The last of the ideas from Daniel Kahneman’s book, Thinking, Fast and Slow, I’m going to write up is the superiority of algorithm-driven interview techniques in personnel selection. This is related to his thinking about when to trust intuition I wrote up in a previous post.
Kahneman says that hundreds of studies have been completed comparing the effectiveness of clinical vs. statistical predictions of long-term mental health in psychology. Sixty percent show statistical prediction is better; the remainder show no difference, but statistical prediction is much cheaper.
Kahneman believes, therefore, that for predictions in similarly “noisy” environments (he calls them “low-validity”)—such as personnel decisions—we should also use algorithms rather than intuitions to make our selections. He thinks algorithms are superior for four reasons:
First, a structured, algorithmic process ensures interviewers collect the information that is most important for predicting future performance. In a typical free-form interview, interviewers may not collect all the relevant information as they are tempted to direct the interview to areas that are most personally interesting to them.
Interviewers also try to consider more factors than an algorithm, but simplicity usually turns out to be better than complexity. Furthermore, humans are inconsistent, evaluating the same information differently at different times. Finally, interviewers are overly confident in their intuitions (consider that feedback is slow or non-existent in many interview-type situations).
Kahneman’s advice for personnel selection, therefore, is as follows:
- Select a few factors that are prerequisites for success (up to six, as independent as possible)
- Make a list of questions that explore the extent to which a candidate possesses the key success factors
- Score candidates in each area (on, say, a scale of 1-5)
- Add up the scores
- Hire the candidates with the highest overall scores
Several years ago I had to restructure and rebuild a team in Asia and I used just this approach. It helped me determine who I wanted to be sure to keep (my stars, if you will), who was ‘OK’ but needed some additional training or experience to really shine, and who was unlikely to be successful. As a result, I able to coach this third group into other roles and make sure my stars knew they were loved. Just as importantly, the explicitness of the criteria helped me navigate the bureaucracy that often comes with personnel matters in large organizations. Finally, I knew exactly what I was looking for when looking for new additions to the team.
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