I’m working on Impact Markets – markets to trade nonexcludable goods.
If you’re also interested in less directly optimific things – such as climbing around and on top of boulders or amateurish musings on psychology – then you may enjoy some of the posts I don’t cross-post from my blog, Impartial Priorities.
Pronouns: Ideally they or she, but the others are fine too. I also go by Dawn now.
Very cool formalization! What do you think of the following way of applying it:
Hiring managers that are looking for similar candidates meet (e.g., online) to hash out a single standardized application process for all the similar open positions.
When the applications are in and they have narrowed them down to the set candidates any of them find at all interesting, they start the process.
Is there a strong reason why they would need to agree on relative impact scores for their organizations? I imagine they’ll find it hard to agree on those. Maybe they can just assume a vector of [1, 1, 1, …] for all the impact scores?
They add more pseudo-organizations to the mix, which could have an impact of −100 in spaces like AGI (representing the average non-safety AGI lab) or 0 in most other spaces. (They don’t have control over the choice between other orgs, so I don’t think it makes sense to add several different values, but there need to be as many pseudo-organizations as there are candidates, in case all organizations decide not to hire.)
They generate the candidate-organization matrix but also include columns for “no candidate at org n,” because not hiring is also an option. (“No candidate” can “work” for multible or all organizations in parallel, so this gets a bit complicated.)
I think in most cases they can assume that everyone will be full time, which could simplify the process in those cases.
Then they pick the row that maximizes the impact.
Step 5 seems like the one that’ll require a lot more work in practice. There could be an independent team of forecasters that does the estimation or all hiring managers estimate this for all orgs, or the hiring manages of org A makes the case for/against each candidate at org A, and then all other hiring manages estimate the impact.
There could be the risk that if one org was wrong and their eventual pick soon quits or doesn’t do a good job, that then the remaining allocation is not optimal anymore because that person would’ve been a good fit at another org that now doesn’t have capacity to hire them anymore.