As I understand it, the actual problem in this area is not so much that “satisficers want to become maximisers”—but rather that a simple and effective means of satisficing fairly often involves constructing a maximising resource-gathering minion. Then after the satisficer is satisfied, the minion(s) may continue unsupervised—unless care is taken. I discussed this issue in my 2009 essay on the topic.
The expected utility of option 1 is higher than the threshold for the satisficer, so it could just as easily pick 1) as 2); it’ll be indifferent between the two choices, and will need some sort of tie-breaker.
But inasmuch as it will want to want one over the other, it will want to want 2 which is guaranteed to continue to satisfice over 1 which has only a 90% chance of continuing to satisfice, so it should not want to become a maximizer.
So that’s actually the “bounded utility” definition, which Stuart says he isn’t using. It does seem more intuitive though… I think you can get a paradox out of Stuart’s definition, actually, which should not be surprising, since it isn’t a utility-maximizer.
A satisficer is not motivated to continue to satisfice. It is motivated to take an action that is a satisficing action, and 1) and 2) are equally satisficing.
I know what you’re trying to do, I think. I tried to produce a “continuously satisficing agent” or “future satisficing agent”, but couldn’t get it to work out.
Option 1) already satisfies. Taking option 1) brings the expected utility up above the threshold, so the satisficer is done.
If you add the extra requirement that the AI must never let the expected utility fall below the threshold in future, then the AI will simply blind itself or turn itself off, once the satisficing level is reached; then its expected utility will never fall, as no extra information ever arrives.
Right, the satisficer will not have an incentive to increase its expected utility by becoming a maximizer when its expected utility (by remaining a satisficer) is already over the threshold. But surely this condition would fail frequently.
I don’t think this follows. Consider the case where there’s two choices:
1) 10% chance of no paperclips, 90% chance of 3^^^3 paperclips 2) 100% chance of 20 paperclips
The maximizer will likely pick 1, while the satisficer will definitely prefer 2.
As I understand it, the actual problem in this area is not so much that “satisficers want to become maximisers”—but rather that a simple and effective means of satisficing fairly often involves constructing a maximising resource-gathering minion. Then after the satisficer is satisfied, the minion(s) may continue unsupervised—unless care is taken. I discussed this issue in my 2009 essay on the topic.
The expected utility of option 1 is higher than the threshold for the satisficer, so it could just as easily pick 1) as 2); it’ll be indifferent between the two choices, and will need some sort of tie-breaker.
But inasmuch as it will want to want one over the other, it will want to want 2 which is guaranteed to continue to satisfice over 1 which has only a 90% chance of continuing to satisfice, so it should not want to become a maximizer.
So that’s actually the “bounded utility” definition, which Stuart says he isn’t using. It does seem more intuitive though… I think you can get a paradox out of Stuart’s definition, actually, which should not be surprising, since it isn’t a utility-maximizer.
A satisficer is not motivated to continue to satisfice. It is motivated to take an action that is a satisficing action, and 1) and 2) are equally satisficing.
I know what you’re trying to do, I think. I tried to produce a “continuously satisficing agent” or “future satisficing agent”, but couldn’t get it to work out.
Surey option 1 has a 10% chance of failing to satisfy.
Option 1) already satisfies. Taking option 1) brings the expected utility up above the threshold, so the satisficer is done.
If you add the extra requirement that the AI must never let the expected utility fall below the threshold in future, then the AI will simply blind itself or turn itself off, once the satisficing level is reached; then its expected utility will never fall, as no extra information ever arrives.
Sorry—a failure to reread the question on my part :-(
Right, the satisficer will not have an incentive to increase its expected utility by becoming a maximizer when its expected utility (by remaining a satisficer) is already over the threshold. But surely this condition would fail frequently.
If it isn’t over the threshold, it could just keep making the same decisions a maximizer would.