I’ve made the diagram assuming equal average impact whether someone is in the ‘community’ or ‘network’ but even if you doubled or tripled the average amount of impact you think someone in the community has there would still be more overall impact in the network.
People in EA regularly talk about the most effective community members having 100x or 1000x the impact of a typical EA-adjacent person, with impact following a power law distribution. For example, 80k attempts to measure “impact-adjusted significant plan changes” as a result of their work, where a “1“ is a median GWWC pledger (which is already more commitment than a lot of EA-adjacent people, who are curious students or giving-1% or something, so maybe 0.1). 80k claims credit for dozens of “rated 10” plan changes per year, a handful of “rated 100” per year, and at least one “rated 1000” (see p15 of their 2018 annual report here).
I’m personally skeptical of some of the assumptions about future expected impact 80k rely on when making these estimates, and some of their “plan changes” are presumably by people who would fall under “network” and not “community” in your taxonomy. (Indeed on my own career coaching call with them they said they thought their coaching was most likely to be helpful to people new to the EA community, though they think it can provide some value to people more familiar with EA ideas as well.) But it seems very strange for you to anchor on a 1-3x community vs network impact multiplier, without engaging with core EA orgs’ belief that 100x-10000x differences between EA-adjacent people are plausible.
The 1-3X and 10⁄90 percent are loosely held assumptions. I think it may be more accurate to assume there are power law distributions for people who would consider themselves in the community and also for those who are in the wider network. If both groups have a similar distribution, than the network probably has an order of magnitude more people who have 100x-1000x impact. Some examples include junior members of the civil service being quite involved in EA, but there are also senior civil servants and lots of junior civil servants who are interested in EA but don’t attend meetups.
I’m not sure that it is a core EA org belief that the difference is down to whether someone is heavily engaged in the community or not. Lots of examples they use of people who have had a much larger impact come from before EA was a movement.
It seems unlikely that the distribution of 100x-1000x impact people is *exactly* the same between your “network” and “community” groups, and if it’s even a little bit biased towards one or the other the groups would wind up very far from having equal average impact per person. I agree it’s not obvious which way such a bias would go. (I do expect the community helps its members have higher impact compared to their personal counterfactuals, but perhaps e.g. people are more likely to join the community if they are disappointed with their current impact levels? Alternatively, maybe everybody else is swamped by the question of which group you put Moskovitz in?) However assuming the multiplier is close to 1 rather than much higher or lower seems unwarranted, and this seems to be a key question on which the rest of your conclusions more or less depend.
Just a quick clarification that 80k plan changes aim to measure 80k’s counterfactual impact, rather than the expected lifetime impact of the people involved. A large part of the spread is due to how much 80k influenced them vs. their counterfactual.
People in EA regularly talk about the most effective community members having 100x or 1000x the impact of a typical EA-adjacent person, with impact following a power law distribution. For example, 80k attempts to measure “impact-adjusted significant plan changes” as a result of their work, where a “1“ is a median GWWC pledger (which is already more commitment than a lot of EA-adjacent people, who are curious students or giving-1% or something, so maybe 0.1). 80k claims credit for dozens of “rated 10” plan changes per year, a handful of “rated 100” per year, and at least one “rated 1000” (see p15 of their 2018 annual report here).
I’m personally skeptical of some of the assumptions about future expected impact 80k rely on when making these estimates, and some of their “plan changes” are presumably by people who would fall under “network” and not “community” in your taxonomy. (Indeed on my own career coaching call with them they said they thought their coaching was most likely to be helpful to people new to the EA community, though they think it can provide some value to people more familiar with EA ideas as well.) But it seems very strange for you to anchor on a 1-3x community vs network impact multiplier, without engaging with core EA orgs’ belief that 100x-10000x differences between EA-adjacent people are plausible.
The 1-3X and 10⁄90 percent are loosely held assumptions. I think it may be more accurate to assume there are power law distributions for people who would consider themselves in the community and also for those who are in the wider network. If both groups have a similar distribution, than the network probably has an order of magnitude more people who have 100x-1000x impact. Some examples include junior members of the civil service being quite involved in EA, but there are also senior civil servants and lots of junior civil servants who are interested in EA but don’t attend meetups.
I’m not sure that it is a core EA org belief that the difference is down to whether someone is heavily engaged in the community or not. Lots of examples they use of people who have had a much larger impact come from before EA was a movement.
It seems unlikely that the distribution of 100x-1000x impact people is *exactly* the same between your “network” and “community” groups, and if it’s even a little bit biased towards one or the other the groups would wind up very far from having equal average impact per person. I agree it’s not obvious which way such a bias would go. (I do expect the community helps its members have higher impact compared to their personal counterfactuals, but perhaps e.g. people are more likely to join the community if they are disappointed with their current impact levels? Alternatively, maybe everybody else is swamped by the question of which group you put Moskovitz in?) However assuming the multiplier is close to 1 rather than much higher or lower seems unwarranted, and this seems to be a key question on which the rest of your conclusions more or less depend.
Just a quick clarification that 80k plan changes aim to measure 80k’s counterfactual impact, rather than the expected lifetime impact of the people involved. A large part of the spread is due to how much 80k influenced them vs. their counterfactual.