Uri Alon's “How to Choose a Good Scientific Problem” review

“How to Choose a Good Scientific Problem” by Uri Alon presents a rather enlightening analysis and very good advice to both students and supervisors. The premise of the article is that choosing a scientific problem is closely related to nurturing, in that when choosing a problem, either for a PhD student, an individual researcher, or even a research lab, the goal should be to maximise their potential by fostering growth and self-motivated research.

The starting premise of the article is that choosing a problem is related to nurturing, just as much as the culture of a research lab is. When choosing a problem, either for a lab, for an individual researcher, or for a PhD student, the goal is to maximise their potential by fostering growth and self-motivated research.

To begin with, Alon frames scientific problems in two dimensions: feasibility and interest. Feasibility reports how hard/easy it is to complete a project, in what concerns time. Interest reports to “the amount in which they increase verifiable knowledge”. So considered options for positioning your research problems are: “low hanging fruit” — easy but not too interesting; “difficult is good” — difficult and low interest, and finally the best of options, feasible and with high interest. So choosing the right problem follows the Pareto principles according to an increasing level of difficulty and career development.

Alon provides a few heuristics that instil students with a wiser, more defensive stance; “Do not commit to a problem before 3 months have elapsed” (whilst reading, discussing and planning) or “Resist the urge to “we must produce — let’s not waste time and start working”, with the given consideration to practical issues that usually arise such as funding, deadlines, etc.

Alon also analyses how the ranking of problems usually occurs, where the value assigned by the community competes with the value from the students’ or researchers’ inner voice. Here, the author highlights the importance of a supportive environment that the supervisors can provide and how much this helps to strengthen that inner voice. He also suggests that recurrent questions which go around inside for years can make the basis of good projects. The level of self-motivation which emerges out of this can lead to a bigger commitment, a more rewarding routine, and a greater appeal to the audience.

So how can one converge towards their problems? The way the author puts it reminded me of the old “Know thyself” adage, or something that I have heard more recently “Research is (Me)search”. For instance, what are personal interests or perspectives on a given problem? What resonates with one’s values that is worthwhile to explore? Achieving self-expression is one of the most important goals in research that may make work self-driven and energising.

In the concluding part of the paper, Alon focuses on what he calls the ‘schema of research’, a path that is taken from a beginning point of research (A) to a particular endpoint (B), which is erroneously believed to be linear and predefined by most. In fact, in most of the cases, the destination of research is a newly found problem © in the way to solve the initial destination problem (B). In the course of a fuzzy stage called ‘meandering of research’, C becomes more interesting, feasible and worthwhile than B.

In this journey, as Alon puts it, the mentors’ task “is to support students through the cloud that seems to guard the entry to the unknown”.

Reference: Alon U. How to choose a good scientific problem. Mol Cell. 2009 Sep 24;35(6):726–8. DOI: 10.1016/j.molcel.2009.09.013. PMID: 19782018.