How To Choose a Good Scientific Problem
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Choosing good problems is essential for being a good scientist. But what is a good problem, and how do you choose one? The subject is not usually discussed explicitly within our profession. Scientists are expected to be smart enough to figure it out on their own and through the observation of their teachers. This lack of explicit discussion leaves a vacuum that can lead to approaches such as choosing problems that can give results that merit publication in valued journals, resulting in a job and tenure.
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This article is a gem, a gift.
Every phrase, every section, filled me with pure joy. It is the most straight-to-the-heart (or to the "faint inner voice") paper I've ever read. It takes courage to publicly claim "don't go for the easy and uninteresting" (nor for the hard and uninteresting, by the way), "take your time" (the three-month rule for new students/post-docs is what I've always tried to do, in an unconscious way), "hear your inner voice" (and your recurrent ideas, instead of what others consider interesting), "expect the unexpected" (the author's "cloud" meets Heraclitus), especially in a context where research is driven, time shrinks and knowledge (as well as students, papers, etc.) becomes a commodity.
A fifteen-minute break that may change your life, just by reading what you already almost knew but were afraid to follow.
Thank you, Uri.
Nice advice for the researchers.
Good article for aspiring scientists. I especially like the feasibility-interest diagram to rate and categorize projects.
Excellent article, but the author misses one important thing. The axes in Figure 1 are not separable or independent dimensions. Feasibility and interest interact with each other. In summary, he forgets that "hard" problems are potentially more interesting than "easy" problems. This is not a value judgement, this is because there is a general human and system bias (as he would agree) to avoid hard problems and go for the "low hanging fruit." Even if we just think about this statistically, that means you're more likely to encounter an interesting problem in the "hard" category than in the "easy" category because it seems obvious that people will go for the easy interesting problems first. This is my general experience of research. It is a very lucky day when you stumble into an interesting problem that is relatively easy to tackle (I have had the good fortune on only 2 occasions in my life). The norm in my experience is that if I want to do something that is really interesting, it is going to be extremely hard to get a handle on - that was the experience I had doing my dissertation. Not that this deters me, but just to note that the system really does not support tackling the interesting hard problems and if you don't run into easy interesting problems, or settle for easy uninteresting problems, tenure could be in jeopardy...
A nice article that every researcher should read.
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