Posted on March 29, 2008 by Peter Turney
I once saw a graph that plotted scientific productivity as a function of the scientist’s age, with different curves for different scientific fields. I remember that the curve for mathematics peaked between the ages of 20 and 30, but the curve for chemistry peaked somewhere around 50. There was no curve for AI researchers, and I occasionally wonder where such a curve might peak. This ties into the neats versus scruffies debate. The neats probably peak early, whereas the scruffies peak late. A good strategy would be to be neat early in life, and switch to scruffy later. Today I went searching for a copy of that graph. I didn’t find it (let me know if you can find it), but I found a lot of interesting studies on the topic.
Dean Simonton has been doing research in this area for a long time, but I am troubled by his emphasis on genius. It seems to me that the concept of genius is closely connected to the heroic theory of scientific development. Instead of trying to measure genius, I would rather focus on productivity, measured by number of publications. There is too much room for subjective bias in a study of genius.
Satoshi Kanazawa has made a bit of a splash with his theory that young male scientists, like young male criminals, make an extra effort in their chosen fields, in order to attract mates. Once they get married, their productivity immediately drops. I am not convinced by his study.
Svein Kyvik finds that productivity in the social sciences is not particularly sensitive to age, but productivity declines with age in the natural sciences. He claims that older scientists cannot keep up with the fast pace of progress in the natural sciences, but progress is slower in the social sciences. I am skeptical.
Paul Allison and John Stewart suggest that there is a kind of feedback effect in a scientist’s career, where success leads to greater productivity and thus greater success, but lack of success leads to lower productivity and thus less success. Therefore there is more variability in the productivity of older scientists than in the productivity of younger scientists. This complicates the analysis of how age affects productivity. Looking at the average productivity can be misleading; we need to look at the variance of the productivity distribution.
Hall, Mairesse, and Turner examine the problem of separating the effect of a scientist’s age from the effect of the time period in which a scientist lives. We would like to be able to compare scientists who have the same age in a certain time period, but were born in different time periods, but this is difficult to arrange. If competitiveness increases steadily with time, then older scientists may appear less productive, but it will be due to the time period in which they worked, rather than biological or psychological effects of aging. Most studies do not attempt to separate these two factors.
My conclusion: more research is needed.