The majority of the work that this adjustment does is due to the assumption that an action with high variance is practically as likely to greatly harm as to greatly help, and thus the long positive tail is nearly canceled out by the long negative tail. For some Pascal’s Muggings (like the original Wager), this may be valid, but for others it’s not.
If Warren Buffett offers to play a hand of poker with you, no charge if he wins and he gives you a billion if he loses, some skepticism about the setup might be warranted- but to say “it would be better for me if we played for $100 of your money instead” seems preposterous. (Since in this case there’s no negative tail, an adjustment based only on mean and variance is bound to screw up.)
I don’t think this is true. I think the majority of the work that the adjustment does is due to the assumption that high value actions are extraordinarily unlikely.
It’s entirely possible that your expectation in situations when you think Buffett is offering $1b is smaller than your expectation in situations when you think Buffett is offering $100. Asking to switch to $100 from $1b misses the point, as by then whether you’re facing a charlatan is probably locked in.
In the Warren Buffet situation, though, we have a high certainty. It’s very unlikely that Warren Buffet will actually pay us $0 or $2,000,000.
The key bit of the article:
Now say that someone estimates that action A (e.g., giving in to the mugger’s demands) has an expected value of X (same units) - but that the estimate itself is so rough that the right expected value could easily be 0 or 2X. More specifically, say that the error in the expected value estimate has a standard deviation of X.
...and an asteroid-prevention program is extremely likely to either do nothing or save the whole world. Modeling it with a normal distribution means pretending that it’s almost as likely to cause billions of deaths (compared to the baseline of doing nothing) as it is to save billions of lives.
•I think that the relevant distribution here is a log-normal distribution; this won’t involve billions of deaths because it’s strictly positive.
•Even if the outcome of an asteroid strike program is essentially binary there’s still uncertainty as to how likely it will be successfully implemented, how likely it is to avert an asteroid strike assuming that it’s implemented properly, and the likelihood of humanity surviving independently of the asteroid strike issue.
•An asteroid strike prevention program has the downside of diverting skilled labor. This may seem negligible by comparison with the upside, but the fact that the probability of impact is so small makes things less clear.
•At the level of the individual donor there’s the issue of fungibility and counterfactuals (is funding an asteroid strike prevention program replacing dollars that someone else would otherwise have used to fund it? If so, what are they doing with the money instead? Is this use of money replacing that of a third person’s dollars? If so, what is that third person doing instead? etc.)
These things all point in the direction of having the distribution of expected value attached to funding an asteroid strike prevention program being more diffuse than it may seem at first blush.
I think that for the purposes of establishing a prior distribution of expectation, “playing poker with Warren Buffett” should be in a really different reference class than e.g. “playing poker with my buddies on Thursday.” So in your example, I don’t think that playing for a billion would actually be so many standard deviations out as to make it less positive than playing for a hundred.
The majority of the work that this adjustment does is due to the assumption that an action with high variance is practically as likely to greatly harm as to greatly help, and thus the long positive tail is nearly canceled out by the long negative tail. For some Pascal’s Muggings (like the original Wager), this may be valid, but for others it’s not.
If Warren Buffett offers to play a hand of poker with you, no charge if he wins and he gives you a billion if he loses, some skepticism about the setup might be warranted- but to say “it would be better for me if we played for $100 of your money instead” seems preposterous. (Since in this case there’s no negative tail, an adjustment based only on mean and variance is bound to screw up.)
I don’t think this is true. I think the majority of the work that the adjustment does is due to the assumption that high value actions are extraordinarily unlikely.
It’s entirely possible that your expectation in situations when you think Buffett is offering $1b is smaller than your expectation in situations when you think Buffett is offering $100. Asking to switch to $100 from $1b misses the point, as by then whether you’re facing a charlatan is probably locked in.
In the Warren Buffet situation, though, we have a high certainty. It’s very unlikely that Warren Buffet will actually pay us $0 or $2,000,000.
The key bit of the article:
...and an asteroid-prevention program is extremely likely to either do nothing or save the whole world. Modeling it with a normal distribution means pretending that it’s almost as likely to cause billions of deaths (compared to the baseline of doing nothing) as it is to save billions of lives.
•I think that the relevant distribution here is a log-normal distribution; this won’t involve billions of deaths because it’s strictly positive.
•Even if the outcome of an asteroid strike program is essentially binary there’s still uncertainty as to how likely it will be successfully implemented, how likely it is to avert an asteroid strike assuming that it’s implemented properly, and the likelihood of humanity surviving independently of the asteroid strike issue.
•An asteroid strike prevention program has the downside of diverting skilled labor. This may seem negligible by comparison with the upside, but the fact that the probability of impact is so small makes things less clear.
•At the level of the individual donor there’s the issue of fungibility and counterfactuals (is funding an asteroid strike prevention program replacing dollars that someone else would otherwise have used to fund it? If so, what are they doing with the money instead? Is this use of money replacing that of a third person’s dollars? If so, what is that third person doing instead? etc.)
These things all point in the direction of having the distribution of expected value attached to funding an asteroid strike prevention program being more diffuse than it may seem at first blush.
I think that for the purposes of establishing a prior distribution of expectation, “playing poker with Warren Buffett” should be in a really different reference class than e.g. “playing poker with my buddies on Thursday.” So in your example, I don’t think that playing for a billion would actually be so many standard deviations out as to make it less positive than playing for a hundred.
Good thought experiment.