It’s an old AI term meaning roughly “find a solution that isn’t (likely) optimal, but good enough for some purpose, without too much effort”. It implies that either your computer is too slow for it to be economical to find the true optimum under your models, or that you’re too dumb to come up with the right models, thus the popularity of the idea in AI research.
You can be impressed if someone starts with a criteria for what “good enough” means, and then comes up with a method they can prove meets the criteria. Otherwise it’s spin.
I’m more used to it as a psychology (or behavior econ) term for a specific, psychologically realistic, form of bounded rationality. In particular, I’m used to it being negative! (that is, a heuristic which often degenerates produces a bias)
It’s an old AI term meaning roughly “find a solution that isn’t (likely) optimal, but good enough for some purpose, without too much effort”. It implies that either your computer is too slow for it to be economical to find the true optimum under your models, or that you’re too dumb to come up with the right models, thus the popularity of the idea in AI research.
You can be impressed if someone starts with a criteria for what “good enough” means, and then comes up with a method they can prove meets the criteria. Otherwise it’s spin.
I’m more used to it as a psychology (or behavior econ) term for a specific, psychologically realistic, form of bounded rationality. In particular, I’m used to it being negative! (that is, a heuristic which often degenerates produces a bias)