Fooled by the truth
Are we getting dumber? Or just too easily fooled by data that misleads?
In late June, Igor Halperin posted on LinkedIn some about data about the birth dates of Nobel prize winners that supposedly supported the idea that people are getting dumber. Someone else reposted it on X, where it blew up.
The argument, backed up by a graph that was factually correct, was pretty simple. Starting in approximately 1935, the number of future Nobel Prize winners born each year has been dramatically declining. Using that as a proxy for average IQ, “this data can be interpreted as supporting previous research that concluded that we are getting dumber.”
As you can see from the graph, there’s a sharp drop, and then a flat line around 1960.
The repost on X got over 10,000 likes and was reposted more than 500 times. Based on the popularity and circulation of the tweet, and a lot of the comments (as well as some comments on LinkedIn), many people believed this was a legitimate, potentially persuasive argument.
What does this data say for our collective intelligence given that we’ve practically stopped producing Nobel Prize winners? Well, not much.
This is a good example of how easily we can fall misleading conclusions backed by data that 100% will survive a fact check. It’s not the data that’s wrong; it’s the conclusions we reach (or, in this instance, accept) based on the data. And because the data survives a fact check, it makes the conclusion feel much more true, especially if the interpretation supports a belief you already have.
Why is the interpretation that people are getting dumber so unfounded here? Because there is an obvious alternative explanation for this seeming disappearance of people of Nobel-Prize-level intelligence: How old are Nobel Prize winners?
Old.
In Burton Feldman’s book on the history of the Nobel Prize, his advice on how to win the award was, “live to a very old age. They may finally catch up with you.” The average Nobel Laureate is 59 years old when they receive the Nobel Prize. Because of the Nobel committee’s restriction against posthumous awards, there are built-in biases against young people. The average age for producing Nobel Prize-worthy research is 39, so you have to live long enough for your time to come. If you die young, or the committee is still getting around to recognizing worthy recipients who are older than you, you’re screwed.
It may be the ultimate example of survivor bias.
To state the obvious, there's a reason why nobody born in 2020 (or 2010, or 2000) has a Nobel Prize. And it's not because people aren't as smart as they used to be. The greatest genius born in 2020 is just entering preschool. The greatest genius of 2000 is a struggling grad student.
On the other hand, any cohort based on birth year takes nearly a century for a final count. Five of the oldest recipients of the Nobel Prize have been in the last few years, aged 89 to 97. The born-in-1922 cohort added 2 members, in 2018 and 1019. The born-in-1931 cohort added 3 members, in 2020 and 2021.
If you want to call the born-in-1980 cohort dumb compared with, say, the born-in-1930 cohort, it’s likely going to take until 2080 for an apples-to-apples comparison. (Who knows? There may still be a few years for people born in 1930 to win Nobel Prizes.)
Incidentally, in a follow-up post on LinkedIn, Halperin explained that the he initially posted this as a joke, “an illustration of how one can obtain absurd results if you are not careful with your data.” Halperin said he was “blown away by the wide spectrum of comments and emojis on my post! They ranged from getting my joke to pointing that people born after 1970 did not yet receive their prizes, to agreeing with me on general philosophical grounds, to insisting that I myself must be a very dumb person.”
He also recognized that the comments could themselves be a proxy for illustrating “dumbness,” but wisely refrained from reaching a conclusion.
Even without quantification, though, the number and range of comments on LinkedIn and X show how easily we can fall for this type of misleading use of data. And if we’re going to fall for something so blatantly and obviously absurd, think about all the ways that you're falling for more subtle versions of this every day.
There's a lot of more danger to this than pure misinformation because with misinformation, the data won’t survive a fact check. But the flatline we observe at 1960 for Nobel prize winners, does easily check out. It’s the conclusion, the way people interpret that data, that is wrong.
That mean it's on you to look at data like that and ask, what else could be driving that effect? In this particular case, there's a totally obvious answer. Nobel laureates are old. But good data hygiene requires searching for alternative explanations even when the faulty conclusion is not so in-your-face absurd.
Alex Edmans has a book out titled "May Contain Lies" that addresses this kind of issue. His stack goes from a statement to is it a fact? to is it representative data? to is it evidence? to is it proof? He uses 2 cognitive biases, confirmation bias and black and white thinking to work thru the stack. The book uses stories to highlight the issues.
Great post, Annie.
My current theory for the data has to do with the decreasing efficacy of the academic/scientific system in producing Nobel-worthy results. As our institutions of higher learning break down (relative to their original purpose and mission) and as the economic incentives around winning a Nobel become more perverse and captured, it stands to reason that what was once a nice “coevolutionary coupling” between the educational system and Nobel system is decoupling as well.
Put another way, it used to that the brightest minds worked on problems/solutions in a relatively pure and intrinsically motivated way, and that was acknowledged and rewarded by a Nobel in the extremes.
Now, like everything, winning the Nobel is a big business, and academia is big business. The pure and intrinsic motivations and been squeezed out of both.