Literally yesterday I stumbled upon the obvious-in-retrospect idea that, if an agent selects/generates the plan it thinks is best, then it will tend to be optimistic about the plan it selects/generates, even if it would be unbiased about a random plan.
So, I wonder if this murphyjitsu idea could be in part related to that—the plan you generate is overoptimistic, but then you plan for how the uncertainty in the environment could lead your plan to fail, and now the same bias should overestimate the environment’s likelihood of thwarting you.
(Perhaps though this general agent planning bias idea is obvious to everyone though except me, and it’s just me who didn’t get it until I tried to mentally model how an idea for an AI would work in practice).
(also, it feels like even if this is part of human planning bias, it’s not the whole story, and a failure to be specific is a big part of what’s going on as noted in the post)
Scraping the memory barrel here… as I recall, the original “planning fallacy” research (or something near it) found that people’s time estimates were identical in the two scenarios:
“How long do you expect this to take?”
“How long do you expect this to take in the best-case scenario?”
The suggestion was much like what I think you’re saying here: that when people are forming plans, they create an ideal image of the plan.
Because of this, when I’d teach this CFAR class I’d sometimes describe the planning fallacy this way, as the thing that makes “Things didn’t go according to plan” never ever mean “Things went better than we expected!”
Literally yesterday I stumbled upon the obvious-in-retrospect idea that, if an agent selects/generates the plan it thinks is best, then it will tend to be optimistic about the plan it selects/generates, even if it would be unbiased about a random plan.
So, I wonder if this murphyjitsu idea could be in part related to that—the plan you generate is overoptimistic, but then you plan for how the uncertainty in the environment could lead your plan to fail, and now the same bias should overestimate the environment’s likelihood of thwarting you.
(Perhaps though this general agent planning bias idea is obvious to everyone though except me, and it’s just me who didn’t get it until I tried to mentally model how an idea for an AI would work in practice).
(also, it feels like even if this is part of human planning bias, it’s not the whole story, and a failure to be specific is a big part of what’s going on as noted in the post)
Scraping the memory barrel here… as I recall, the original “planning fallacy” research (or something near it) found that people’s time estimates were identical in the two scenarios:
“How long do you expect this to take?”
“How long do you expect this to take in the best-case scenario?”
The suggestion was much like what I think you’re saying here: that when people are forming plans, they create an ideal image of the plan.
Because of this, when I’d teach this CFAR class I’d sometimes describe the planning fallacy this way, as the thing that makes “Things didn’t go according to plan” never ever mean “Things went better than we expected!”