But in a bad way; not in a good way. I was blown away by the monotony and the abundance of inaccurate science. One wouldn’t normally expect mainly bad science from a Nobel Prize winner. Then again, perhaps one would if the Nobel Prize is in Economic Sciences, since in general the further you stray from the hard sciences the less rigorous the science is. (Incidentally, the Nobel Memorial Prize in Economic Sciences was created in 1968 whereas the other five were created in 1895.)
The book provides a few interesting insights, but not nearly enough for 400+ pages. I’ll quote the most interesting one here so you won’t make the mistake of buying the book yourself:
Consider two car owners who seek to reduce their costs:
Adam switches from a gas-guzzler of 12 mpg to a slightly less voracious guzzler that runs at 14 mpg.
The environmentally virtuous Beth switches from a 30 mpg car to one that runs at 40 mpg.
Suppose both drivers travel equal distances over a year. Who will save more gas by switching?
If you do the math, the unintuitive answer is that Adam will save more. The conclusion is that US labeling policy should be changed from mpg to the more intuitive gpm.
I fear that the above may entice you to read the book. So in an attempt to offset that urge let me provide some examples of the deplorable logic that dominate the book.
On page 72, Kahneman tells a story in which he and his wife saw an acquaintance in Australia, then two weeks later saw the same acquaintance in a London theater. He states, “By any measure of probability, meeting Jon in the theater was much less likely than meeting one of our hundreds of acquaintances – yet meeting Jon seemed more normal.” Huh?? Why is meeting Jon less likely? Actually, if all else is equal, they are equally likely to meet Jon in London as any other acquaintance. And if you take Jon’s propensity to travel (as evidenced by the only additional information we have, namely his presence in Australia) into account then it is more likely to meet Jon in London.
Then on page 76, Kahneman writes, “Experiments have shown that six-month-old infants see the sequence of events as a cause-effect scenario, and they indicate surprise when the sequence is altered. We are evidently ready from birth to have impressions of causality, which do not depend on reasoning about patterns of causation.” Huh?? Does Kahneman assume that a human cannot learn impressions of causality in his first six months?
You may think I’m nit-picking a bit here, but I wouldn’t have picked up on these two examples if erroneous thinking wasn’t pervasive throughout the book. Let me present two more examples.
On page 116, he recounts his contribution to the Israeli Air Force during a war. Two similar squadrons were faring differently. One squadron lost four planes while the other had lost none. The operational differences between the squadrons were initially found to be small. He says, “My advice was that the command should accept that the different outcomes were due to blind luck, and that the interviewing of the pilots should stop. I reasoned that luck was the most likely answer, that a random search for a nonobvious [sic] cause was hopeless, and that in the meantime the pilots in the squadron that had sustained losses did not need the extra burden of being made to feel that they and their dead friends were at fault.” Again: huh?? This one I really don’t get. His thinking is so ridiculous I can’t explain how it’s ridiculous. But let me attempt anyway. He implies that since the losses were probably random that they shouldn’t be investigated. I would say that the loss of a multimillion dollar plane should be investigated regardless of whether you think it may be random or not. What if a non-random cause is discovered?
Lastly, on page 184, he ends the previous chapter with the following exercise:
One of my favorite examples of the errors of intuitive prediction is adapted from Max Bazerman’ s excellent text Judgment in Managerial Decision Making:
You are the sales forecaster for a department store chain. All stores are similar in size and merchandise selection, but their sales differ because of location, competition, and random factors. You are given the results for 2011 and asked to forecast sales for 2012. You have been instructed to accept the overall forecast of economists that sales will increase overall by 10%. How would you complete the following table?
Store 2011 2012
1 $11,000,000 ________
2 $23,000,000 ________
3 $18,000,000 ________
4 $29,000,000 ________
Total $81,000,000 $89,100,000
Having read this chapter, you know that the obvious solution of adding 10% to the sales of each store is wrong. You want your forecasts to be regressive, which requires adding more than 10% to the low-performing branches and adding less (or even subtracting) to others. But if you ask other people, you are likely to encounter puzzlement: Why do you bother them with an obvious question? As Galton painfully discovered, the concept of regression is far from obvious.
Again Kahneman is incorrect. The most reasonable calculation in this scenario is to treat each 2011 sales number as that store’s average due to the store’s location (and therewith simply add 10% to each number), and not to confusedly combine the numbers and regress them to the mean as if they were representative of all stores.
It is not only bad science that makes this book so unreadable; it is also unfortunately oozing with self-satisfaction and reminiscences of Kahneman’s happy, bygone friendship with his “research” partner Amos. Does Kahneman mean to present this book as an autobiography or a popular account of science? By all means, be inspired by and happy with your work, but emoting such self-satisfaction about your own poor thinking only degrades the quality of your work further.
Kahneman attempts in this book to give a scientific exposition of various failings of humans’ propensity to occasionally misinterpret certain numeric scenarios, and to make it sound new in the process. The ideas he presents in this book are not new. Some of them are from earlier books and articles, and others from his papers of 1983 and earlier. As I read I felt Kahneman attempting to make his book entertaining, as Malcolm Gladwell does with similar subjects. But where Gladwell succeeds in entertaining us, Kahneman fails miserably. Neither does Kahneman adhere to any scientific method, as I detailed earlier. Kahneman’s resulting book is neither new, nor entertaining, nor scientific; it is unfortunately simply a monotonous aggregation of anecdotal statistics.