Part II: Heuristics and Biases
People tend to rely too heavily on representativeness and stereotypes when judging likelihoods. People are not good at intuiting Bayes's Rule or other probabilistic laws (especially when there is a good story to tell otherwise).
This section of the book is the weakest so far in terms of the quality of the research, robustness of the results, and the importance of the results in a broader context. I was going to rip it apart paragraph by paragraph, but Kahneman does a very surprising thing in chapter 15: he admits that it is the weakest body of research his ideas have sparked. I find this incredibly honorable (even though I still think the research is even weaker than he admits). One doesn't often see this in popular writings.
Case in point: I read Why Nations Fail last year, and while the book is good and from two great economists, the whole thing can be summarized in one sentence: Nations fail because of bad, extractive institutions while culture and geography don't matter. But the authors go way overboard in stating their case, reliance on questionable historical examples, and bashing opposing theories. The world is more complicated than they admit, and solutions are not as simple as clamoring for "inclusive institutions" (what does that even mean, anyway?).
But, I digress. I want to talk about two main studies, in this section of the book.
1) Profile of Tom W: "Tom W is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to have little feel and little sympathy for other people, and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense."
Given the psychological profile of Tom W, people tend to be overly confident that he is a computer scientist. The error is that while Tom W may be close to the stereotypical/representative computer scientist, there aren't that many computer scientists relative to the population as a whole. There are many more people in humanities and education than computer science, so there should still be a significant chance (maybe even more likely) that Tom is in the social sciences. People seem to disregard the population base rate when given specific information that tells a good story.
However, as this study (which I've linked to before) summarized in the literature review/introduction, when researchers ask for frequencies ("how many out of 100?") rather than probabilities, base rate neglect disappears and subjects act like good Bayesians even when the subjects are not trained in statistics.
Here one more problem: Kahneman pulls a bit of a fast one in his description of the Tom W results. Rather than providing actual frequencies of nerdiness in different fields (which is important because stereotypes are real reflections of the environment and real selection effects and can be self-reinforcing), he claims people should have stayed close to the base rates because "the source of Tom W's description was not highly trustworthy." Say what? I mean, we know that and Kaheman knows that (he made up the description especially to fool a colleague -- it's not something that comes from an actual psychological profiling), but do the subjects know that? Probably not.
2) Linda. "Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Please rank order the following statements with respect to their probability:
-- Linda is a bank teller.
-- Linda is a bank teller and active in the feminist movement."
Of course, "Linda is a bank teller" is more likely. The probability rule is
Pr(A) = Pr(A and not B) + Pr(A and B).
In other words, the probability Linda is a bank teller is equal to the probability Linda is a bank teller and NOT active in the feminist movement plus the probability Linda is a bank teller and active in the feminist movement. Therefore, Linda is a bank teller must be more likely. The problem is that "Linda is a bank teller and active in the feminist movement" makes a good story. (The conjunction fallacy.)
However, if instead of "Please rank order the following statements with respect to their probability" we ask "To how many out of 100 people who are like Linda do the following statements apply?" subjects do not violate the probability rule. The results are very sensitive to how the question is asked.
Probability is hard! Do people even know what that really means? Kahneman on the one hand says people substitute representativeness for probability because probability is hard but on the other hand discounts the research that asks for frequencies because people intuitively understand what probability means. I don't think he can have it both ways.
When there are no stakes, right answers and wrong answers don't matter. Thinking (especially about probabilities) is difficult and costly. One of Kaheman's students responded "so what?" when Kahneman pointed out he violated an elementary logical rule in the Linda study. Kahneman was discouraged by this. But he shouldn't be. Why should people think hard when they don't want to and there is no cost to getting it wrong?
One more thing. Kahneman mentions John List's studies on the market for baseball cards as confirming evidence for the existence of psychological biases in individuals. But (for example, this paper) even if individuals have strong biases and fall into all the traps psychologists lay for them, it may not matter at all at the market level. Markets punish mistakes and people learn. If you keep making mistakes, you will go bankrupt and be forced out of the market. The market participants may not have any idea what statistical rules they are learning, they just know they are doing better. They may even exhibit really bad biases in all other areas of life. But that market experience matters for that market.
This is a critically important insight. We can make fun of economists all we want for modeling simple, perfectly rational individuals, when we know people are more complicated than that, but if at the market level these biases are competed out and the market looks like it's made up of simple, rational people, then that's all that matters.
Regression to the mean.
These chapters are not that interesting. There is one really good quotation, though:
"Whether undetected or wrongly explained, the phenomenon of regression is strange to the human mind. So strange, indeed, that it was first identified and understood two hundred years after the theory of gravitation and differential calculus."
Amen to that.