Conflating expected value estimates with effectiveness estimates. There is a difference between a 50% chance to save 10 children, and a 100% chance to save 5 children. Estimates sometimes don’t make a clear distinction.
I understand these are two different things, but am wondering exactly what problems you are seeing this equivocation causing. Is this a risk-aversion issue?
Yes, the distinction is important for people who want to make sure they had at least some impact (I’ve met some people like that). Also, after reading GiveWell’s CEA, you might be tempted to say “I donated $7000 to AMF so I saved two lives.” Interpreting their CEA this way would be misleading, even if it’s harmless. Maybe you saved 0, maybe you saved 4 (or maybe it’s more complicated because AMF, GiveWell, and whoever invented bednets should get some credit for saving those lives as well, etc.).
Another related problem is that probabilities in CEAs are usually subjective Bayesian probabilities. It’s important to recognize that such probabilities are not always on equal footing. E.g., I remember how people used to say things like “I think this charity has at least 0.000000001% chance of saving the world. If I multiply by how many people I expect to ever live… Oh, so it turns out that it’s way more cost-effective than AMF!” I think that this sort of reasoning is important but it often ignores the fact that the 0.000000001% probability is not nearly as robust as probabilities GiveWell uses. Hence you are more likely to fall for the Optimizer’s Curse. In other words, choosing between AMF and the speculative charity here feels choosing between eating at a restaurant with one 5 star Yelp review and eating at a restaurant with 200 Yelp reviews averaging 4.75 star (wording stolen from Karnofsky (2016). I’d choose the latter restaurant.
Also, an example where the original point came up in practice can be seen in this comment.
Thanks for writing this.
Could you give an example of this one, please?
I understand these are two different things, but am wondering exactly what problems you are seeing this equivocation causing. Is this a risk-aversion issue?
Yes, the distinction is important for people who want to make sure they had at least some impact (I’ve met some people like that). Also, after reading GiveWell’s CEA, you might be tempted to say “I donated $7000 to AMF so I saved two lives.” Interpreting their CEA this way would be misleading, even if it’s harmless. Maybe you saved 0, maybe you saved 4 (or maybe it’s more complicated because AMF, GiveWell, and whoever invented bednets should get some credit for saving those lives as well, etc.).
Another related problem is that probabilities in CEAs are usually subjective Bayesian probabilities. It’s important to recognize that such probabilities are not always on equal footing. E.g., I remember how people used to say things like “I think this charity has at least 0.000000001% chance of saving the world. If I multiply by how many people I expect to ever live… Oh, so it turns out that it’s way more cost-effective than AMF!” I think that this sort of reasoning is important but it often ignores the fact that the 0.000000001% probability is not nearly as robust as probabilities GiveWell uses. Hence you are more likely to fall for the Optimizer’s Curse. In other words, choosing between AMF and the speculative charity here feels choosing between eating at a restaurant with one 5 star Yelp review and eating at a restaurant with 200 Yelp reviews averaging 4.75 star (wording stolen from Karnofsky (2016). I’d choose the latter restaurant.
Also, an example where the original point came up in practice can be seen in this comment.