One possible solution is to have applicants create a prediction market on their chance of getting a job/grant, before applying—this helps grant applicants get a sense of how good their prospects are. (example 1, 2) Of course, there’s a cost to setting up a market and making the relevant info legible to traders, but it should be a lot less than the cost of writing the actual application.
Another solution I’ve been entertaining is to have grantmakers/companies screen applications in rounds, or collaboratively, such that the first phase of application is very very quick (eg “drop in your Linkedin profile and 2 sentences about why you’re a good fit”).
I’d be interested in seeing some organizations try out the very very quick method. Heck, I’d be willing to help set it up and trial run it. My rough/vague perception is that a lot of the information in a job application is superfluous.
I also remember Ben West posting some data about how a variety of “how EA is this person” metrics held very little predictive value in his own hiring rounds.
One possible solution is to have applicants create a prediction market on their chance of getting a job/grant, before applying—this helps grant applicants get a sense of how good their prospects are. (example 1, 2) Of course, there’s a cost to setting up a market and making the relevant info legible to traders, but it should be a lot less than the cost of writing the actual application.
Another solution I’ve been entertaining is to have grantmakers/companies screen applications in rounds, or collaboratively, such that the first phase of application is very very quick (eg “drop in your Linkedin profile and 2 sentences about why you’re a good fit”).
I’d be interested in seeing some organizations try out the very very quick method. Heck, I’d be willing to help set it up and trial run it. My rough/vague perception is that a lot of the information in a job application is superfluous.
I also remember Ben West posting some data about how a variety of “how EA is this person” metrics held very little predictive value in his own hiring rounds.