Gerd Gigerenzer on the legacy of Daniel Kahneman

Gigerenzer writes:

Daniel Kahneman and Amos Tversky’s joint papers from the 1970s and 1980s . . . turned statistical thinking–previously a niche interest–into a major research focus. . . . In their joint work, known as the heuristics-and-biases program, Kahneman and Tversky argued that human judgment systematically deviates from the norms of probability and logic, resulting in predictable cognitive biases. These biases were attributed to heuristics–mental shortcuts–which led to a broader narrative in behavioral economics and psychology that emphasized human fallibility in decision-making. . . .

In his article, Gigerenzer offers an interesting perspective. One thing I like is that he talks about the science and the sociology of science, both of which are important, and he treats Tversky and Kahneman as human beings rather than as abstract heroes (as here, for example).

I had four thoughts in reaction to Gigerenzer’s article.

1. Staying out of war

I appreciate that Gigerenzer pushes against the metaphor of scientific debate as war:

The intellectual exchange between Kahneman & Tversky and myself has been dubbed the “rationality wars.” I am not partial to the term war, given that I tried hard to separate scientific disagreements and personal respect. It is easy to contest someone’s ideas if you dislike the person, but it is emotionally demanding to disagree with mutual respect. It was not always easy for either side, but Kahneman and I both did our best.

I’d put it in even stronger terms. Sometimes I’ve had scientific disputes with people who I think have behaved very badly and whom I strongly dislike–but I still don’t think such disputes should have the flavor of “war.” Even with a scientist who is doing misguided work and who is also a bad person, it’s rare for there to be a negative-sum “war” scenario, and I think we should do our best to avoid such settings. I wrote something about this once, entitled There is a war between the ones who say there is a war, and the ones who say there isn’t.

2. Don’t forget Laplace

Just a reminder that many of the ideas of cognitive illusions, heuristics and biases were in a book by Laplace from the early 1800s; see here. In his article, Gigerenzer mentions Laplace in the context of the gambler’s fallacy and the idea of probabilistic reasoning being a form of logic. But there’s a lot more to the Laplace book than that! I say this not to disparage the important work of Tversky and Kahneman, but just to trace the history of these ideas. Among other things, Laplace formulated the concept of cognitive illusions.

3. Economist are confused about rationality and psychology

As Gigerenzer points out, one of Kahenman’s important contributions was to bring some modern ideas of cognitive psychology and decision analysis to the attention of the field of economics. But economists remain very confused about concepts of rationality and psychology. One place I’ve seen this is in the very misused term, “risk aversion”: see here or, for more links, here. Given this persistent misunderstanding on the part of nearly all of the economics profession, one thing I have always appreciated about Kahneman and Tversky is that they pushed against naive interpretations in which probabilities and utilities can be directly deduced from decisions. Even if, Gigerenzer argues, they went too far in many cases, I think they moved the discussion in the right direction.

4. The progress of Tversky and Kahneman’s early research

Gigerenzer talks about Tversky and Kahneman’s experimentation using simple questions (Linda, etc.) rather than randomization devices. I’m not sure on this, but it’s my impression that there was an intermediate step, that before asking questions about Linda etc., Tversky and Kahneman were asking questions of psychology researchers about sample size and replication. For example see the studies describes in pages 107-109 of their Belief in Small Numbers paper. They anticipated a lot of the concerns of the replication crisis, which is ironic given Kahneman’s later dive into poorly supported pop-psychology of the Ariely variety.

My impression, without studying the literature carefully, is that Tversky and Kahneman started with the then-surprising realization that professional psychology researchers had systematic confusions about probability and uncertainty, and then they moved to probability reasoning questions that were not tied to psychology, for example this one, from their 1974 paper:

A certain town is served by two hospitals. In the larger hospital about 
45 babies are born each day, and in 1the smaller hospital about 15 
babies are born each day. As you know, about 50 percent of all babies 
are boys. However, the exact percentage varies from day to day. 
Sometimes it may be higher than 50 percent, sometimes lower. For a 
period of 1 year, each hospital recorded the days on which more than 60 
percent of the babies born were boys. Which hospital do you think 
recorded more such days?
- The larger hospital (21)
- The smaller hospital (21)
- About the same (that is, within 5 percent of each other) (53)

Then they moved to even more general questions, such as Linda the feminist, Dick the engineer, and so forth. From the standpoint of pure psychology, there’s an appeal to questions about Linda and Dick because they do not require understanding of the context of research practice, sample size, statistical significance, etc. Gigerenzer criticizes the experiments on Linda etc. as lacking context, which is fair; I just think that in a review of Kahneman and Tversky’s work it makes sense to see the progression. I still love the Belief in Small Numbers paper; if every psychology researcher had read it carefully, maybe we would’ve had much less of a replication crisis. Unfortunately the message of that paper seems to have been lost amid the later work by Tversky and Kahneman.

More from Gigerenzer

I sent the above to Gerd G., who added the following:

As to your observation of a transition stage between Tversky & Edwards experiments with physical random devices and the Kahneman-style Linda-type questions, you are correct. This transition is best illustrated in their first joint paper “Belief in the Law of Small Numbers.” In this paper they show that many academic psychologists to not understand the statistics they rely on, which is amazingly still the case today.

There exists a different literature on intuitions about sample size, beginning in the 1950s – e.g., Piaget & Inhelder, The Origin of the Idea of Chance in Children, 1951 (translated into English in 1975) – that concluded that already by age 11, children pay attention to sample size, which seems to contradict the findings by Tversky & Kahneman. In the attached paper, Peter Sedlmeier and I have tried to resolve this apparent contradiction: most people pay attention to sample size in tasks involving frequency distributions, but not in tasks involving sampling distributions.

As to Laplace, I am fortunate to be married to a historian of science, who has written on Laplace and the Enlightenment probabilists (Lorraine Daston, 1988, Classical Probability in the Enlightenment, Princeton University Press). Both Laplace and Tversky & Kahneman is that both have written about cognitive illusions, as you point out. But for Laplace, these illusions were mostly due to hope and fear, not cognitive flaws. Laplace also believed that these illusions can be made disappear by education, while Tversky and Kahneman relied on the analogy of stubborn visual illusions.

Most important for the comparison of current thinking with that of Laplace is that the Enlightenment probabilistic had a different framework of thought, known as “mixed mathematics.” For the Bernoullis to Laplace, probability and human reasoning were seen as two kinds of the same coin. Just like Euclidean geometry was about the physical world, not an abstract system, probability theory was about the human mind. This is why the St. Petersburg Problem was called a “paradox”; there is not mathematical contradiction, but probability contradicted the reasoning of reasonable people. The psychologists closest to this perspective are Piaget and Inhelder. You find the idea of mixed mathematics still in George Boole’s 1854 book.

In our 1989 book The Empire of Chance, we have described the rise and fall of the classical interpretation of probability – as the way the human mind works. The fall in the early 19th century was in part due to the terrible events in the aftermath of the French Revolution which could not be reconciled with the view that probability and reasoning of educated people would go together. The fall gave rise to the frequency interpretation of probability, which then was known as moral statistics.