This post massively increased my confidence in GiveWell’s opinions by asking and answering intelligent, relevant questions very clearly.
The post would be even stronger if it addressed Carl Shulman’s concern about how to handle non-tiny likelihoods that a charity is more than 10x better than, say, the United Way of Massachusetts. Log-normal distributions would help, but I think sometimes an initial estimate provides evidence that a charity is either extremely effective (>3X) or not very effective (<0). E.g. if I tell you that my charity has invented a safe and effective vaccine for HIV that can be manufactured and distributed for $20 a dose, and is currently seeking funds to distribute the vaccine, I am probably either (a) lying, (b) woefully misinformed, (c) contributing to Malthusian doom, or (d) running an absurdly effective charity. These four options probably account for the vast majority of the probability distribution. I would expect the amount of probability left over to be tiny—the probability that i have instead invented, e.g., a risky and sometimes effective vaccine for HIV that can be manufactured and distributed for $4,000 per dose is pretty small. For this kind of situation, it wouldn’t make sense to model the probability as continuously declining above 0.5X—you would want most of the probability to hover around 0, and a little bit of probability to hover around 3X (or whatever figure you would adopt if my claims proved to be correct). There would be some probability at X and 2X—but not much; the distribution would have two peaks, not one peak.
This post massively increased my confidence in GiveWell’s opinions by asking and answering intelligent, relevant questions very clearly.
The post would be even stronger if it addressed Carl Shulman’s concern about how to handle non-tiny likelihoods that a charity is more than 10x better than, say, the United Way of Massachusetts. Log-normal distributions would help, but I think sometimes an initial estimate provides evidence that a charity is either extremely effective (>3X) or not very effective (<0). E.g. if I tell you that my charity has invented a safe and effective vaccine for HIV that can be manufactured and distributed for $20 a dose, and is currently seeking funds to distribute the vaccine, I am probably either (a) lying, (b) woefully misinformed, (c) contributing to Malthusian doom, or (d) running an absurdly effective charity. These four options probably account for the vast majority of the probability distribution. I would expect the amount of probability left over to be tiny—the probability that i have instead invented, e.g., a risky and sometimes effective vaccine for HIV that can be manufactured and distributed for $4,000 per dose is pretty small. For this kind of situation, it wouldn’t make sense to model the probability as continuously declining above 0.5X—you would want most of the probability to hover around 0, and a little bit of probability to hover around 3X (or whatever figure you would adopt if my claims proved to be correct). There would be some probability at X and 2X—but not much; the distribution would have two peaks, not one peak.