On Asking the Right Question

As I reviewed my earlier essay on how problems may be chosen for a thesis, the discussion turned to the issue of asking the right question (or set of questions) to frame a problem. The notion of asking the appropriate question is essential for the following potential reasons:

a) Facilitate self understanding: only by questioning something and analyzing the responses does one really understand (the basis of the Socratic method and a pre-cursor to the scientific method and empiricism)
b) Understanding other people’s work: especially during talks, in lectures in the classroom, reviewing papers (scientific or other wise), reviewing popular articles
c) Identifying new areas of further exploration: Actually as you ask questions, you realize we operate in world with large gaps in our understanding of things. As an example, take a look at the following state of work on the physics of coffee drops (You may think it is a well understood problem but many have built a career on it)
d) Communicating your own work and ideas: Many fields have developed a common terminology and lexicon to describing things and using the same to situate your work in context of the questions below is essential. Once you position your work this way, the audience can relate to you with a shared language and terminology and get to the essence of the conversation at the earliest.

The Basic Questions

Given the way science and philosophy have evolved (based on a reductionist viewpoint), there is actually a basic blueprint of essential questions one can ask which I list below. Reductionism is the idea that any phenomena (natural, artificial) can be subdivided into a set of conceptual components (which may/may not have physical instantiations and thus can be abstract), each such component can be studied in their individuality, and the behavior of the composite phenomena may be analyzed/explained/predicted in terms of the behavior of its components. Note that this analysis may be further applied to the “components” themselves in a recursive sort of way. This viewpoint has pervaded all of science (social, economic, natural, engineering, pure and applied). It is actually an efficient effective approach that has led to the advances in science and technology over the past four centuries (and actually was the basis of Greek, Hindu and Chinese philosophers). Anyways, this “conceptual underpinning” drives the following basic set of questions:

a) What is the system/phenomena of interest ? What is its “structure” ? What are its components ? What are the rules of interaction of the components ?
b) Is the “set of components” completely known ? Are there components not yet known ?
c) Is the behavior of the system completely explainable in terms of the component behaviors? what behaviors have been explained ? What have not been explained?
d) What are the behaviors of the system that have been studied ? Is this set of behaviors complete ?
e) What are the limiting conditions of the system ? When does it break ? When do its inputs/outputs stress the system?
f) What are the nominal conditions under which the system works properly ? What conditions stress it out ? and Why ? What happens when the system operates in a different regime ? (Remember in the realworld, all phenomena happen contemporaneously, not based on your models alone)
g) What resources does the system consume to operate effectively ? What happens when the resource is limited or in excess ?
h) How does the system interact with a neighbouring system ? How/why ?
j) How do collections of such systems work when they are in one single ensemble ? What if they are disparate ?
k) What are the basic experiments that have established these features/properties of the system? What was measured and how? Are tools available to measure them? What is the sensitivity?
l) Is there a “mathematical model” of the system that we can use to “predict behavior” ? why/why not ?
m) How does the system behavior over time ? short term – nanoseconds to seconds to minutes to hours
n) How does the system behave in different conditions of space, temperature, ambience, load, usage etc.
o) How can it be controlled to obtain “specific” behaviors? How do you keep it stable ? what happens when it fails ? Things can fail structurally (where the integrity of the structure/component fails) or behaviorally. What caused the failure ? Is the failure due to long-term cause/use or short-term? Was it a side-effect of other phenomena?

The above list assumes the “system” of interest is well-isolated. Usually there are a few things to keep in mind:

a) A “system” and its frame of reference (its boundary) in the physical world is a conceptual boundary for us to focus on things of interest. In the realworld, every physical entity is described by a multitude of models for different phenomena that may co-exist concurrently. The way to imagine this is: if you are going for your morning run – all the following apply to you – the law of gravitation keeps you on the road, friction helps you run, the laws of conservation of energy and momentum apply to you to control your speed, the laws of stress/strain guide the behavior of your limbs as they handle the impact of your footfalls etc.. You get the picture. For purposes of discussion on nutrition, we may only focus on one part, how much energy did you burn. Thus a number of “underlying assumptions”,  some explicit, some implicit guide the rest of the discussion. Elucidating and understand the assumptions is essential to any fruitful progress. The questions above have to asked regularly to establish the validity of these assumptions and there is nothing wrong in asking these questions.

b) Computational systems – such as software and the like – and other “symbol systems” are abstract creations – which exhibit similar conceptual structure but may not be bounded by similar laws – though they may have inherent laws of their own. They are unbounded in that sense. When physical systems are modelled computationally, one has to realize what assumptions one is making and what one is not (explicitly or implicitly). Any computational system has to be physically realized in a device – such as an abacus and depending on its tasks may be limited by the applicable physical laws.

c) Technological systems around us are a combination of the above two consisting of various components and interactions and include humans in the loop giving rise to immense complexity.

Concluding Remarks

Finally, the above framework also applies to the humanities (Actually creative writing and such exploit a set of stock characters, themes, tropes as components and basic motifs, journalism has its own rules etc.), history, social sciences, economics etc. Each  field however differs in its objective for setting up the framework, its rigor, its basic language to describe these primitives etc. but the underlying structure is sort of similar. Understanding this implicit structure of knowledge and armed with the stock list of questions above, one can meaningfully pursue on ones own effort to understand the world around us in our individual and collective terms.