Eliezer Yudkowsky wrote that Robin Hanson solved the Pascal's mugging thought experiment:
Robin Hanson has suggested penalizing the prior probability of hypotheses which argue that we are in a surprisingly unique position to affect large numbers of other people who cannot symmetrically affect us. Since only one in 3^^^^3 people can be in a unique position to ordain the existence of at least 3^^^^3 other people who are not symmetrically in such a situation themselves, the prior probability would be penalized by a factor on the same order as the utility.
I don't quite get it, is there a post that discusses this solution in more detail?
To be more specific, if a stranger approached me, offering a deal saying, "I am the creator of the Matrix. If you fall on your knees, praise me and kiss my feet, I'll use my magic powers from outside the Matrix to run a Turing machine that simulates 3^^^^3 copies of you having their coherent extrapolated volition satisfied maximally for 3^^^^3 years." Why exactly would I penalize this offer by the amount of copies being offered to be simulated? I thought the whole point was that the utility, of having 3^^^^3 copies of myself experiencing maximal happiness, does outweigh the low probability of it actually happening and the disuility of doing what the stranger asks for?
I would love to see this problem being discussed again and read about the current state of knowledge.
I am especially interested in the following questions:
- Is the Pascal's mugging thought experiment a "reduction to the absurd" of Bayes’ Theorem in combination with the expected utility formula and Solomonoff induction?1
- Could the "mugger" be our own imagination?2
- At what point does an expected utility calculation resemble a Pascal's mugging scenario and should consequently be ignored?3
1 If you calculate the expected utility of various outcomes you imagine impossible alternative actions. The alternatives are impossible because you already precommited to choosing the outcome with the largest expected utility. Problems: 1.) You swap your complex values for a certain terminal goal with the highest expected utility, indeed your instrumental and terminal goals converge to become the expected utility formula. 2.) Your decision-making is eventually dominated by extremely small probabilities of obtaining vast utility.
2 Insignificant inferences might exhibit hyperbolic growth in utility: 1.) There is no minimum amount of empirical evidence necessary to extrapolate the expected utility of an outcome. 2.) The extrapolation of counterfactual alternatives is unbounded, logical implications can reach out indefinitely without ever requiring new empirical evidence.
3 Extrapolations work and often are the best we can do. But since there are problems like 'Pascal's Mugging', that we perceive to be undesirable and that lead to an infinite hunt for ever larger expected utility, I think it is reasonable to ask for some upper and lower bounds regarding the use and scope of certain heuristics. We agree that we are not going to stop pursuing whatever terminal goal we have chosen just because someone promises us even more utility if we do what that agent wants. We might also agree that we are not going to stop loving our girlfriend just because there are many people who do not approve our relationship and who together would experience more happiness if we divorced than the combined happiness of us and our girlfriend being married. Therefore we already informally established some upper and lower bounds. But when do we start to take our heuristics seriously and do whatever they prove to be the optimal decision?
Hm... I've been thinking about it for a while (http://www.gwern.net/mugging) and still don't have any really satisfactory argument.
Now, it's obvious that you don't want to scale any prior probability more or less than the reward because either way involves you in difficulties. But do we have any non-ad hoc reason to specifically scale the size of the reward by 1/n, to regard the complexity as equal to the size?
I think there may be a Kolmogorov complexity argument that size does equal complexity, and welcome anyone capable of formally dealing with it. My intuition goes: Kolmogorov complexity is about lengths of strings in some language which map onto outputs. By the pigeonhole principle, not all outputs have short inputs. But various languages will favor different outputs with the scarce short inputs, much like a picture compressor won't do so hot on music files. 3^^^^3 has a short representation in the usual mathematical language, but that language is just one of indefinitely many possible mathematical languages; there are languages where 3^^^^3 has a very long representation, one 3^^^^3 long even, or longer still! Just like there are inputs to
zip
which compress very well and inputs which aren't so great.Now, given that our particular language favors 3^^^^3 with such a short encoding rather than many other numbers relatively nearby to 3^^^^3, doesn't Pascal's mugging start looking like an issue with our language favoring certain numbers? It's not necessary to favor 3^^^^3, other languages don't favor it or actively disfavor it. Those other languages would seem to be as valid and logically true as the current one, just the proofs or programs may be longer and written differently of course.
So, what do you get when you abstract away from the particular outputs favored by a language? When you add together and average the languages with long encodings and the ones with short encodings for 3^^^^3? What's the length of the average program for 3^^^^3? I suggest it's 3^^^^3, with every language that gives it a shorter encoding counterbalanced by a language with an exactly longer encoding.
For a sufficiently crazy set of languages you could make this true for 3^^^^3, but in general what's simple in one language is still fairly simple elsewhere. If 3+3 takes b bits to describe in language A it takes b+c bits in language B where c is the length of the shortest interpreter for language B in language A (edit: or less :P).