Red-penning is a general problem-solving method that's kinda similar to this research methodology.

I'd believe the claim if I thought that alignment was easy enough that AI products that pass internal product review and which don't immediately trigger lawsuits would be aligned enough to not end the world through alignment failure. But I don't think that's the case, unfortunately.

It seems like we'll have to put special effort into both single/single alignment and multi/single "alignment", because the free market might not give it to us.

I'd like more discussion of the claim that alignment research is unhelpful-at-best for existential safety because of it accelerating deployment. It seems to me that alignment research has a couple paths to positive impact which might balance the risk:

1. Tech companies will be incentivized to deploy AI with slipshod alignment, which might then take actions that no one wants and which pose existential risk. (Concretely, I'm thinking of out with a whimper and out with a bang scenarios.) But the existence of better alignment techniques might legitimize governance demands, i.e. demands that tech companies don't make products that do things that literally no one wants.

2. Single/single alignment might be a prerequisite to certain computational social choice solutions. E.g., once we know how to build an agent that "does what [human] wants", we can then build an agent that "helps [human 1] and [human 2] draw up incomplete contracts for mutual benefit subject to the constraints in the [policy] written by [human 3]". And slipshod alignment might not be enough for this application.

In this case humans are doing the job of transferring from $D$ to $D^{\ast }$, and the training algorithm just has to generalize from a representative sample of $D^{\ast }$ to the test set.

Thanks for the references! I now know that I'm interested specifically in cooperative game theory, and I see that Shoham & Leyton-Brown has a chapter on "coalitional game theory", so I'll take a look.

A proof of the lemma $V(a,b^{\ast })\le v^{\ast }$:

$$V(a,b^{\ast })=V(a,{\mathrm{argmin}}_b\underset{a^{\prime }}{max}V(a^{\prime },b\left)\right)$$

$$\le \underset{a^{\prime }}{max}V(a^{\prime },{\mathrm{argmin}}_b\underset{a^{\prime }}{max}V(a^{\prime },b\left)\right)$$

$$=\underset{b}{min}\underset{a^{\prime }}{max}V(a^{\prime },b)$$

$$=v^{\ast }$$

Ah, ok. When you said "obedience" I imagined too little agency — an agent that wouldn't stop to ask clarifying questions. But I think we're on the same page regarding the flavor of the objective.

Might not intent alignment (doing what a human wants it to do, being helpful) be a better target than obedience (doing what a human told it to do)?

My takeaway from this is that if we're doing policy selection in an environment that contains predictors, instead of applying the counterfactual belief that the predictor is always right, we can assume that we get rewarded if the predictor is wrong, and then take maximin.

How would you handle Agent Simulates Predictor? Is that what TRL is for?

The observation can provide all sorts of information about the universe, including whether exploration occurs. The exact set of possible observations depends on the decision problem.

$E$ and $O$ can have any relationship, but the most interesting case is when one can infer $E$ from $O$ with certainty.