I suspect it all comes down to modeling of outcome distributions. If there’s a narrow path to success, then both biases are harmful. If there are a lot of ways to win, and a few disasters, then optimism bias is very harmful, as it makes the agent not loss-averse enough. If there are a lot of ways to win a little, and few ways to win a lot, then pessimism bias is likely to miss the big wins, as it’s trying to avoid minor losses.
I’d really enjoy an analysis focused on your conditions (maximize vs satisfice, world symmetry) - especially what kinds of worlds and biased predictors lead satisficing to get better outcomes than optimizing.
I suspect it all comes down to modeling of outcome distributions. If there’s a narrow path to success, then both biases are harmful. If there are a lot of ways to win, and a few disasters, then optimism bias is very harmful, as it makes the agent not loss-averse enough. If there are a lot of ways to win a little, and few ways to win a lot, then pessimism bias is likely to miss the big wins, as it’s trying to avoid minor losses.
I’d really enjoy an analysis focused on your conditions (maximize vs satisfice, world symmetry) - especially what kinds of worlds and biased predictors lead satisficing to get better outcomes than optimizing.