Nod. So, first of all: I don’t know. My own guesses would depend on a lot of details of the individual person (even those in a similar situation to you).
(This feels somewhat outside the scope of the main thrust of this post, but, definitely related to my broader agenda of ‘figure out a training paradigm conveying the skills and tools necessary to solve very difficult, confusing problems’)
But, riffing so far. And first, summarizing what seemed like the key points:
You want to predict optimization daemons as they arise in a system, and want a good mathematical basis for that, and don’t feel satisfied with the existing tools.
You’re currently exploring this on-the-side while working on some more tractable problems.
You’ve identified two broad-strategies, which are:
somehow “deliberate practice” this,
somehow explore and follow your curiosity intermitently
Three things I’d note:
“Deliberate practice” is very openended. You can deliberate practice noticing and cultivating veins of curiosity, for example.
You can strategize about how to pursue curiosity, or explore (without routing through the practice angle)
There might be action-spaces other than “deliberate practice” or “explore/curiosity” that will turn out to be useful.
My current angle for deliberate practice is to find problem sets that feel somehow-analogous to the one you’re trying to tackle, but simpler/shorter. They should be difficult enough that they feel sort of impossible while you’re working on them, but, also actually solvable. They should be varied enough that you aren’t overfitting to one particular sort of puzzle.
After the exercise, apply the Think It Faster meta-exercise to it.
Part of the point here is notice strategies like “apply explicit systematic thinking” and strategies like “take a break, come back to it when you feel more inspired”, and start to develop your own sense of which strategies work best for you.
Nod. So, first of all: I don’t know. My own guesses would depend on a lot of details of the individual person (even those in a similar situation to you).
(This feels somewhat outside the scope of the main thrust of this post, but, definitely related to my broader agenda of ‘figure out a training paradigm conveying the skills and tools necessary to solve very difficult, confusing problems’)
But, riffing so far. And first, summarizing what seemed like the key points:
You want to predict optimization daemons as they arise in a system, and want a good mathematical basis for that, and don’t feel satisfied with the existing tools.
You’re currently exploring this on-the-side while working on some more tractable problems.
You’ve identified two broad-strategies, which are:
somehow “deliberate practice” this,
somehow explore and follow your curiosity intermitently
Three things I’d note:
“Deliberate practice” is very openended. You can deliberate practice noticing and cultivating veins of curiosity, for example.
You can strategize about how to pursue curiosity, or explore (without routing through the practice angle)
There might be action-spaces other than “deliberate practice” or “explore/curiosity” that will turn out to be useful.
My current angle for deliberate practice is to find problem sets that feel somehow-analogous to the one you’re trying to tackle, but simpler/shorter. They should be difficult enough that they feel sort of impossible while you’re working on them, but, also actually solvable. They should be varied enough that you aren’t overfitting to one particular sort of puzzle.
After the exercise, apply the Think It Faster meta-exercise to it.
Part of the point here is notice strategies like “apply explicit systematic thinking” and strategies like “take a break, come back to it when you feel more inspired”, and start to develop your own sense of which strategies work best for you.