JesseClifton

How to think about an uncertain future: lessons from other sectors & mistakes of longtermist EAs

Very late here, but a brainstormy thought: maybe one way one could start to make a rigorous case for RDM is to suppose that there is a “true” model and prior that you would write down if you had as much time as you needed to integrate all of the relevant considerations you have access to. You would like to make decisions in a fully Bayesian way with respect to this model, but you’re computationally limited so you can’t. You can only write down a much simpler model and use that to make a decision.

We want to pick a policy which, in some sense, has low regret with respect to the Bayes-optimal policy under the true model. If we regard our simpler model as a random draw from a space of possible simplified models that we could’ve written down, then we can ask about the frequentist properties of the regret incurred by different decision rules applied to the simple models. And it may be that non-optimizing decision rules like RDM have a favorable bias-variance tradeoff, because they don’t overfit to the oversimplified model. Basically they help mitigate a certain kind of optimizer’s curse.

some concerns with classical utilitarianism

nil already kind of addressed this in their reply, but it seems important to keep in mind the distinction between the intensity of a stimulus and the moral value of the experience caused by the stimulus. Statements like “experiencing pain just slightly stronger than that threshold” risk conflating the two. And, indeed, if by “pain” you mean “moral disvalue” then to discuss pain as a scalar quantity begs the question against lexical views.

Sorry if this is pedantic, but in my experience this conflation often muddles discussions about lexical views.

What are some low-information priors that you find practically useful for thinking about the world?

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

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.

+1. I think many who have asymmetric sympathies might say that there is a strong

aestheticpull to bringing about a life like Michael’s, but that there is an overridingmoralresponsibility not to create intense suffering.