Suppose we made a charity evaluator based on Statistical Prediction Rules, which perform pretty well.
Is that just vanilla linear regression?
Diversification then decreases payoff for such cheating; sufficient diversification can make it economically non viable for selfish parties to fake the signals.
Even without cheating, evaluation is still problematic:
Suppose you have a formula that computes the expected marginal welfare (QUALYs, etc.) of a charity given a set of observable variables. You run it on a set of charities and it the two top charities get a very close score, one slightly greater than the other. But the input variables all affected by noise, and the formula contains several approximations, so you perform error propagation analysis and it turns out that the difference between these scores is within the margin of error.
Should you still donate everything to the top scoring charity even if you know that the decision is likely based on noise?
Should you still donate everything to the top scoring charity even if you know that the decision is likely based on noise?
If the charities are this close then you only expect to do very slightly better by giving only to the better scoring one. So it doesn’t matter much whether you give to one, the other, or both.
Ideally, you run your charity-evaluator function on huge selection of charities, and the one for which your charity-evaluator function gives the largest value, is in some sense the best, regardless of the noise.
More practically, imagine an imperfect evaluation function that due to a bug in it’s implementation multiplies by a Very Huge Number value of a charity whose description includes some string S which the evaluation function mis-processes in some dramatic way. Now, if the selection of charities is sufficiently big as to include at least one charity with such S in it’s description, you are essentially donating at random. Or worse than random, because the people that run in their head the computation resulting in production of such S tend to not be the ones you can trust.
Normally, I would expect people who know about human biases to not assume that evaluation would resemble the ideal and to understand that the output of some approximate evaluation will not have the exact properties of expected value.
Is that just vanilla linear regression?
Even without cheating, evaluation is still problematic:
Suppose you have a formula that computes the expected marginal welfare (QUALYs, etc.) of a charity given a set of observable variables. You run it on a set of charities and it the two top charities get a very close score, one slightly greater than the other. But the input variables all affected by noise, and the formula contains several approximations, so you perform error propagation analysis and it turns out that the difference between these scores is within the margin of error. Should you still donate everything to the top scoring charity even if you know that the decision is likely based on noise?
If the charities are this close then you only expect to do very slightly better by giving only to the better scoring one. So it doesn’t matter much whether you give to one, the other, or both.
Systematic errors are the problem.
Ideally, you run your charity-evaluator function on huge selection of charities, and the one for which your charity-evaluator function gives the largest value, is in some sense the best, regardless of the noise.
More practically, imagine an imperfect evaluation function that due to a bug in it’s implementation multiplies by a Very Huge Number value of a charity whose description includes some string S which the evaluation function mis-processes in some dramatic way. Now, if the selection of charities is sufficiently big as to include at least one charity with such S in it’s description, you are essentially donating at random. Or worse than random, because the people that run in their head the computation resulting in production of such S tend to not be the ones you can trust.
Normally, I would expect people who know about human biases to not assume that evaluation would resemble the ideal and to understand that the output of some approximate evaluation will not have the exact properties of expected value.