A school that reports to each student their class ranking easily games this test. The test could even favor schools that don’t teach students enough to question an arbitrary class rank.
Also, this doesn’t consider the possibility that students can be good rationalists, but don’t interact with enough of the other students to make a good assessment of their relative strengths.
“crack a rationalist” made me think of the AI-Box Experiment (“http://yudkowsky.net/singularity/aibox″) Maybe a rationality test could be something like how long the subject lasts as the gatekeeper before letting the AI out.
Perhaps we can still test for this systematic optimism, while filtering for the noise I objected to, by instead of asking a “yes” or “no” question, asking for the probability that the student is in the top 50%. Treat the sum of these probabilities as the count of “yes” answers in the original version. Then a rational student should be able to account for his ignorance of other students in his answer.
For the first objection, that the AI Box experiment has too many unknowns, let us instead construct an argument based on psychological tricks for any bad conclusion to try on the subject.
For the second objection, that this tests stubbornness rather than rationality, use a sequence of tests, some using tricks to argue for false conclusions, and some using Bayesian evidence for a good conclusion. The score should reward being convinced when, and only when, the subject should be convinced. Stubbornness can only meet half this requirement.
The task of compiling arguments of both types, which would not be readily available to the subject ahead of time, remains.
I expect a good computer programmer to be effective in a language he has not seen before by looking up the syntax as he needs it, and to increase his effectiveness over time by learning the syntax through use, without any specific effort of memorization. At least, provided the novel language is good.
Understanding the process of extending math theory is essential if you want to be able to tell the difference between being led down a good path and being led down a bad path.
If all you do is memorize theorems without understanding their proofs, you can memorize false theorems and not know the difference until, maybe, a contradiction hits you over the head and demands attention.
My job is to cancel out an idiot.
How do you coordinate so that many others with the same strategy don’t cancel out the same idiot? If you also consider everyone who uses strategy as an idiot, it could work, but it seems difficult to achieve in practice. I think it would be more effective to actually make a judgment of what you would do if you were in charge, and then vote that way.
Your skill at not causing accidents, your skill at avoiding accidents that someone else would cause, your skill at evaluating these skills in others, and the number of times you have been in situations that test these skills seems like a lot of information to condense into 1 binary value.
So, in order to not be counterproductive, the strategy needs an environment in which it will be ineffective? Or are you suggesting that the difference in idiots on the two sides will be larger than the cancelers, but smaller than the cancelers combined with the experts? I think verifying this in a particular situation would be difficult.
On the other hand, if you actually have a position on the issue, you can use strategies that go beyond voting, like trying to persuade people. Even trying to persuade people not to vote because of their own ignorance could be more effective, if you really can’t make a good judgment.
It is—that’s what makes things like ‘rationality’ good heuristics—they bind together a bunch of different things that would be unwieldy to get at separately.
Good heuristics should synthesize relevant information and filter out noise. Counting indicators that should have different weights with the same weight is noise. Causing an accident is worse than failing to avoid one, even though failing to avoid one is bad.
As for the ‘binary’ part, it could as easily be ‘how many accidents have you been in?’ (followed, perhaps, by ‘how many have been determined to be your fault’)
Excellent, now we are using the criticism to improve the question. I would further change to “how many accidents have you caused (or been in) in the last year?”. Or some other set time. And ask how many miles the subject drives per year in the same time period, to normalize. Other demographic information might help here.
Post in this thread if you live in Orange County
I live in Lake Forest
Of course the one-boxers get more money: They were put in a situation in which they could either get $1 000 000 or $1 001 000, whereas the two-boxers were put in a situation in which they could get $0 or $1000.
When faced with this decision, you are either in the real world, in which case you can get an extra $1000 by two boxing, or you are in a simulation, in which case you can arrange so your self in the real world gets and extra $1,000,000 by one boxing. Given that you can’t tell which of these is the case, and that you are deterministic, you will make the same decision in both situations. So your choice is to either one box and gain $1,000,000 or two box and gain $1000. If you like having more money, it seems clear which of those choices is more rational.
Consider the following cases:
The difference in the number of idiots on the two sides is greater than the number of cancelers plus the number of experts. The cancelers have not made a difference. The impact is neutral.
The number of cancelers is sufficient to narrow the difference in the number of idiots to smaller than the number of experts (who have presumably achieved an expert consensus). The experts voting as a block can sway the election either way. The cancelers have enabled the experts to make a decision. The expected impact is positive (50% chance the experts change the decision, 50% the idiots were right anyways).
The number of cancelers is greater than the difference in the number of idiots plus the number of experts. The cancelers have changed the results of the election, without empowering the experts. The expected impact is neutral. (50% chance the new decision is right, 50% it is wrong. It is worse if the strategy convinces you to cancel out the idiots you think are a little more likely to be right. If you are canceling the idiots you think are a little more likely to be wrong, you have other reasons to vote that way.)
Having a reliable positive impact depends on being in situation 2, which, given a small number of experts, seems unlikely unless you are careful to only apply the strategy in this case, which would be a lot of work. I expect other strategies to get better results for the effort.
Alleged rationalists should not find themselves envying the mere decisions of alleged nonrationalists, because your decision can be whatever you like.
Eliezer said this in the Newcomb’s Problem post which introduced “Rationalists should win”.
Perhaps for a slogan, shorten it to: “Rationalists should not envy the mere decisions of nonrationalists.” This emphasizes that rationality contributes to winning through good decisions.
A potential problem is that, in some circumstances, an alleged rationalist could find a factor that seems unrelated to their decisions to blame for losing, and therefore argue that their being rational is consistent with the slogan. For example, a someone who blames losing on luck might need to reconsider their probability theory that is informing their decisions. Though this should not be a fully general counterargument, someone who wins more often than others in the same situation is likely doing something right, even if they do not win with probability 1.
A strategy Omega uses to avoid paradox which has the effect of punishing certain rituals of cognition because they lead to paradox is different than Omega deliberately handicapping your thought process. It is not a winning strategy to pursue a line of thought that produces a paradox instead of a winning decision. I would wait until Omega forbids strategies that would otherwise win before complaining that he “bounds how rational I can be”.
I could randomize: use my detector to determine p, and then use 1-p.
In this case, Omega figures out you would use that detector and predicts you will use 1-p. If your detector is effective, it will take into account that Omega knows about it, and will figure that Omega predicted 1-(1-p) = p. But Omega would have realized that the detector could do that. This is the beginning of an infinite recursion attempting to resolve a paradox, no different because we are using probabilities instead of Booleans. Omega recognizes this and concludes the game is not worth playing. If you and your detector are rational, you should too, and find a different strategy. (Well, Omega could predict a probability of .5 which is stable, but a strategy to take advantage of this would lead to paradox.)
Could you taboo “simulation” and explain what you are prohibiting Omega from doing by specifying that Omega does not use simulations? Presumably this still allows Omega to make predictions.
Indexical uncertainty is a property of you, not Omega.
Saying Omega cannot create a situation in which you have indexical uncertainty is too vague. What process of cognition is prohibited to Omega that prevents producing indexical uncertainty, but still allows for making calibrated, discriminating predictions?
With the Bayes-score being always negative, I don’t see what incentive one would have to submit a mistake report. I think it would be better to test for better than, for example, 90% confidence, by awarding 1 point for a correct report and deducting 9 points for an incorrect report. This achieves the goal of detecting ability to detect bad arguments. Measuring calibration would have to be a seperate test.