Deborah Estrin on Top and Bottom versus Middle

Deborah Estrin is a computer science professor at UCLA. Commenting on my recent post Top and Bottom versus Middle: Schools, China, Health? she said “amen to that”.

I asked her why she agreed. Because she sees the same thing a lot, she said. In particular, performance metrics are often devised by people in the middle, and those metrics tend to serve their interests — and not the interests of everyone else. She gave three examples: 1. Fee for service. Doctors are paid per office visit and per surgery, for example. The bad effects of this are obvious. For example, surgeons are pushed to recommend ill-advised surgeries. 2. Financial instruments, such as derivatives. They were sold to outsiders as ways to reduce risk but we all now know they had the opposite effect. As Michael Lewis puts it, “extremely smart traders inside Wall Street investment banks devise deeply unfair, diabolically complicated bets, and then send their sales forces out to scour the world for some idiot who will take the other side of those bets.” 3. Publications. Professors are rated and promoted and to some extent paid based on how many publications they produce. This pushes them toward “safe” projects that are likely to produce a publication within a reasonable time and away from harder, more important problems.

When you measure yourself you can use whatever metric you want — and thereby a metric that serves your interests.



One Reply to “Deborah Estrin on Top and Bottom versus Middle”

  1. A friend who recently got her Ph.D. through the Berkeley psych dept describes how in her “professional seminar” a distinguished professor in the dept came and gave a talk and explained how by the time they got their Ph.D.s they needed to have about 7 publications. At least one should be in X journal; another should be in Y journal; the others should be in A, B, and C journals; etc.

    She loves doing psychological research and exploring ideas, but she found this rather shattering; it took some of the joy out of the process and biases the kinds of research people are willing to do…

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