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.
1. Sure, standardized application process for similar positions but also for their diverse set. Because a candidate’s impact in one type of role should be compared to that in another role type. 2. So, maybe there should be two ‘rounds’ of recommendations: one ‘broad’ to e. g. prevent people who would do relatively poorly in a policy think tank applying there and vice versa and then when there are final candidates for all similar policy think tank positions, there can be further estimation of best fit into specific roles. 3 and 4. Sure, that makes sense: since EA-related orgs communicate, impact could be assumed the same but some EA-unrelated orgs could have even negative or neutral impact. 5. I think that if an organization does not hire then it is just all zeros in a row in the FTE matrix. Multiple rows can have all zeros—if multiple orgs do not hire. 6) Yes, that would make the calculation easier—because only one one could be in one row and column in the candidate-org matrix. Then, for the no hiring of some orgs, it would be at most one 1 in row or column. 7) You mean if candidates are in rows and orgs in columns then the hiring managers pick for each candidate the row that maximizes their impact? (in that row there would be one for the org the candidate should work for)
Probably if each hiring manager estimates maybe top 5 candidates and their relative impact would be the easiest? Oh yes :) that is a risk but maybe hiring managers should include uncertainty which would complicate things … or should hire everyone for a trial period and then reevaluate (which could be somewhat unpopular since some candidates could change jobs ).
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.
Thank you :)
1. Sure, standardized application process for similar positions but also for their diverse set. Because a candidate’s impact in one type of role should be compared to that in another role type. 2. So, maybe there should be two ‘rounds’ of recommendations: one ‘broad’ to e. g. prevent people who would do relatively poorly in a policy think tank applying there and vice versa and then when there are final candidates for all similar policy think tank positions, there can be further estimation of best fit into specific roles. 3 and 4. Sure, that makes sense: since EA-related orgs communicate, impact could be assumed the same but some EA-unrelated orgs could have even negative or neutral impact. 5. I think that if an organization does not hire then it is just all zeros in a row in the FTE matrix. Multiple rows can have all zeros—if multiple orgs do not hire. 6) Yes, that would make the calculation easier—because only one one could be in one row and column in the candidate-org matrix. Then, for the no hiring of some orgs, it would be at most one 1 in row or column. 7) You mean if candidates are in rows and orgs in columns then the hiring managers pick for each candidate the row that maximizes their impact? (in that row there would be one for the org the candidate should work for)
Probably if each hiring manager estimates maybe top 5 candidates and their relative impact would be the easiest? Oh yes :) that is a risk but maybe hiring managers should include uncertainty which would complicate things … or should hire everyone for a trial period and then reevaluate (which could be somewhat unpopular since some candidates could change jobs ).