# CuSithBell comments on A plan for Pascal's mugging? - Less Wrong Discussion

1 04 June 2012 09:04AM

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Comment author: 04 June 2012 03:56:29PM *  2 points [-]

But the median outcome is losing 5 utils?

Edit: Oh, wait! You mean the median total utility after some other stuff happens (with a variance of more than 5 utils)?

Suppose we have 200 agents, 100 of which start with 10 utils, the rest with 0. After taking this offer, we have 51 with -5, 51 with 5, 49 with 10000, and 49 with 10010. The median outcome would be a loss of -5 for half the agents, a gain of 5 for half, but only the half that would lose could actually get that outcome...

And what do you mean by "the possibility of getting tortured will manifest itself only very slightly at the 50th percentile"? I thought you were restricting yourself to median outcomes, not distributions? How do you determine the median distribution?

Comment author: 04 June 2012 10:11:51PM 0 points [-]

And what do you mean by "the possibility of getting tortured will manifest itself only very slightly at the 50th percentile"? I thought you were restricting yourself to median outcomes, not distributions? How do you determine the median distribution?

I don't. I didn't write that.

Your formulation requires that there be a single, high probability event that contributes most of the utility an agent has the opportunity to get over its lifespan. In situations where this is not the case (e.g. real life), the decision agent in question would choose to take all opportunities like that.

The closest real-world analogy I can draw to this is the decision of whether or not to start a business. If you fail (which there is a slightly more than 50% chance you will), you are likely to be in debt for quite some time. If you succeed, you will be very rich. This is not quite a perfect analogy, because you will have more than one chance in your life to start a business, and the outcomes of business ownership are not orders of magnitude larger than the outcomes in real life. However, it is much closer than the "51% chance to lose \$5, 49% chance to win \$10000" that your example intuitively brings to mind.

Comment author: 05 June 2012 02:54:02PM 0 points [-]

Ah! Sorry for the mixed-up identities. Likewise, I didn't come up with that "51% chance to lose \$5, 49% chance to win \$10000" example.

But, ah, are you retracting your prior claim about a variance of greater than 5? Clearly this system doesn't work on its own, though it still looks like we don't know A) how decisions are made using it or B) under what conditions it works. Or in fact C) why this is a good idea.

Certainly for some distributions of utility, if the agent knows the distribution of utility across many agents, it won't make the wrong decision on that particular example by following this algorithm. I need more than that to be convinced!

For instance, it looks like it'll make the wrong decision on questions like "I can choose to 1) die here quietly, or 2) go get help, which has a 1/3 chance of saving my life but will be a little uncomfortable." The utility of surviving presumably swamps the rest of the utility function, right?

Comment author: 05 June 2012 03:29:56PM 1 point [-]

Ah, it appears that I'm mixing up identities as well. Apologies.

Yes, I retract the "variance greater than 5". I think it would have to be variance of at least 10,000 for this method to work properly. I do suspect that this method is similar to decision-making processes real humans use (optimizing the median outcome of their lives), but when you have one or two very important decisions instead of many routine decisions, methods that work for many small decisions don't work so well.

If, instead of optimizing for the median outcome, you optimized for the average of outcomes within 3 standard deviations of the median, I suspect you would come up with a decision outcome quite close to what people actually use (ignoring very small chances of very high risk or reward).

Comment author: 05 June 2012 06:25:39PM 0 points [-]

This all seems very sensible and plausible!