brainstorming / regurgitating some random additional ideas -
Goodhart’s law—a charity may from the outset design itself or self-modify itself around Effective Altruist metrics, thereby pandering to the biases of the metrics and succeeding in them despite being less Good than a charity which scored well on the same metrics despite no prior knowledge of them. (Think of the difference between someone who has aced a standardized test due to intentional practice and “teaching to the test” vs. someone who aced it with no prior exposure to standardized tests—the latter person may possess more of the quality that the test is designed to measure). This is related to “influencing charities” issue, but focusing on the potential for defeating of the metric itself, rather than direct effects of the influence.
Counterfactuals of donations (other than the matching thing)- a highly cost effective charity which can only pull from an effective altruist donor pool might have less impact than a slightly less cost effective charity which successfully redirects donations from people who wouldn’t have donated to a cost effective charity (this is more of an issue for the person who controls talent, direction, and other factors, not the person who controls money).
Model inconsistency—Two very different interventions will naturally be evaluated by two very different models, and some models may inherently be harsher or more lenient on the intervention than others. This will be true even if all the models involved are as good and certain as they can realistically be.
Regression to the mean—The expected value of standout candidates will generally regress to the mean of the pool from which they are drawn, since at least some of the factors which caused them to rise to the top will be temporary (including legitimate factors that have nothing to do with mistaken evaluations)
Good points. (Also, I believe am personally required to upvote posts that reference Goodhart’s law.)
But I think both regression to the mean and Goodhart’s law are covered, if perhaps too briefly, under the heading “Estimates based on past data might not be indicative of the cost-effectiveness in the future.”
brainstorming / regurgitating some random additional ideas -
Goodhart’s law—a charity may from the outset design itself or self-modify itself around Effective Altruist metrics, thereby pandering to the biases of the metrics and succeeding in them despite being less Good than a charity which scored well on the same metrics despite no prior knowledge of them. (Think of the difference between someone who has aced a standardized test due to intentional practice and “teaching to the test” vs. someone who aced it with no prior exposure to standardized tests—the latter person may possess more of the quality that the test is designed to measure). This is related to “influencing charities” issue, but focusing on the potential for defeating of the metric itself, rather than direct effects of the influence.
Counterfactuals of donations (other than the matching thing)- a highly cost effective charity which can only pull from an effective altruist donor pool might have less impact than a slightly less cost effective charity which successfully redirects donations from people who wouldn’t have donated to a cost effective charity (this is more of an issue for the person who controls talent, direction, and other factors, not the person who controls money).
Model inconsistency—Two very different interventions will naturally be evaluated by two very different models, and some models may inherently be harsher or more lenient on the intervention than others. This will be true even if all the models involved are as good and certain as they can realistically be.
Regression to the mean—The expected value of standout candidates will generally regress to the mean of the pool from which they are drawn, since at least some of the factors which caused them to rise to the top will be temporary (including legitimate factors that have nothing to do with mistaken evaluations)
Good points. (Also, I believe am personally required to upvote posts that reference Goodhart’s law.)
But I think both regression to the mean and Goodhart’s law are covered, if perhaps too briefly, under the heading “Estimates based on past data might not be indicative of the cost-effectiveness in the future.”