Meticulous but expensive heuristics can be negative value compared to sloppy but cheap heuristics for many applications; you might be better off making a biased decision about laundry detergent since you can use that time and energy to better effect elsewhere.
Hold on, computationally cheap is not the same as biased. If you just want to get some laundry detergent and get out in a hurry, you can just go ahead and pick the familiar one (provided it’s not too expensive) on the grounds that it’s a safe decision and that the payoff of additional investigation will probably not be worth it.
There’s no bias there, provided your confidence in your result is proportional to how well you investigated the question (i.e. from a brand being familiar, conclude “It’s a pretty safe decision to buy this brand”, not “This is the best brand of detergent”).
Hold on, computationally cheap is not the same as biased.
You’re right; I’m being sloppy with the word “bias,” partly to make that point. The heuristics I’m interested in for that section aren’t heuristics that are merely efficient, but heuristics that sacrifice accuracy for computational cheapness. “Just buy whatever you got last time” is not a good recipe for getting good detergent, because it doesn’t even ask how satisfied you were with the detergent! But when you’re in a hurry, increases in detergent quality are less valuable than the time it would take to get them.
Other options for sloppy but cheap heuristics would be things like “buy the detergent closest to the start of the aisle” or “buy the detergent with the prettiest packaging”- things that I would be willing to call “biased” in most senses of the word but wouldn’t call heuristics with negative instrumental value.
The heuristics I’m interested in for that section aren’t heuristics that are merely efficient, but heuristics that sacrifice accuracy for computational cheapness.
Ok, I see what you mean. Sounds like the difference between a bad bias and a good bias is whether the person realizes they’re sacrificing accuracy, and is consistent in dealing with the consequences of that.
Or maybe a better way to put it is: bad biases lose accuracy, good biases only lose precision.
Hold on, computationally cheap is not the same as biased. If you just want to get some laundry detergent and get out in a hurry, you can just go ahead and pick the familiar one (provided it’s not too expensive) on the grounds that it’s a safe decision and that the payoff of additional investigation will probably not be worth it.
There’s no bias there, provided your confidence in your result is proportional to how well you investigated the question (i.e. from a brand being familiar, conclude “It’s a pretty safe decision to buy this brand”, not “This is the best brand of detergent”).
You’re right; I’m being sloppy with the word “bias,” partly to make that point. The heuristics I’m interested in for that section aren’t heuristics that are merely efficient, but heuristics that sacrifice accuracy for computational cheapness. “Just buy whatever you got last time” is not a good recipe for getting good detergent, because it doesn’t even ask how satisfied you were with the detergent! But when you’re in a hurry, increases in detergent quality are less valuable than the time it would take to get them.
Other options for sloppy but cheap heuristics would be things like “buy the detergent closest to the start of the aisle” or “buy the detergent with the prettiest packaging”- things that I would be willing to call “biased” in most senses of the word but wouldn’t call heuristics with negative instrumental value.
Ok, I see what you mean. Sounds like the difference between a bad bias and a good bias is whether the person realizes they’re sacrificing accuracy, and is consistent in dealing with the consequences of that.
Or maybe a better way to put it is: bad biases lose accuracy, good biases only lose precision.
That is a beautiful way to put it.