Writing a good research paper or thesis is hard. It can be very intimidating to figure out the scope of the work needed to be done. Thanks to Oscar, I have been empowered with three adjectives to think about problems of scope. These adjectives were: breadth, depth and completeness.
Breadth and Depth
At the early stage of my thesis, I prepared a list of action items that I proposed to address for my research. I went to see Oscar for a confirmation that addressing these points would lead to a good thesis. I described him the list of action items and asked “Oscar, if I realize all this, will this make a good thesis?”. He look at me and said “For a good thesis, you must cover a topic with sufficient breadth and depth“. I came back to my office, confused and worried.
What I had failed to understand is that a thesis needs a frame. A frame has a breadth, and depth. The breadth characterizes the perspectives on the idea, and the depth the level of details. Say, your thesis is about dynamic updates. Technical feasibility and user adoption are two perspectives that belong to the breadth dimension. Implementation and formalization are different levels of details that belong to the depth dimension.
Different pieces of work have different ratios of breadth and depth. A position paper might have lots of breadth, but little depth. A paper proposing an optimization of an algorithm has little breadth, but lots of depth. For a thesis, you need a good ratio of both.
Later on, I was once invited to prepare an extended version of a conference paper for a journal. I prepared a draft of the changes and went to see Oscar. I asked “Oscar, are my changes enough for an extended journal paper?”. He looked at me and said “For a journal paper, you must do what is necessary for your research to be complete“. I came back to my office, confused and worried.
What I had failed to understand is that maturity of research is not defined by how much has been done in terms of effort, but by the actual amount of speculation left. Research is an incremental process, and a piece of research is complete when all that was needed to support the claim (results or analysis) has been provided. Let’s imagine that you have an implementation of a dynamic update algorithm that you claim is efficient. You must show efficiency in speed and memory use for the research to be complete.
Thinking in terms of breadth, depth and completeness has become a simple technique in my toolkit of planning methods.