In the early stages of the PhD, you work to ‘reduce the problem space’. Define the problem using the smallest number of steps, variables or data points. This shows you the scope of your study. Then elaborate the problem in as many contexts as you think it applies. This shows you the applicability of it. Most of our advice boils down to ways of doing both. The term comes from computer science. The initial problem state and solutions to it are partly unknown. The algorithm is designed to propose potential solution states which are selected according to a heuristic.

All research involves a heuristic: a way of engaging with the research material or context that captures some essence about it. Sela-Smith (2002) characterises it as tied to phenomenological research where the research seeks discovery by recreating the research subject’s experience. My view – one shared with her – is that a heuristic approach is involved throughout any kind of research or theory. In qualitative research it is more usually tied to the self, and therefore becomes more apparent. However it is quite typical in other methods too. A theorist uses a heuristic reading of his or her subject matter. A computer scientist using human in the loop machine learning is doing the same. As Sela-Smith says, heuristic scholars can be found everywhere research is being done. They have some notable qualities, not afraid to take risks, or recommit to learning throughout their lives. Much of this is quiet though, taking place often in places that are not on the list of high status academic achievements.

Sela-Smith outlines six aspects of the heuristic approach: ‘indwelling, tacit knowing, intuition, self-dialogue, focusing, and the internal frame of reference’. I word this as consistent self reflection, developing a feel for things/getting skin in the game, sparking understanding, creating the internal conversation, living with the problem and knowing the problem set. Framing it as I have pulls it away from Sela-Smith’s focus on the self so it changes the meaning and quality of the heuristic, so you should look her article to understand it. Each element has a related activity: ‘initial engagement, immersion, incubation, illumination, explication, and creative synthesis.’ Some of these tasks will be very familiar to the PhD researcher.  Each can be broken down into sub-tasks which contribute to the whole. For example, developing a taxonomy can be part of incubation. Creative synthesis is an activity that I have seen some PhDs do very successfully. They use methods such as creating fictions from the data in the form of narratives of what was, is and might be.

The problem space approach might seem to work against creativity as it focuses on producing defined solution states. However if we see the solution state as being a product of the creative process then it fits better. One different I have with the heuristic approach is that it tends to focus on the individual, rather than the researcher as embedded within a community. Researchers might feel the individual qualities of the heuristic process as being demanding or involving risks e.g of self-alienation. I can see risks in piling requirements on the emotional labour of the PhD. This is where having a creative community comes in, where the work is held within the group rather than being a lonely labour of love. Computer science has a set of explicit protocols to do this but social science does not so that is something we can learn about.

Sela-Smith S (2002) Heuristic Research: A Review and Critique of Moustakas’s Method. Journal of Humanistic Psychology 42(3). SAGE Publications Inc: 53–88. DOI: 10.1177/0022167802423004.