Through Didier and Nielsen, I found a list of Golden Rules for Successful Scientific Research attributed to Dijkstra.
- “Raise your quality standards as high as you can live with, avoid wasting your time on routine problems, and always try to work as closely as possible at the boundary of your abilities. Do this, because it is the only way of discovering how that boundary should be moved forward.”
- “We all like our work to be socially relevant and scientifically sound. If we can find a topic satisfying both desires, we are lucky; if the two targets are in conflict with each other, let the requirement of scientific soundness prevail.”
- “Never tackle a problem of which you can be pretty sure that (now or in the near future) it will be tackled by others who are, in relation to that problem, at least as competent and well-equipped as you.”
Of the three rules, only the last one seems important. The second one appears self-evident: you want to be socially relevant, but not to the point of producing low quality work. This being said, most researchers go to the other extreme and ignore social relevance and their work loses out its motivation. If you tackle a problem that only you care about, don’t expect much recognition. I actually disagree with the first rule: small problems, technical issues actually often hide interesting problems. Always focusing on the management and top level issue is a bad idea I think. Michelangelo was painting a church! In research, do not be so quick to think that there are noble and not-so-noble problems. All problems can be interesting and knowledge of technical issues can bring much insight.
One thought on “The Three Dijkstra Rules for Successful Scientific Research”
I think so. But for the last rule, we usually
don’t know which problem will be solved by
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