As a researcher, there’s kinda a stack of “what I’m trying to do”, from the biggest picture to the most microscopic task. Like here’s a typical “stack trace” of what I might be doing on a random morning:
LEVEL 5: I’m trying to ensure a good future for life
LEVEL 1: …by reading a bunch of articles about the nucleus incertus
So as researchers, we face a practical question: How do we allocate our time between the different levels of the stack? If we’re 100% at the bottom level, we run a distinct risk of “losing the plot”, and working on things that won’t actually help advance the higher levels. If we’re 100% at the top level, with our head way up in the clouds, never drilling down into details, then we’re probably not learning anything or making any progress.
Obviously, you want a balance.
And I’ve found that striking that balance properly isn’t something that takes care of itself by default. Instead, my default is to spend too much time at the bottom of the stack and not enough time higher up.
So to counteract that tendency, I have for many months now had a practice of “Solve The Whole Problem Day”. That’s one day a week (typically Friday) where I force myself to take a break from whatever detailed things I would otherwise be working on, and instead I fly up towards the top of the stack, and try to see what I’m missing, question my assumptions, find new avenues to explore, etc.
In my case, “The Whole Problem” = “The Whole Safe & Beneficial AGI Problem”. For you, it might be The Whole Climate Change Problem, or The Whole Animal Suffering Problem, or The Whole Becoming A Billionaire Problem, or whatever. (If it’s not obvious how to fill in the blank, well then you especially need a Solve The Whole Problem Day! And maybe start here & here & here.)
It’s a great explication-plus-habit-implementation for “keeping your eye on the ball”. Clarifying one’s personal view of the “stack” also just seems good more broadly, cf. Dave Banerjee’s archetype of “a large fraction of [the] researchers in AI safety/governance fellowships [he’s had 1-1s with]”:
My guess is that spending time clarifying and re-clarifying the stack isn’t a dispositionally preferable thing for most folks who end up doing frontier-pushing research. Anecdotally, when I got interested in cost-effectiveness analysis for improving decision-making a few years ago and started reaching out to experts whose public work I respected, coming from a “business intelligence” corporate background where analyses were always in contact with all kinds of business decisions small-to-large and fast-turnaround operational to slow strategy, I was struck by the disparity between their obsessive interest in the research & analysis part and their diplomatically-couched near-indifference to how their analysis changed any decisions whatsoever. It was jarring; it made me decide not to be like them, or work in roles that incentivised this.
Your #2, and to a lesser extent #3, reminded me of Steve Byrnes’s Research productivity tip: “Solve The Whole Problem Day”, whose intro I sometimes share with friends:
It’s a great explication-plus-habit-implementation for “keeping your eye on the ball”. Clarifying one’s personal view of the “stack” also just seems good more broadly, cf. Dave Banerjee’s archetype of “a large fraction of [the] researchers in AI safety/governance fellowships [he’s had 1-1s with]”:
My guess is that spending time clarifying and re-clarifying the stack isn’t a dispositionally preferable thing for most folks who end up doing frontier-pushing research. Anecdotally, when I got interested in cost-effectiveness analysis for improving decision-making a few years ago and started reaching out to experts whose public work I respected, coming from a “business intelligence” corporate background where analyses were always in contact with all kinds of business decisions small-to-large and fast-turnaround operational to slow strategy, I was struck by the disparity between their obsessive interest in the research & analysis part and their diplomatically-couched near-indifference to how their analysis changed any decisions whatsoever. It was jarring; it made me decide not to be like them, or work in roles that incentivised this.
Thanks for sharing, I wasn’t aware of those posts from Steve Byrnes and Dave Banerjee, and they are quite on point!