Avoiding the Pitfalls of Expected Value and Finding a Balanced Perspective • Expected value can produce very large numbers that may dominate other models but may not be robust or reliable. • Sandboxing different frameworks or tools can provide a more comprehensive view and help assess the solidity of expected value. • Considering multiple perspectives and not relying solely on one set of assumptions can prevent reaching repugnant conclusions.
Spencer Greenberg
Agree. And expected value can have this tendency to produce like really large numbers in some circumstances where you’re like, well, it’s so big, it should trump all the other models because It just sort of dominates them. But then it’s like, hmm, that’s not very robust. It’s a little bit worrisome that it can spit out such a big number that it doesn’t matter. All the other considerations are irrelevant.
Joey Savoie
With that is by sandboxing. So you can epistemically say, okay, I have these three clusters of ways I look at the world. Say one is expected value framework. Say one is like expert wisdom framework. And say one is like a multifactored model where I put weights on different things, but don’t multiply them together. I sum score them or whatever. Three different tools that you’re using to view the world. And you want to look for something that looks solid on at least 80% of the tools that you’re using. I think that can kind of bound expected value in a way that it’s like, okay, no matter how good this expected value is, if it’s looking bad on every other front, I probably shouldn’t do It. And I think in general, that’s probably quite a good move in life. You end up at some really weird repugnant conclusions if you’re resting too heavily on one set of assumptions.
Spencer Greenberg
Yeah,
