Long post over at GiveWell about relying on expected value estimates for charitable donations. The main thrust is that large expected values with high variance carry very little weight when combined with a proper prior. LessWrong is referenced heavily throughout.
We believe that people in this group are often making a fundamental mistake, one that we have long had intuitive objections to but have recently developed a more formal (though still fairly rough) critique of. The mistake (we believe) is estimating the “expected value” of a donation (or other action) based solely on a fully explicit, quantified formula, many of whose inputs are guesses or very rough estimates. We believe that any estimate along these lines needs to be adjusted using a “Bayesian prior”; that this adjustment can rarely be made (reasonably) using an explicit, formal calculation; and that most attempts to do the latter, even when they seem to be making very conservative downward adjustments to the expected value of an opportunity, are not making nearly large enough downward adjustments to be consistent with the proper Bayesian approach.
This view of ours illustrates why - while we seek to ground our recommendations in relevant facts, calculations and quantifications to the extent possible - every recommendation we make incorporates many different forms of evidence and involves a strong dose of intuition. And we generally prefer to give where we have strong evidence that donations can do a lot of goodrather than where we have weak evidence that donations can do far more good - a preference that I believe is inconsistent with the approach of giving based on explicit expected-value formulas (at least those that (a) have significant room for error (b) do not incorporate Bayesian adjustments, which are very rare in these analyses and very difficult to do both formally and reasonably).
I'm particularly interested in anyone more familiar with bayesian stats who can shed more light on the validity of Givewell's approach here.
http://blog.givewell.org/2011/08/18/why-we-cant-take-expected-value-estimates-literally-even-when-theyre-unbiased/
Long post over at GiveWell about relying on expected value estimates for charitable donations. The main thrust is that large expected values with high variance carry very little weight when combined with a proper prior. LessWrong is referenced heavily throughout.
I'm particularly interested in anyone more familiar with bayesian stats who can shed more light on the validity of Givewell's approach here.