Notes on my seminar (German title “Chancen, Gleichheit und Genetik”) oriented around K. Paige Harden’s book “The Genetic Lottery” (2021).
Session 2 is still mainly input from me, with some discussion. We talk about how to have discussions on the controversial topics of the book, I respond to comprehension questions about the chapter, show them the results of their quiz.
I put up a few principles up for discussion (assume good faith, avoid overloaded words, stay friendly, watch how much you’re speaking). Then, we play a game, a confirmation bias exercise. Last year, I learned that most of them knew the 2-4-6 Wason sequences game (where people ask in a confirmatory way and grow overconfident about the rule), so this year, I modified it a little (letters instead of numbers, more complex rule). They shout possible 5-letter-words and I tell them whether they fit the rule or not. I got the desired result (overconfidence about the rule after confirmatory questions), though my game was maybe a little overengineered.
I use this game as a jumping off point for talking about human (ir)rationality. I tell them that some of the “biases, fallacies, priming, nudging” work hasn’t survived the replication crisis and that some of the newer work on fake news, filter bubbles, the post-truth age, the backfire effect also is full of holes. Still, confirmation bias is very replicable, as the exercise just showed. I namecheck Mercier & Sperber’s argumentative theory of reasoning as one compelling explanation.
I then show them a graph of their political leanings from the anonymous quiz, which in Leipzig reliably shows that they lean left. I argue that to sharpen our arguments, we need some friction, otherwise we’re at risk of confirmation bias and ending up with weak arguments for predetermined conclusions. If we want sharp arguments that will survive a debate with someone further away on the political spectrum, we need to do better.
How? I planned to tell them about how the role of “advocatus diaboli” can help us keep this in check, as it did for the Catholic church, who ended up with an excess of saints and miracles after doing away with their advocatus diaboli. Unfortunately, that stylized fact doesn’t quite hold up to scrutiny (see tweet thread below). So, instead I told them about my journey of discovering that it’s more complicated, after of course seeking initially merely to confirm rather than disprove the story I heard. I guess it works on a meta level?
A story I've heard repeated (e.g. in Zenko's book "Red Team"): when the Cath church got rid of devil's advocates, the number of saints rose sharply.
— Ruben C. Arslan (@rubenarslan) October 18, 2022
Problem: I tried to find a quantitative treatment, but found only news items.
Self-made graph from Wikipedia's list of blesseds 1/n pic.twitter.com/LQJGAxIyxU
As part of this, I remind them that I want to be criticized as well, and that I have set up a way for them to contact me anonymously.
This part is a bit long. I don’t know if I need all of it, but the discussions last year were pretty healthy and my tendency is to keep the spiel.
In this part, I try to answer to comprehension questions they raised as part of their responses to the quiz. For chapter 1, a lot of people always ask about what is basically “happiness” economics. Harden challenges the ‘classic’ Kahneman & Deaton 2010 study which purportedly found a limit after which more money (in income) is no longer associated with more happiness. First, they answer a small survey about the limit where they think happiness no longer increases (or whether they believe in a relationship at all). The modal result is usually something like 80,000€ a year, but this year it was more like 100,000€ a year. Inflation? :-)
Then, I show them the graph from K&D and then graphs from the international replication by Jebb, Tai, Diener, & Oishi (2018). These both show some sort of flattening or even a peak of happiness (with log income on the X axis). In discussion, they come up with all sorts of reasons why that may be.
I ask them whether it fits their impression that most people stop hustling after their first million. Then, I show them the following clip:
Then, I show them Matthew Killingsworth (2021)1, who found no peak, no flattening, just a plain old loglinear relationship. I walk them through the result, the explanation for the previous results (ceiling effects, insensitive at high levels of positive affect). Then, I show them the graph with income not on a log scale and ask them what it implies for the effect of redistribution on average happiness.
Here, I just pick some items that many got wrong and try to explain the correct answer with some recycled slides from my lecture. Because they all did their B.Sc. in a bunch of different places, the quantity and quality of their previous genetics lectures varies from “none” to “in depth knowledge of the virtues of the candidate gene paradigm” to “twin studies”. I don’t have the ambition to lay all the groundwork myself, because the book does it well, but of course it’s nice to add some graphs and other illustrative examples.
To anticipate the next chapter, I close with a video of a Galton board/bean machine plus a picture of a lognormal variant (since we just talked about income). Then, some pictures of basketballers and a scatterplot of height for MZ/DZ twins, because Harden will go on to talk about Shawn Bradley, a very tall basketballer who has been genotyped.
so jealous of this name for a man with this research topic↩︎
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For attribution, please cite this work as
Arslan (2022, Oct. 20). One lives only to make blunders: Opportunity, equality, and genetics, seminar session 2. Retrieved from https://rubenarslan.github.io/posts/2022-10-20-opportunity-equality-and-genetics-session-2/
BibTeX citation
@misc{arslan2022opportunity,,
author = {Arslan, Ruben C.},
title = {One lives only to make blunders: Opportunity, equality, and genetics, seminar session 2},
url = {https://rubenarslan.github.io/posts/2022-10-20-opportunity-equality-and-genetics-session-2/},
year = {2022}
}