Over the past few days I’ve been commenting on Daniel Kahneman’s book, Thinking, Fast and Slow. I’ve written about the advice he has for avoiding biases in planning and forecasting and about how loss aversion may help explain why it is so common for companies to throw good money after bad.
Today, I want to focus on Kahneman’s thoughts about when to trust intuition, which he developed with fellow psychologist Gary Klein.
According to Kahneman, expert intuition is valuable when the environment is orderly and regular enough be predictable, there are opportunities to learn those regularities with prolonged practice, and high quality feedback is available quickly. Kahneman says nurses, firefighters, chess and poker players, and high-level athletes fit in this category in some cases.
Music is another sphere where this would seem to apply. Many types of music have an underlying logic and one can internalize it with practice. Feedback is available instantaneously. A jazz jam session seems to be a perfect example of expert intuition at work.
On the other hand, Kahneman says to be wary of so-called “expert” intuition in cases where there is no regularity or underlying logic, or where feedback is limited or slow. Political scientists operate in such a fundamentally unpredictable environment. So do investors and many other businesspeople.
Where (or whether) one should make a distinction between intuition, forecasting, and prediction is not entirely clear to me. Political scientists, investors, and businesspeople seem to be just as inaccurate with forecasts they develop explicitly as those they make intuitively. So, it seems the lesson for businesspeople is to be careful with all forecasts and to avoid biases in planning and forecasting.
There’s another type of intuition worth discussing—judgments of people. In a subsequent post I’ll summarize Kahneman’s advice on algorithmic vs. intuitive personnel selection.
For now, though, let me relay a story about an M&A deal I was involved with. The company I was working with was negotiating with three Japanese companies for an investment. According to our objective evaluation criteria, two of them where clearly better candidates because they were larger, willing to invest more, and better known—all of which would have provided a strongly positive signalling effect to other investors and customers. As a result, we spent several months focusing on negotiations with these companies in preference to the third, smaller candidate.
However, as good as the larger candidates may have been on paper, they were also arrogant, cagey, and difficult to work with. The smaller candidate was pleasant, open, and easy to work with. Unfortunately, our evaluation framework did not include these qualitative, or “intuitive” factors and we prioritized the objectively superior, larger candidates. Only after months of negotiations failed with those companies did we do the deal with the smaller company. It turned out to be an excellent, productive partnership.
This is the third of four posts about this book. The final post is about the superiority of algorithm-driven interview techniques.
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