I’m going to write a relatively long comment making a relatively narrow objection to your post. Sorry about that, but I think it’s a particularly illustrative point to make. I disagree with these two points against the neglectedness framing in particular:
that it could divide by zero, and this is a serious problem
that it splits a fraction into unnecessarily conditional parts (the “dragons in Westeros” problem).
Firstly in response to (1), this is a legitimate illustration that the framework only applies where it applies, but it seems like in practice like it isn’t an obstacle. Specifically, the framing works well when your proposed addition is small relative to the existing resource, and it seems like that’s true of most people in most situations. I’ll come back to this later.
More importantly, I feel like (2) misses the point of what the framework was developed for. The goal is to get a better handle on what kinds of things to look for when evaluating causes. So the fact that the fraction simplifies to “good done per additional resource” is sort of trivial – that’s the goal, the metric we’re trying to optimize. It’s hard to measure that directly, so the value added by the framework is the claim that certain conditionalizations of the metric (if that’s the right word) yield questions that are easier to answer, and answers that are easier to compare.
That is, we write it as “scale times neglectedness times solvability” because we find empirically that those individual factors of the metric tend to be more predictable, comparable and measurable than the metric as a whole. The applicability of the framework is absolutely contingent on what we in-practice discover to be the important considerations when we try to evaluate a cause from scratch.
So while there’s no fundamental reason why neglectedness, particularly as measured in the form of the ratio of percentage per resource, needs to be a part of your analysis, it just turns out to be the case that you can often find e.g. two different health interventions that are otherwise very comparable in how much good they do, but with very different ability to consume extra resources, and that drives a big difference in their attractiveness as causes to work on.
If ever you did want to evaluate a cause where the existing resources were zero, you could just as easily swap the bad cancellative denominator/numerator pair with another one, say the same thing in absolute instead of relative terms, and the rest of the model would more or less stand up. Whether that should be done in general for evaluating other causes as well is a judgement call about how these numbers vary in practice and what situations are most easily compared and contrasted.
I’m going to write a relatively long comment making a relatively narrow objection to your post. Sorry about that, but I think it’s a particularly illustrative point to make. I disagree with these two points against the neglectedness framing in particular:
that it could divide by zero, and this is a serious problem
that it splits a fraction into unnecessarily conditional parts (the “dragons in Westeros” problem).
Firstly in response to (1), this is a legitimate illustration that the framework only applies where it applies, but it seems like in practice like it isn’t an obstacle. Specifically, the framing works well when your proposed addition is small relative to the existing resource, and it seems like that’s true of most people in most situations. I’ll come back to this later.
More importantly, I feel like (2) misses the point of what the framework was developed for. The goal is to get a better handle on what kinds of things to look for when evaluating causes. So the fact that the fraction simplifies to “good done per additional resource” is sort of trivial – that’s the goal, the metric we’re trying to optimize. It’s hard to measure that directly, so the value added by the framework is the claim that certain conditionalizations of the metric (if that’s the right word) yield questions that are easier to answer, and answers that are easier to compare.
That is, we write it as “scale times neglectedness times solvability” because we find empirically that those individual factors of the metric tend to be more predictable, comparable and measurable than the metric as a whole. The applicability of the framework is absolutely contingent on what we in-practice discover to be the important considerations when we try to evaluate a cause from scratch.
So while there’s no fundamental reason why neglectedness, particularly as measured in the form of the ratio of percentage per resource, needs to be a part of your analysis, it just turns out to be the case that you can often find e.g. two different health interventions that are otherwise very comparable in how much good they do, but with very different ability to consume extra resources, and that drives a big difference in their attractiveness as causes to work on.
If ever you did want to evaluate a cause where the existing resources were zero, you could just as easily swap the bad cancellative denominator/numerator pair with another one, say the same thing in absolute instead of relative terms, and the rest of the model would more or less stand up. Whether that should be done in general for evaluating other causes as well is a judgement call about how these numbers vary in practice and what situations are most easily compared and contrasted.