0033644. PMID: 24790683; PMCID: PMC4001084.

Boris Forthmann, Anne Gerwig, Heinz Holling, Pınar Çelik, Martin Storme, Todd Lubart. The be-creative effect in divergent thinking: The interplay of instruction and object frequency. Intelligence,Volume 57,2016, 25-32, https://doi.org/10.1016/j.intell.2016.03.005.

Y.N. Kenett, D. Anaki, M. Faust (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8 , 1-16, 10.3389/fnhum.2014.00407

S.A. Mednick (1962). The associative basis of the creative process. Psychological Review, 69, 220-232, 10.1037/h0048850

Piffer, D. (2012). Can creativity be measured? An attempt to clarify the notion of creativity and general directions for future research. Thinking Skills and Creativity, 7(3), 258–264. https://doi.org/10.1016/j.tsc.2012.04.009

Hikaru Takeuchi, Yasuyuki Taki, Rui Nouchi, Ryoichi Yokoyama, Yuka Kotozaki, Seishu Nakagawa, Atsushi Sekiguchi, Kunio Iizuka, Sugiko Hanawa, Tsuyoshi Araki, Carlos Makoto Miyauchi, Kohei Sakaki, Yuko Sassa, Takayuki Nozawa, Shigeyuki Ikeda, Susumu Yokota, Daniele Magistro, Ryuta Kawashima (2020). Originality of divergent thinking is associated with working memory–related brain activity: Evidence from a large sample study, NeuroImage,Volume 216,2020,116825,1053-8119, https://doi.org/10.1016/j.neuroimage.2020.116825.

Appendix:

ChatGPT answers

First prompt:

Milk bottles can be used as a container for storing and transporting liquids, such as water, juice, or homemade smoothies.

Milk bottles can be used for making homemade yogurt or fermented dairy products, such as kefir or crème fraîche.

Milk bottles can be used for growing herbs or small plants, by filling the bottle with soil and seeds, and placing it in a sunny spot.

Milk bottles can be used for making homemade cleaning solutions, by mixing water, vinegar, and essential oils in the bottle.

Milk bottles can be used as a makeshift funnel, by cutting off the top of the bottle and using it to pour liquids or dry ingredients into small containers or bottles.

Milk bottles can be used as a DIY bird feeder, by filling the bottle with birdseed and hanging it up in a tree or on a balcony.

Milk bottles can be used for storing small objects, such as buttons, beads, or craft supplies.

Milk bottles can be used for making homemade soap or shampoo, by mixing together various oils, fragrances, and other ingredients in the bottle.

Milk bottles can be used as a makeshift watering can, by filling the bottle with water and using the spout to water plants.

Milk bottles can be used as a DIY lamp, by cutting a hole in the bottom of the bottle, inserting a light bulb, and wiring it to a power source.

Second prompt:

Milk bottles can be used as a portable container for drinks, such as coffee, tea, or hot chocolate, by attaching a lid and carrying it with you on the go.

Milk bottles can be used as a makeshift ice pack, by filling the bottle with water and freezing it.

Milk bottles can be used as a DIY sprinkler, by cutting small holes in the sides of the bottle and attaching a hose to the top.

Milk bottles can be used for storing and organizing small items, such as coins, paper clips, or rubber bands.

Milk bottles can be used for making homemade salad dressing or marinades, by mixing together oil, vinegar, and spices in the bottle.

Milk bottles can be used for making homemade insect repellent, by mixing together essential oils and water in the bottle and spraying it on your skin or clothing.

Milk bottles can be used as a DIY snow globe, by filling the bottle with water, adding small figurines or other decorations, and sealing the top.

Milk bottles can be used as a makeshift telescope, by cutting a small hole in the bottom of the bottle and looking through it to magnify objects in the distance.

Milk bottles can be used as a DIY aquarium, by filling the bottle with water and adding small fish or other aquatic creatures.

Milk bottles can be used as a makeshift drum, by filling the bottle with beans, rice, or other small objects and tapping it with a spoon or other object to create different sounds.

Third prompt:

Milk bottles can be used as a DIY birdhouse, by cutting a hole in the side of the bottle and attaching it to a tree or other structure.

Milk bottles can be used as a makeshift terrarium, by filling the bottle with soil, plants, and other decorations to create a miniature ecosystem.

Milk bottles can be used for storing and organizing art supplies, such as markers, crayons, or paints.

Milk bottles can be used as a DIY candle holder, by filling the bottle with melted wax and inserting a wick.

Milk bottles can be used as a makeshift maraca, by filling the bottle with beans, rice, or other small objects and shaking it to create different sounds.

Milk bottles can be used as a DIY pinhole camera, by covering the top of the bottle with aluminum foil and making a small hole in the center.

Milk bottles can be used for storing and organizing small tools, such as screws, nails, or bolts.

Milk bottles can be used as a DIY sand art project, by filling the bottle with different colors of sand and creating designs or patterns.

Milk bottles can be used as a makeshift drumstick, by attaching a piece of foam or other soft material to the end of the bottle and using it to hit a drum or other percussion instrument.

Milk bottles can be used as a DIY sensory bottle, by filling the bottle with water, food coloring, and small objects, and sealing the top to create a visually appealing and tactile sensory experience.

It is difficult to say definitively whether artificial intelligence (AI) is more creative than humans, as creativity is a complex and multifaceted concept that cannot be easily compared.

On one hand, AI has the potential to be more creative than humans in certain ways. For example, AI can process and analyze vast amounts of data …

Read more

We spoke for nearly an hour about a variety of topics, including:

• Intelligence research, its past, present, and future.

• The smartest intelligence researchers and people Charles and Helmuth have met.

• Nationalism and white supremacy

• How societies are “coming apart”

• Whether America was a doomed project

• Whether multi-ethnic societies can be successful

Watch the full conversation below:

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Ideas Sleep Furiously

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Timestamps:

0:00 Intro

1:35 Paul’s working-class background and how he came to write the book

5:53 Paul’s writing (and non-university) background

8:25 Did Paul have to develop a thicker skin as a public writer?

10:46 Saying “I was wrong” and flirting with the far left

14:30 Following in Orwell’s footsteps and the left’s authoritarian tendency

22:32 How many people are worth talking to?

24:39 The left’s response to the brutal murder of Sir David Amess and personality politics

28:50 What is Blue Labour?

32:06 How the left adopts the mentality of the right

35:50 Would Paul rather see extreme social liberalism or extreme neoliberalism?

39:06 Could Blue Labour transform the Party?

40:51 Have the Tories parked their tanks on the Blue Labour lawn?

43:52 Would Paul ever go into politics?

45:28 What would have to happen for Paul to enter politics?

46:55 The bankruptcy of the Marxist “false consciousness”

52:28 What’s the most woke idea Paul agrees with?

54:35 The problem with “lived experience”

55:45 Are we moving in a more authoritarian direction?

1:01:44 Pronouns are not just about being kind

1:05:50 Why are trade unions going woke?

1:10:42 Favourite fiction and non-fiction

1:12:25 What other occupation would Paul have chosen?

1:14:10 Paul’s advice to young people

1:15:54 If Paul could only change one policy

For the price of a couple of fancy coffees with cream, you can support us, gain early access to our podcasts and films, and exclusive access to narrated versions of our articles and our Three Special Questions (for example).

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology, and tales of cultural decline...

This my first end-of-year letter as ISF’s founding and lead editor. Of course, many of those words might lead you to think we hadn’t just set up shop. It was only in October that we properly got this show on the road (that is, as a collective of data-driven writers).

Yet in the last two months, we’ve already had great success. Obvious highlights include becoming a Substack Featured Publication one day after our Relaunch Week in mid-October, and my own article on a genius basic income being effusively discussed in theWashington Post by Adrian Wooldridge, who called ISF ‘an intriguing Substack’. But you’ve seen nothing yet!

2023 is going to be a very special year. We will be welcoming regular columnists to the ISF Substack, releasing high-production short films on our main YouTube channel about fascinating people who are rarely spotlighted, and attracting high-quality guests on the ISF Podcast. There is much more, but it shall remain top secret for now.

ISF’s goal is to do something different in the “heterodox” space. We want to focus on the social science and philosophy of the future. By which I mean, we’re not interested in ephemeral ideas and problems that will likely be solved or forgotten in a decade or less. Our perspective is long-term. That’s why, for example, much of our focus is on genetics.

If we have an editorial line, it’s rather simple: we believe that our species’ astonishing technological progress is undeniable, important, and too often overlooked. Mark from Peep Show is a solid advocate for this position:

Watch the brilliant 38-second diatribe here:

As a data-driven publication, we are interested in trend lines, not headlines. I recently Tweeted a handful of stories from 2022 that illustrate the reverence we all should have for the smartest people in the world and the remarkable — but far-from-perfect — aquarium we all get to swim in:

🧠Ideas Sleep Furiously🎙️ @Ideas_Sleep

THIRTY POSITIVE STORIES FROM 2022: 1. Earth now has enough solar installations to power the whole of Europe. 2. The UN has brought us closer to ending plastic pollution with a new global treaty. 3. mRNA vaccine technology could be treating cancer by 2030.

4:18 PM ∙ Dec 25, 2022

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23Likes10Retweets

On that note, we also believe that much of the West is undoubtedly in a cultural malaise, if not decline. That’s hard to contest when you read modern fiction, laugh at modern art, despair at modern music, or hold your vomit at most modern architecture. Of course, the talent to overturn these things is likely still there, dormant and waiting for Spring.

In this regard, we are not simply another “anti-Woke” publication pretending Drag Queen Story Hour is the Fall of Constantinople. No, ISF is an anti-bad-ideas-wherever-we-find-them publication — as cringeworthy as that may sound, it’s hard to find other words.

Richard Hanania did a much better job of outlining this position in his own end-of-year reflections, where he discussed why he had become more left-wing on social issues and berated low-IQ conservatives for their ‘hostility to progress’ and individual liberty. He writes:

In sum, it’s becoming clearer that neither of our major coalitions comes close to representing what I believe. Identifying with the “Right” or “Left” at this point would force me to accept a lot that is indefensible, but at the very least categories like “social liberal”, “anti-woke”, and “economic conservative” seem useful to talk clearly about political and social topics given the extent to which positions on various issues under each of those umbrellas tend to be correlated with one another.

What’s more, we do not believe that culture and technology are non-overlapping magisteria. Things can quickly become so bad culturally that a country’s technological sophistication or natural resources offer no bulwark against demagoguery and institutional collapse.

Hence our motto: Ideas for a future worth wanting.

That future is a common sense one only true radicals would disagree with, say green anarchists or Jihadists. We want technology that enables us to protect our planet, cure disease, and colonise the galaxy. And we want proud, non-self-flagellating cultures that seriously appreciate how rare, beautiful, and, indeed, imperfect Western civilisation in particular is.

If an idea is important in this regard and if there’s a good chance it’s true, we’re interested in publishing it. Take, for example, the winner of our inaugural ISF Essay Competition, where we asked people to argue for and against a controversial opinion they may or may not hold. The winning essay was on the possible benefits of simulated child porn, those benefits being deterring paedophiles from committing child sexual abuse, catching more paedophiles, and reducing the demand for real child porn.

Several people whom I greatly respect (and who had said plenty of controversial things themselves) told me not to publish this piece. Why? Because of optics. “Stay away from anything with the words, ‘child porn’! ”, wrote one. Another brilliant writer told me that ISF should avoid getting a reputation as mere controversialists, writing:

It's very easy for our enemies to say "--- gave an award to an article defending child porn," and people don't have a lot of patience to listen to our rebuttal about how it's not real child porn, and actually we want to protect children, or whatever.

I have no doubt they are right about the “optics”. The mere words ‘child porn’ are enough to give me quite the ick response. But if anybody read the actual piece, all they would find is a data-driven discussion about the population and individual effects of pornography, informed by the most mainstream of studies and heavily caveated (as per the Competition’s guidelines) at every corner. This isn’t to say I know, as a layman, whether the article is correct, but if Psychology Today, Scientific American, and mainstream journals publish similar content, ISF cannot turn it down.

Not least because if it is correct, children’s lives and safety depend on such debate. To those who would stifle that debate by slandering a publication, thereby endangering children, I say fuck you. I doubt paedophilia and pornography are issues we will return to any time soon (unless, of course, someone wanted to write a rebuttal), but ISF will not be cowed into self-censorship because a group of low-decoupling morons cannot discuss controversy with the same dispassionate analysis that they apply to buying toothpaste.

It will be an odd year if several of the articles we publish in 2023 are not equally as “controversial” (by mainstream normie standards). So let me end by stating the obvious: we may well publish articles about topics like group differences in socially important traits, dysgenics, and migration, but we do not have a dog in the fight. We only want to know what is true, something academia cares less and less about. We will always welcome dialogue.

If you think that’s worth the price of a couple of coffees per month, please consider supporting our unique publication for just $6.99 a month or $69.99 a year. Paid supporters get access to our 3 special podcast questions, early podcast access, narrated articles, and much more is coming down the pipeline. On January 2nd, they will have access to a special filmed conversation between Charles Murray and Helmuth Nyborg:

Thank you for your support this year.

Happy New Year & see you in the next one.

Matthew Archer,

Founding Editor

Our three most-read articles in 2022:

Ideas Sleep Furiously

Ideology literally makes people illogical

Listen now (5 min) | Below are three arguments. I want you to think about whether the conclusions follow from the given premises. (1) All drugs that are dangerous should be illegal. Marijuana is a drug that is dangerous. Therefore, Marijuana should be illegal. (2) Judge Wilson believes that if a living thing is not a person, then one has the right to end its life. She also b…

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7 months ago · 19 likes · 5 comments · Ideas Sleep Furiously

Ideas Sleep Furiously

Diagnosing Our Decay | An interview with N.S. Lyons

Listen now (1 min) | N.S. Lyons is a popular Substacker. He describes himself as existing in the U.S. foreign policy blob, surfing the zeitgeist, and chronicling our shared unreality. He writes The Upheaval, a wide-ranging newsletter exploring the nature, causes, and consequences of the chaos increasingly engulfing our lives as the world is forcibly reconfigured by at least…

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4 months ago · 45 likes · 14 comments

Ideas Sleep Furiously

Do Women Really Select For Intelligence?

Supporters can listen to this article here. Ideas Sleep Furiously is a heterodox social science & philosophy publication and podcast. You can join the ISF community for just $5 a month and gain early access to podcasts, the exclusive 3 special questions we ask guests, and much more. For the price of a fancy coffee with cream, you can help us to grow and …

Read more

2 months ago · 35 likes · 22 comments · Ideas Sleep Furiously

Two underappreciated podcasts:

Ideas Sleep Furiously

The British working classes once read the canon | Prof Jonathan Rose

Listen now (66 min) | Prof Jonathan Rose is the William R. Kenan Professor of History. He has held visiting appointments at the University of Cambridge and Princeton University and he reviews books for The Times Literary Supplement and the Daily Telegraph. His fields of study are British history, intellectual history and the history of the book. We mainly talk about his magn…

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5 months ago · 3 likes · Ideas Sleep Furiously

Ideas Sleep Furiously

The Future of Genetic Engineering | Torill Kornfeldt - ISF Podcast

Listen now (110 min) | Torill Kornfeldt is a Swedish science journalist, author and speaker focusing on biology and biotechnology. She is the author of two books. The first titled, The Re-Origin of Species, discusses the reconstruction of extinct animals using gene modifications. However, in this conversation we mostly speak about Torill’s second book…

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7 months ago · 5 likes · 4 comments · Ideas Sleep Furiously

OBVIOUS DISCLAIMER:

Publishing ≠ endorsement. What’s more, the opinion of the writer isn’t necessarily their own. In several cases, they explicitly told us that they didn’t actually hold the controversial opinion but thought a good argument could be made.

Fake Child Porn Might Be A Net Positive

Written by Tobias Christensen.

This essay will argue that simulated child porn (hereafter: SCP) is likely to be a net positive for future societies. This analysis excludes unrealistic futures, such as the mass imprisonment of most pedophiles. Likewise, it seems unlikely that a pharmaceutical cure will be available before SCP saturates the internet. The reader should also note that throughout the essay I only refer to SCP which is visually indistinguishable from standard CP, rather than something like manga. I argue SCP has the potential to do three things:

1. Deter pedophiles from committing child sexual abuse (hereafter: CSA)

2. Catch more pedophiles

3. Reduce the demand for real CP.

I attempt to offer the best criticism against each proposition. 

1. Deterring Pedophiles

Three possible outcomes arise from pedophiles watching CP, whether real or simulated:

1. It increases the risk of committing CSA.

2. It decreases the risk.

3. There is no meaningful effect.

Regarding (1), the three main causal pathways are: 

• (A) Pedophiles become more likely to commit CSA because CP excites them to an uncontrollable degree. It is like an inhibition-lowering drug increasing the likelihood of dangerous behavior.

• (B) CP might eventually cease to satisfy needs. A similar pharmaceutical analogy works: child porn could be a “gateway drug” leading pedophiles to search for a bigger “hit”.

• (C) Finally, there is the chance that culturally normalizing SCP signals to pedophiles that their desires are morally permissible, causing them to advance to CSA.

The academic literature for all three claims is statistically messy. Some obvious problems are:

• Studies reporting a strong link between CP possession and CSA often use convicted pedophiles as samples. 

• The best-educated guess is that 1% of men are pedophiles. Hence, either the overwhelming majority of pedophiles never become abusers or we do not catch them (the former being much more likely given the vast disparity between reports of CSA and the estimated number of pedophiles).

• Most acts of CSA are not even performed by pedophiles but by situational offenders, those who use children for gratification without attraction. 

• Finally, pedophiles are not, by any means, a psychologically homogeneous group — it is entirely possible that CP has diametrically opposite effects depending on the pedophile.

In this regard, psychologist Dennis Howitt’s 1995 summary statement endorses outcome 3: ‘The most reasonable assessment based on the available research literature is that the relationship between pornography, fantasy and offending is unclear.’ However, Howitt wrote those words before researchers had analyzed national case studies of governments changing policy. Sometimes, this was a dramatic shift from extremely punitive to legally permissible. History thus granted a series of natural experiments, the results of which support outcome 2.

In 1990, after the Velvet Revolution, the (now) Czech Republic decriminalized the possession of child pornography. A 2010 landmark study by Milton Diamond et al. compared the 15–17-year interval during which any pornography was illegal with the 18-year span in which it was widely available (1989-2007). Hence, the data include easily attainable internet porn. Of course, one criticism here is that CSA was already declining due to unknown causes. 

Yet what’s remarkable is that robbery, impersonal murder, and other serious crimes did spike after communism. Yet, as the authors note, ‘the number of sexually motivated murders or killings somehow associated with sex did not increase’.

The researchers note that Denmark and Japan also experienced prolonged intervals during which the possession of child pornography was not illegal, with both countries showing a significant decrease in the incidence of CSA. Of course, this should not be surprising because the same trend holds for regular pornography in every country that has been investigated. That is, pornography appears to substitute for sexual aggression. 

Other studies are more dubious in their methodology, but should at least be noted. For example, Endrass et al. (2009) used a clever comparative design to check recidivism rates among offenders and concluded CP-viewing alone does not seem to be a risk factor for future sex offenses. Another well-documented finding is that rapists and child sex offenders tend to view less pornography.

2. Catching Pedophiles

A well-known and effective—but time-consuming—tactic for catching pedophiles is for authorities and vigilantes to pose as children. Given high-fidelity SCP will soon proliferate, one suggestion is for law enforcement to distribute digitally watermarked SCP. This may actually be more ethical than the vigilante version of the “honey trap” tactic because public shaming can result in pedophile suicide, making it difficult for authorities to uncover further victims and often leaving the state to support the dead pedophile’s family.

A criticism of this proposal is what I call the Pedo-Laffer curve. Namely, if all CP were SCP, and all SCP were digitally watermarked, we may catch a larger proportion of the people downloading and distributing, but if the deterrence argument is correct, CSA would presumably rise because it becomes too risky for the pedophile to watch SCP, the thing which has a moderate to large effect for CSA prevention.

3. Reducing demand 

Finally, if pedophiles can eventually (and easily) generate any type of porn they want, real CP might become utterly deficient in comparison, especially if SCP is augmented by hardware that further enhances realism. Demand for CP could therefore plummet.

However, this is a more speculative argument. One could object to it with what I call the Fake Mona Lisa Effect. Philosopher Jesse Prinz found that 80% of a focus group would prefer to look at the ashes of the original Mona Lisa than a perfect duplicate. If true, this is more likely to be a culturally contingent effect than a deeply biological one. But regardless of its etiology, the urge for authenticity may contribute to maintaining CP demand. One could imagine a future subculture of CP distributors who “deal in the real thing” because some sadistic pedophiles gain more pleasure from knowing the children suffered.

For those interested in reading more, here are two links to non-academic articles:

Psychology Today: Evidence Mounts: More Porn, Less Sexual Assault

Scientific American: The Sunny Side of Smut

Ideas Sleep Furiously

MAY CONTAIN: Philosophy, genetics, intellectual history, psych/soci-ology, and tales of cultural decline...