I recently finished Michael Lewis’s The Undoing Project, which focuses on the lives and work of psychologists Danny Kahneman and Amos Tversky. They changed how we think about how we think, with their work on psychology having major influences in economics and medicine, in particular. I really enjoyed the book, and there were a few points I wanted to write about here, as I think they are important for scientists, mentors, and/or academics to consider. It’s not a full review of the book* – I’m just focusing in on a few areas that I thought were particularly notable.
Variation in and between samples & how we interpret it
One major theme of Amos and Danny’s work is that humans are not nearly as rational as we think we are. (I’m referring to them by their first names because this is what Lewis does throughout the book.) This includes studies that they did on academic statisticians, who routinely made basic errors when asked about different scenarios. One important one is that
even statisticians tended to leap to conclusions from inconclusively small amounts of evidence. They did this, Amos and Danny argued, because they believed – even if they did not acknowledge the belief – that any given sample of a large population was more representative of that population than it actually was. – page 159
This is what leads people to believe that, if a coin is flipped and there are several heads in a row, that a tail is more likely on the next flip, rather than having the same likelihood it always does: 50 percent.
Even people trained in statistics and probability theory failed to intuit how much more variable a small sample could be than the general population – and that the smaller the sample, the lower the likelihood that it would mirror the broader population. They assumed the sample would correct itself until it mirrored the population from which it was drawn. – page 160
Amos and Danny called this “the law of small numbers”, which was that people believed that small sample sizes would have the same properties that large samples do.
One of the things that was the most striking to me was a question they posed to their fellow academic psychologists: what should they recommend to a student who collects one sample and finds X and then collects a second sample and finds not X? For the most part, psychologists didn’t say they would recommend that the student increase sample sizes or think critically about their theory. Instead, they said they would recommend that the student should try to find a reason for the difference between the two groups. That is: they ignored that the two samples would possibly differ from one another just because they are small samples drawn from the same larger population, and, instead, assumed that the underlying populations are different. I suspect many of us would make the same mistake. If I sample one lake and find X and sample another lake and find Y, my first instinct would be to wonder what was different about the two lakes. But it’s entirely possible — especially if the sample sizes are small — that the lakes are identical, and it’s just that I didn’t properly characterize the population with my sample.
To put it succinctly, we “tend to extract more certainty from the data than the data, in fact, contain”. – page 162 (emphasis mine)
I felt like this was such an important thing to consider that I assigned this section of the book for lab meeting one week this summer.
The dangers of getting attached to a single explanation
In the course of our personal and professional lives, we often run into situations that appear puzzling at first blush…Typically, however, within a very short time we come up with an explanation, a hypothesis, or an interpretation of the facts that renders them understandable, coherent, or natural. The same phenomenon is observed in perception. People are very good at detecting patterns and trends even in random data. In contrast to our skill in inventing scenarios, explanations, and interpretations, our ability to assess their likelihood, or to evaluate them critically, is grossly inadequate. Once we have adopted a particular hypothesis or interpretation, we grossly exaggerate the likelihood of that hypothesis, and find it very difficult to see things any other way. (pages 205-206, quote from a talk given by Amos, emphasis mine)**
This is definitely something I’ve worried about: that we can easily start to believe just so stories because they have a compelling narrative, rather than because they are actually true. This was a good reminder of the importance of having multiple working hypotheses, and of being critical of one’s own evidence and narratives.
One really striking thing is how close and intense the collaboration between Amos and Danny was. It was basically a marriage. There were clearly highs – the book notes how they used to lock themselves in a room with just each other, and all people outside the room could hear was constant laughter as they worked. They would sit next to each other as they wrote each individual sentence of a manuscript, which is something that I can’t imagine doing with a collaborator! Here’s a description from the book:
Their offices were tiny, so they worked in a small seminar room. Amos didn’t know how to type, and Danny didn’t particularly want to, so they sat with notepads. They went over each sentence time and again and wrote, at most, a paragraph or two each day. – page 158
In the end, they could never tell who had come up with which idea.
At least, that was true while they were in the same place. Eventually, they were in different places (I’m not sure if they actually moved more than a typical academic, but, given how hard I found moving, how much they moved stood out to me) and their collaboration started to fall apart. Around that time, they were interviewed by someone who was interested in power (professional) couples. During that interview, Amos said:
The credit business is very hard. There is a lot of wear and tear, and the outside world isn’t helpful to collaborations. – page 294
Notably, the person who interviewed them, a Harvard psychiatrist named Miles Shore, began the project because of disagreement over whether to promote someone at his institution who was clearly doing important work, but all of it in collaboration with another person. Clearly collaborations have become the norm in academia, but it’s still true that this issue of trying to partition credit can end up being problematic. It’s part of why I’ve been interested in the question of what is signified by last and corresponding authorship of a paper. The book also notes that awards ended up causing additional tension: Amos won a MacArthur genius grant while Danny did not; as it’s put in this New Yorker piece about the book:
When the MacArthur grants are awarded every year, only the most egomaniacal of us read the list and say, “Damn, I lost.” Unless, that is, your best friend wins the prize for work you did entirely together.
But back to the specific subject of Amos and Danny’s collaboration: they had very different personalities – Amos was brash and outgoing, while Danny is much more self-doubting. Because they were still able to work together and respect and trust each other’s ideas and insights, Danny’s doubts and Amos’s confidence were able to balance each other, leading them to do world-changing science.
Praise, criticism, and regression to the mean
Danny and Amos are (or were, in the case of Amos) both Israeli, and one recurring theme of the book is their time fighting for Israel and/or working with the Israeli army. Early in his career, Danny had worked with the Israeli army to try to change how they selected, assigned, and trained incoming recruits. During that time, he found that the instructors believed that harshly criticizing a pilot who had a bad flight was the best way to improve their performance.*** The experience was that, if someone had a bad flight and was harshly criticized for it, he usually did better the next time. If he had a good flight and was praised for it, he usually did worse the next time. The higher ups in the military had taken this to mean that harsh criticism caused people to improve and praise caused people to get worse. Danny pointed out that it was really just regression to the mean: if someone had an exceptionally good flight, they probably weren’t going to have a second exceptionally good flight right after it. The same holds for an exceptionally bad flight. It was really just regression to the mean, not the impact of what was said, but it led to a culture where there was lots of criticism and not a lot of praise. It seems to me that this same phenomemon applies pretty broadly. To quote the book:
We are exposed to a lifetime schedule in which we are most often rewarded for punishing others, and punished for rewarding. – page 203
The importance of downtime
The secret to doing good research is always to be a little underemployed. You waste years by not being able to waste hours. – Amos Tversky, page 230
This is actually the reason I got the book, after learning about this quote from Andrew Read. I thought this was going to be a major theme of the book, but it wasn’t. Still, I think this a nice, pithy way of framing the idea that we need time to step back from our work and let things stew a bit. This is one of the benefits of running for me.
It was troubling to consider, he began, ‘an organism equipped with an affective and hormonal system not much different from that of the jungle rat being given the ability to destroy every living thing by pushing a few buttons.’ Given the work on human judgment that he and Amos had just finished, he found it further troubling to think that ‘crucial decisions are made, today as thousands of years ago, in terms of the intuitive guesses and preferences of a few men in positions of authority.’ The failure of decision makers to grapple with the inner workings of their own minds, and their desire to indulge in their gut feelings, made it ‘quite likely that the fate of entire societies may be sealed by a series of avoidable mistakes committed by their leaders.’ – page 247, talking about a talk that Danny Kahneman gave
I literally sighed as I finished typing that quote. It applies to so much that is going on today, including decisions related to climate change.
No one ever made a decision because of a number. They need a story. – Danny Kahneman, page 250
This is a particularly succinct way of framing much of the advice related to science communication! He then went on to say,
the understanding of numbers is so weak that they don’t communicate anything. Everyone feels that those probabilities are not real – that they are just something on somebody’s mind.
Science & theory
Science is a conversation and you have to compete for the right to be heard. And the competition has its rules. And the rules, oddly enough, are that you are tested on formal theory. – Danny Kahneman, page 287
This relates to the part above about collaboration. As Lewis puts it in the book, “Danny’s interest ended with the psychological insights; Amos was obsessed with the business of using the insights to create a structure. What Amos saw, perhaps more clearly than Danny, was that the only way to force the world to grapple with their insights into human nature was to embed them in a theory. That theory needed to explain and predict behavior better than existing theory, but it also needed to be expressed in symbolic logic.” Danny is also quoted as saying
What made the theory important and what made it viable were completely different – Danny Kahneman, page 287
I think that, because they were focusing a lot on things with implications for economics, having a formal, mathematical theory was probably particularly important, but I think the same general sentiments can apply in ecology and evolution.
As I said at the beginning, my goal wasn’t to provide a complete book review, but, rather, to focus in on a few things that I found particularly interesting and thought might be interesting to readers of the blog. There were lots of other interesting things in the book, too (including descriptions of Danny’s escape from Nazi Europe as a child and of life in Israel in the 60s and 70s). Overall, I really enjoyed it and found it gave me lots of food for thought.