(This is a long post. If you’re going to read only part, please read sections 1 and 2, subsubsection 5.6.2, and the conclusion.)
1. Introduction
Suppose you want to give some money to charity: where can you get the most bang for your philanthropic buck? One way to make the decision is to use explicit expected value estimates. That is, you could get an unbiased (averaging to the true value) estimate of what each candidate for your donation would do with an additional dollar, and then pick the charity associated with the most promising estimate.
Holden Karnofsky of GiveWell, an organization that rates charities for cost-effectiveness, disagreed with this approach in two posts he made in 2011. This is a response to those posts, addressing the implications for existential risk efforts.
According to Karnofsky, high returns are rare, and even unbiased estimates don’t take into account the reasons why they’re rare. So in Karnofsky's view, our favorite charity shouldn’t just be one associated with a high estimate, it should be one that supports the estimate with robust evidence derived from multiple independent lines of inquiry.1 If a charity’s returns are being estimated in a way that intuitively feels shaky, maybe that means the fact that high returns are rare should outweigh the fact that high returns were estimated, even if the people making the estimate were doing an excellent job of avoiding bias.
Karnofsky’s first post, Why We Can’t Take Expected Value Estimates Literally (Even When They’re Unbiased), explains how one can mitigate this issue by supplementing an explicit estimate with what Karnofsky calls a “Bayesian Adjustment” (henceforth “BA”). This method treats estimates as merely noisy measures of true values. BA starts with a prior representing what cost-effectiveness values are out there in the general population of charities, then the prior is updated into a posterior in standard Bayesian fashion.
Karnofsky provides some example graphs, illustrating his preference for robustness. If the estimate error is small, the posterior lies close to the explicit estimate. But if the estimate error is large, the posterior lies close to the prior. In other words, if there simply aren’t many high-return charities out there, a sharp estimate can be taken seriously, but a noisy estimate that says it has found a high-return charity must represent some sort of fluke.
Karnofsky does not advocate a policy of performing an explicit adjustment. Rather, he uses BA to emphasize that estimates are likely to be inadequate if they don’t incorporate certain kinds of intuitions — in particular, a sense of whether all the components of an estimation procedure feel reliable. If intuitions say an estimate feels shaky and too good to be true, then maybe the estimate was noisy and the prior is more important. On the other hand, if intuitions say an estimate has taken everything into account, then maybe the estimate was sharp and outweighs the prior.
Karnofsky’s second post, Maximizing Cost-Effectiveness Via Critical Inquiry, expands on these points. Where the first post looks at how BA is performed on a single charity at a time, the second post examines how BA affects the estimated relative values of different charities. In particular, it assumes that although the charities are all drawn from the same prior, they come with different estimates of cost-effectiveness. Higher estimates of cost-effectiveness come from estimation procedures with proportionally higher uncertainty.
It turns out that higher estimates aren’t always more auspicious: an estimate may be “too good to be true,” concentrating much of its evidential support on values that the prior already rules out for the most part. On the bright side, this effect can be mitigated via multiple independent observations, and such observations can provide enough evidence to solidify higher estimates despite their low prior probability.
Charities aiming to reduce existential risk have a potential claim to high expected returns, simply because of the size of the stakes. But if such charities are difficult to evaluate, and the prior probability of high expected values is low, then the implications of BA for this class of charities loom large.
This post will argue that competent efforts to reduce existential risk reduction are still likely to be optimal, despite BA. The argument will have three parts:
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BA differs from fully Bayesian reasoning, so that BA risks double-counting priors.
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The models in Karnofsky’s posts, when applied to existential risk, boil down to our having prior knowledge that the claimed returns are virtually impossible. (Moreover, similar models without extreme priors don’t lead to the same conclusions.)
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We don’t have such prior knowledge. Extreme priors would have implied false predictions in the past, imply unphysical predictions for the future, and are justified neither by our past experiences nor by any other considerations.
Claim 1 is not essential to the conclusion. While Claim 2 seems worth expanding on, it’s Claim 3 that makes up the core of the controversy. Each of these concerns will be addressed in turn.
Before responding to the claims themselves, however, it’s worth discussing a highly simplified model that will illustrate what Karnofsky’s basic point is.
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