Thanks Milan—I probably should have been a bit more detailed in my summary.
Here are the main issues I see:
-The optimizer’s curse is an underappreciated threat to those who prioritize among causes and programs that involve substantial, poorly understood uncertainty.
-I think EAs are unusually prone to wrong-way reductions: a fallacy where people try to solve messy, hard problems with tidy, formulaic approaches that actually create more issues than they resolve.
--I argue that trying to turn all uncertainty into something like numeric probability estimates is a wrong-way reduction that can have serious consequences.
--I argue that trying to use Bayesian methods in situations where well-ground priors are unavailable is often a wrong-way reduction. (For what it’s worth, I rarely see EAs actually deploy these Bayesian methods, but I often see people suggest that the proper approaches in hard situations involve “making a Bayesian adjustments.” In many of these situations, I’d argue that something closer to run-of-the-mill critical thinking beats Bayesianism.)
-I think EAs sometimes have an unwarranted bias towards numerical, formulaic approaches over less-quantitative approaches.
Thanks Milan—I probably should have been a bit more detailed in my summary.
Here are the main issues I see:
-The optimizer’s curse is an underappreciated threat to those who prioritize among causes and programs that involve substantial, poorly understood uncertainty.
-I think EAs are unusually prone to wrong-way reductions: a fallacy where people try to solve messy, hard problems with tidy, formulaic approaches that actually create more issues than they resolve.
--I argue that trying to turn all uncertainty into something like numeric probability estimates is a wrong-way reduction that can have serious consequences.
--I argue that trying to use Bayesian methods in situations where well-ground priors are unavailable is often a wrong-way reduction. (For what it’s worth, I rarely see EAs actually deploy these Bayesian methods, but I often see people suggest that the proper approaches in hard situations involve “making a Bayesian adjustments.” In many of these situations, I’d argue that something closer to run-of-the-mill critical thinking beats Bayesianism.)
-I think EAs sometimes have an unwarranted bias towards numerical, formulaic approaches over less-quantitative approaches.