I wrote this essay in early August. I now consider the presentation to be somewhat confused, and now better understand where problems arise within the "standard alignment model." I'm publishing a somewhat edited version, on the grounds that something is better than nothing.

Summary: Consider the argument: "Imperfect value representations will, in the limit of optimization power, be optimized into oblivion by the true goal we really wanted the AI to optimize." But... I think my brother cares about me in some human and "imperfect" way. I also think that the future would contain lots of value for me if he were a superintelligent dictator (this could be quite bad for other reasons, of course).

Therefore, this argument seems to prove too much. It seems like one of the following must be true:

My brother cares about me in the "perfect" way, or
Dictator-brother would do valueless things according to my true values, or
The argument is wrong.

To explore these points, I dialogue with my model of Eliezer.

Suppose you win a raffle and get to choose one of n prizes. The first prize is a book with true value 10, but your evaluation of it is noisy (drawn from the Gaussian N(10,1) with standard deviation 1). The other n-1 prizes are widgets with true value 1, but your evaluation is more noisy (drawn from N(1,16) with standard deviation 4). As n increases, you’re more probable to select a widget and lose out on 10-1=9 utility. By considering so many options, you’re selecting against your own ability to judge prizes by implicitly selecting for high noise. You end up “optimizing so hard” that you delude yourself. This is the Optimizer’s Curse.

You’re probably already familiar with Goodhart’s Law, which applies when an agent optimizes a proxy U (e.g. how many nails are produced) for the true quantity V which we value (e.g. how profitable the nail factory is).

Goodhart’s Curse is their combination. According to the article, optimizing a proxy measure U over a trillion plans can lead to high regret under the true values V, even if U is an unbiased but noisy estimator of the true values V. This seems like bad news, insofar as it suggests that even getting an AI which understands human values on average across possibilities can still produce bad outcomes.

Below is a dialogue with my model of Eliezer. I wrote the dialogue to help me think about the question at hand. I put in ...