JesseClifton

Problems with EA representativeness and how to solve it

Nice comment; I'd also like to see a top-level post.

One quibble: Several of your points risk conflating "far-future" with "existential risk reduction" and/or "AI". But there is far-future work that is non-x-risk focused (e.g. Sentience Institute and Foundational Research Institute) and non-AI-focused (e.g. Sentience Institute) which might appeal to someone who shares some of the concerns you listed.

“Just take the expected value” – a possible reply to concerns about cluelessness

Distribution P is your credence. So you are saying "I am worried that my credences don't have to do with my credence." That doesn't make sense. And sure we're uncertain of whether our beliefs are accurate, but I don't see what the problem with that is.

I’m having difficulty parsing the statement you’ve attributed to me, or mapping it what I’ve said. In any case, I think many people share the intuition that “frequentist” properties of one’s credences matter. People care about calibration training and Brier scores, for instance. It’s not immediately clear to me why it’s nonsensical to say “P is my credence, but should I trust it?”

“Just take the expected value” – a possible reply to concerns about cluelessness

It sounds to me like this scenario is about a difference in the variances of the respective subjective probability distributions over future stock values. The variance of a distribution of credences does not measure how “well or poorly supported by evidence” that distribution is.

My worry about statements of the form “My credences over the total future utility given intervention A are characterized by distribution P” does not have to do with the variance of the distribution P. It has to do with the fact that I do not know whether I should trust the procedures that generated P to track reality.

“Just take the expected value” – a possible reply to concerns about cluelessness

whether you are Bayesian or not, it means that the estimate is robust to unknown information

I’m having difficulty understanding what it means for a subjective probability to be robust to unknown information. Could you clarify?

subjective expected utility theory is perfectly capable of encompassing whether your beliefs are grounded in good models.

Could you give an example where two Bayesians have the same subjective probabilities, but SEUT tells us that one subjective probability is better than the other due to better robustness / resulting from a better model / etc.?

“Just take the expected value” – a possible reply to concerns about cluelessness

For a Bayesian, there is no sense in which subjective probabilities are well or poorly supported by the evidence, unless you just mean that they result from calculating the Bayesian update correctly or incorrectly.

Likewise there is no true expected utility to estimate. It is a measure of an epistemic state, not a feature of the external world.

I am saying that I would like this epistemic state to be grounded in empirical reality via good models of the world. This goes beyond subjective expected utility theory. As does what you have said about robustness and being well or poorly supported by evidence.

“Just take the expected value” – a possible reply to concerns about cluelessness

But that just means that people are making estimates that are insufficiently robust to unknown information and are therefore vulnerable to the optimizer's curse.

I'm not sure what you mean. There is nothing being estimated and no concept of robustness when it comes to the notion of subjective probability in question.

“Just take the expected value” – a possible reply to concerns about cluelessness

I can’t speak for the author, but I don’t think the problem is the difficulty of “approximating” expected value. Indeed, in the context of subjective expected utility theory there is no “true” expected value that we are trying to approximate. There is just whatever falls out of your subjective probabilities and utilities.

I think the worry comes more from wanting subjective probabilities to *come* from somewhere — for instance, models of the world that have a track-record of predictive success. If your subjective probabilities are not grounded in such a model, as is arguably often the case with EAs trying to optimize complex systems or the long-run future, then it is reasonable to ask why they should carry much epistemic / decision-theoretic weight.

(People who hold this view might not find the usual Dutch book or representation theorem arguments compelling.)

What consequences?

Thanks for writing this. I think the problem of cluelessness has not received as much attention as it should.

I’d add that, in addition to the brute good and x-risks approaches, there are approaches which attempt to reduce the likelihood of dystopian long-run scenarios. These include suffering-focused AI safety and values-spreading. Cluelessness may still plague these approaches, but one might argue that they are more robust to both empirical and moral uncertainty.

An Argument for Why the Future May Be Good

Lazy solutions to problems of motivating, punishing, and experimenting on digital sentiences could also involve astronomical suffering.

Some Bayesian statisticians put together prior choice recommendations. I guess what they call a "weakly informative prior" is similar to your "low-information prior".