If I can be forgiven for tooting my own horn, I also wrote a forum post about the framework around the same time as John posted his. EAs have often talked about “cause prioritisation” as being distinct from “intervention evaluation”: the former is done in terms of ITN, the latter in term of cost-effectiveness. I agree with Ben Todd’s suggestion the best way to understand ITN is as three factors that combine to a calculation of cost-effectiveness (aka “good done per dollar”). One result of this that I think it’s confused to think that “cause prioritisation” and “intervention evaluation” are two different things. I discuss some implications of this.
Given a set of values, I see there as being multiple layers of heuristics, which are all useful to consider and make comparisons based on:
Yardsticks (e.g. x-risk, qualys)
Causes (e.g. AI alignment)
Interventions (e.g. research into the deployment problem)
Specific jobs /orgs (e.g. working at FHI)
Comparisons at all levels are all ultimately about finding proxies for expected value relative to your values.
The cause level abstraction seems to be especially useful for career planning (and grantmaking) since it helps you get career capital that builds up in a useful area. Intervention selection usually seems too brittle. Yardsticks are too broad. This post is pretty old but tries to give some more detail: https://80000hours.org/2013/12/why-pick-a-cause/
I think it’s probably the case that good heuristics for making career decisions are different than good heuristics for making donation decisions. We shouldn’t necessarily expect a framework (ITN or otherwise) to be ideal for both.
If someone today decides to work on a certain cause, they strengthen the pipeline of good funding opportunities in that cause. But there’s a time lag. Pivoting to work on biosecurity might be a great career decision right now. However funding a person to do that work might not be a great donation until a few years down the road, when they’ve gained the skills and credentials needed to make an impact.
If I can be forgiven for tooting my own horn, I also wrote a forum post about the framework around the same time as John posted his. EAs have often talked about “cause prioritisation” as being distinct from “intervention evaluation”: the former is done in terms of ITN, the latter in term of cost-effectiveness. I agree with Ben Todd’s suggestion the best way to understand ITN is as three factors that combine to a calculation of cost-effectiveness (aka “good done per dollar”). One result of this that I think it’s confused to think that “cause prioritisation” and “intervention evaluation” are two different things. I discuss some implications of this.
Given a set of values, I see there as being multiple layers of heuristics, which are all useful to consider and make comparisons based on:
Yardsticks (e.g. x-risk, qualys)
Causes (e.g. AI alignment)
Interventions (e.g. research into the deployment problem)
Specific jobs /orgs (e.g. working at FHI)
Comparisons at all levels are all ultimately about finding proxies for expected value relative to your values.
The cause level abstraction seems to be especially useful for career planning (and grantmaking) since it helps you get career capital that builds up in a useful area. Intervention selection usually seems too brittle. Yardsticks are too broad. This post is pretty old but tries to give some more detail: https://80000hours.org/2013/12/why-pick-a-cause/
I think it’s probably the case that good heuristics for making career decisions are different than good heuristics for making donation decisions. We shouldn’t necessarily expect a framework (ITN or otherwise) to be ideal for both.
If someone today decides to work on a certain cause, they strengthen the pipeline of good funding opportunities in that cause. But there’s a time lag. Pivoting to work on biosecurity might be a great career decision right now. However funding a person to do that work might not be a great donation until a few years down the road, when they’ve gained the skills and credentials needed to make an impact.