At the current point of my PhD research, I believe I now have a broad view of the field, a plan outline for my research, have tackled with practical work, exchanged ideas with other researchers and now looking to define my specific research questions or problems. Further than that, I am preparing to start writing my upgrade I keep thinking about the whole process as whole that needs a successful conclusion.
One article that I was lucky to see passing before my eyes when I was beginning my PhD at UCP was “How to Choose a Good Scientific Problem” by Uri Alon. The title seems rather prescriptive but the analysis that it presents is highly enlightening.
The starting point of the article is that choosing a problem is, just as the culture of a specific lab, related to nurturing. When choosing a problem, both for a lab or for an individual researcher or student, the goal is maximising their potential by fostering growth and self-motivated research.
For that, Alon frames scientific problems in two dimensions: feasibility and interest. Feasibility reports to 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 to 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.
Some heuristics are provided that attempt to give students a more wise, defensive stance; “Do not commit to a problem before 3 months have elapsed” (whilst reading, discussing an 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.
The author departs to analyse how the ranking of problems occurs. Here, the value assigned by the community competes with the value, with the inner voice from the student or researcher. And a special mention is made to the importance of the supportive environment that the supervisors can provide and how much this helps to strengthen this inner voice. And how recurrent questions that go around inside for years can make the basis of good projects, how the self-motivation that 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 his problems? This way the author puts it reminded me of the old adage “Know thyself”. What are the personal interests, what is our perspective on a specific problem, what resonates with one’s values to explore? Achieving self-expression is one of the most important goals in research that may make work self-driven and revitalising.
On the concluding part of the paper, Alon focus on the schema of research, a path that is taken from beginning of research (A) to a particular end (B), and that is erroneously believed to be linear and predefined by most. In fact, in most of the cases the destination of research has been a newly found problem (C) in the way to solve the initial destination problem (B). In the course of a fuzzy stage called meandering of research, C became more interesting, feasible and worthwhile than that. As Alon puts it, the mentors’ task “is to support students through the cloud that seems to guard the entry to the unknown”.