I would say the square in the number of participants is too extreme, since the average attendee probably wouldn’t meet many more people than otherwise, except for those who wouldn’t have gotten to attend at all.
(EDIT, nvm this bit; I don’t know how to strike it out via mobile: Plus, because people are going to meet based on interests, if you were thinking about the number of possible meetings, I think it would be better to think about it like multiple cliques doubling in size than a single large clique doubling in size, or something more complicated.)
The first 500 (except those hiring?) probably wouldn’t get much more out of it, since it at most only slightly adds to who they might have met in terms of counterfactual value, and they might even get less, since they need to compete with the new 500 over meetings with the first 500. Then the next 500 at least get to meet each other, but also the first 500, and especially the (I assume) roughly fixed number of organizations that are hiring.
The first 500 is also plausibly made up of many people who are already largely in contact with one another because they work at EA-related orgs, either the same org, or orgs working in the same area who review each other’s work, strategize together or collaborate.
Around 2x seems plausible to me, but my best guess is less than 2x.
Sorry, I think you could arrive at 2x for bayesian reasons (like weighing multiple models), but I just wanted to push back on the model that an event with twice as many attendees should be straightforwardly modeled as twice as valuable.
I agree that it’s not straightforward that a linear model is approximately correct. I do think a linear model could still be approximately correct for straightforward linear reasons, like the value being roughly proportional to the number of one-on-ones, though, and not just because you weighed multiple models together and it happened to come out to about 2x.
I would say the square in the number of participants is too extreme, since the average attendee probably wouldn’t meet many more people than otherwise, except for those who wouldn’t have gotten to attend at all.
(EDIT, nvm this bit; I don’t know how to strike it out via mobile: Plus, because people are going to meet based on interests, if you were thinking about the number of possible meetings, I think it would be better to think about it like multiple cliques doubling in size than a single large clique doubling in size, or something more complicated.)
The first 500 (except those hiring?) probably wouldn’t get much more out of it, since it at most only slightly adds to who they might have met in terms of counterfactual value, and they might even get less, since they need to compete with the new 500 over meetings with the first 500. Then the next 500 at least get to meet each other, but also the first 500, and especially the (I assume) roughly fixed number of organizations that are hiring.
The first 500 is also plausibly made up of many people who are already largely in contact with one another because they work at EA-related orgs, either the same org, or orgs working in the same area who review each other’s work, strategize together or collaborate.
Around 2x seems plausible to me, but my best guess is less than 2x.
Sorry, I think you could arrive at 2x for bayesian reasons (like weighing multiple models), but I just wanted to push back on the model that an event with twice as many attendees should be straightforwardly modeled as twice as valuable.
I agree that it’s not straightforward that a linear model is approximately correct. I do think a linear model could still be approximately correct for straightforward linear reasons, like the value being roughly proportional to the number of one-on-ones, though, and not just because you weighed multiple models together and it happened to come out to about 2x.