Previously I talked about discovering that my basic unit of inquiry should be questions, not books. But what I didn’t talk about was how to generate those questions, and how to separate good questions from bad. That’s because I don’t know yet; my own process is mysterious and implicit to me. But I can give a few examples.
For any given question, your goal is to disambiguate it into smaller questions that, if an oracle gave you the answers to all of them, would allow you to answer the original question. Best case scenario, you repeat this process and hit bedrock, an empirical question for which you can find accurate data. You feed that answer into the parent question, and eventually it bubbles up to answering your original question.
That does not always happen. Sometimes the question is one of values, not facts. Sometimes sufficient accurate information is not available, and you’re forced to use a range- an uncertainty that will bubble up through parent answers. But just having the questions will clarify your thoughts and allow you to move more of your attention to the most important things.
Here are a few examples. First, a reconstructed mind map of my process that led to several covid+economics posts. In the interests of being as informative as possible, this one is kind of stylized and uses developments I didn’t have at the time I actually did the research.
If you’re curious about the results of this, the regular recession post is here and the oil crisis post is here.
Second, a map I created but have not yet researched, on the cost/benefit profile of a dental cleaning while covid is present.
Aside: Do people prefer the horizontal or vertical displays? Vertical would be my preference, but Whimsical does weird things with spacing so the tree ends up with a huge width either way.
Honestly this post isn’t really done; I have a lot more to figure out when it comes to how to create good questions. But I wanted to have something out before I published v0.1 of my Grand List of Steps, so here we are.
Many thanks to Rosie Campbell for inspiration and discussion on this idea.
One recommendation: the goal is not just to break a question into smaller questions sufficient to answer the original. The goal is to carve reality at the joints; to structure the search for answers in a way which reflects the structure of reality. This is useful mainly because it allows re-use and generalization of intermediate answers.
In a lot of simple cases, it’s fairly obvious how to structure questions—e.g. query “probability of catching covid when doing X”, rather than “probability that covid came from X given covid”, even though either one might technically be sufficient information for a super-query.
The hard/interesting cases are those where the right break-down is nonobvious. For these cases, remember that it’s useful to carve reality at the joints because it allows re-use/generalization of intermediate answers. Reversing that idea suggests a strategy for generating good intermediate questions: rather than starting from one goal-question, start from several related goal-questions, and then look for intermediate-questions which shed light on multiple goals. Rather than a tree, the question-breakdown should look like a DAG. By forcing intermediate questions to support multiple use-cases, we naturally push ourselves to look for questions which will have general relevance.