I think you should add more uncertainty to your model around the value of an 80K career change (in both directions). While 1 impact-adjusted change is approximately the value of a GWWC pledge, that doesn’t mean it is equal in both mean and standard deviation as your model suggests, since the plan changes involve a wide variety of different possibilities.
It might be good to work with 80K to get some more detail about the kinds of career changes that are being made and try to model the types of career changes separately. Certainly, some people do take the GWWC pledge, and that is a change that is straightforwardly comparable with the value of the GWWC pledge (minus concerns about the counterfactual share of 80K), but other people make much higher-risk higher-reward career changes, especially in the 10x category.
Speaking just for me, in my personal view looking at a few examples of the 80K 10x category, I’ve found them to be highly variable (including some changes that I’d personally judge as less valuable than the GWWC pledge)… while this certainly is not a systematic analysis on my part, it would suggest your model should include more uncertainty than it currently does.
Lastly, I think your model right now assumes 80K has 100% responsibility for all their career changes. Maybe this is completely fine because 80K already weights their reported career change numbers for counterfactuality? Or maybe there’s some other good reason to not take this into account? I admit there’s a good chance I’m missing something here, but it would be nice to see it addressed more specifically.
Lastly, I think your model right now assumes 80K has 100% responsibility for all their career changes. Maybe this is completely fine because 80K already weights their reported career change numbers for counterfactuality? Or maybe there’s some other good reason to not take this into account? I admit there’s a good chance I’m missing something here, but it would be nice to see it addressed more specifically.
I don’t think that’s true, because the GWWC pledge value figures have been counterfactually adjusted, and because we don’t count all of the people we’ve influenced to take the GWWC pledge.
While 1 impact-adjusted change is approximately the value of a GWWC pledge, that doesn’t mean it is equal in both mean and standard deviation as your model suggests, since the plan changes involve a wide variety of different possibilities.
Agree with that—the standard deviation should be larger.
Peter, indeed your point #2 about uncertainty is what I discuss in the last point of “2) Outcome measures”, under “Model limitations”. I argued in a handwaving way that because 80K still causes some lower risk and lower return global health type interventions—which our aggregation model seems to favor, probably due to the Bayesian prior—it will probably still beat MIRI that focuses exclusively on high risk, high return things that the model seems to penalize. But yes we should have modeled it in this way.
I think you should add more uncertainty to your model around the value of an 80K career change (in both directions). While 1 impact-adjusted change is approximately the value of a GWWC pledge, that doesn’t mean it is equal in both mean and standard deviation as your model suggests, since the plan changes involve a wide variety of different possibilities.
It might be good to work with 80K to get some more detail about the kinds of career changes that are being made and try to model the types of career changes separately. Certainly, some people do take the GWWC pledge, and that is a change that is straightforwardly comparable with the value of the GWWC pledge (minus concerns about the counterfactual share of 80K), but other people make much higher-risk higher-reward career changes, especially in the 10x category.
Speaking just for me, in my personal view looking at a few examples of the 80K 10x category, I’ve found them to be highly variable (including some changes that I’d personally judge as less valuable than the GWWC pledge)… while this certainly is not a systematic analysis on my part, it would suggest your model should include more uncertainty than it currently does.
Lastly, I think your model right now assumes 80K has 100% responsibility for all their career changes. Maybe this is completely fine because 80K already weights their reported career change numbers for counterfactuality? Or maybe there’s some other good reason to not take this into account? I admit there’s a good chance I’m missing something here, but it would be nice to see it addressed more specifically.
I don’t think that’s true, because the GWWC pledge value figures have been counterfactually adjusted, and because we don’t count all of the people we’ve influenced to take the GWWC pledge.
More discussion here: https://80000hours.org/2016/12/has-80000-hours-justified-its-costs/#giving-what-we-can-pledges
Agree with that—the standard deviation should be larger.
Peter, indeed your point #2 about uncertainty is what I discuss in the last point of “2) Outcome measures”, under “Model limitations”. I argued in a handwaving way that because 80K still causes some lower risk and lower return global health type interventions—which our aggregation model seems to favor, probably due to the Bayesian prior—it will probably still beat MIRI that focuses exclusively on high risk, high return things that the model seems to penalize. But yes we should have modeled it in this way.