Extremely underrated post, I'm sorry I only skimmed it when it came out.

I found 3a,b,c to be strong and well written, a good representation of my view. 

In contrast, 3d I found to be a weak argument that I didn't identify with. In particular, I don't think internal conflicts are a good way to explain the source of goal misgeneralization. To me it's better described as just overfitting or misgeneralization.^[1] Edge cases in goals are clearly going to be explored by a stepping back process, if initial attempts fail. In particular if attempted pathways continue to fail. Whereas thinking of the AI as needing to resolve conflicting values seems to me to be anthropomorphizing in a way that doesn't seem to transfer to most mind designs.

You also used the word coherent in a way that I didn't understand.

Human intelligence seems easily useful enough to be a major research accelerator if it can be produced cheaply by AI

I want to flag this as an assumption that isn't obvious. If this were true for the problems we care about, we could solve them by employing a lot of humans.

humans provides a pretty strong intuitive counterexample

It's a good observation that humans seem better at stepping back inside of low-level tasks than at high-level life-purposes. For example, I got stuck on a default path of finishing a neuroscience degree, even though if I had reflected properly I would have realised it was useless for achieving my goals a couple of years earlier. I got got by sunk costs and normality.

However, I think this counterexample isn't as strong as you think it is. Firstly because it's incredibly common for people to break out of a default-path. And secondly because stepping back is usually proceeded by some kind of failure to achieve the goal using a particular approach. Such failures occur often at small scales. They occur infrequently in most people's high-level life plans, because such plans are fairly easy and don't often raise flags that indicate potential failure. We want difficult work out of an AI. This implies frequent total failure, and hence frequent high-level stepping back. If it's doing alignment research, this is particularly true.

1. ^{^}

   Like for reasons given in section 4 of the misalignment and catastrophe doc.

To me it seems like one important application of this work is to understanding and fixing the futachy hack in FixDT and in Logical Inductor decision theory. But I'm not sure whether your results can transfer to these settings, because of the requirement that the agents have the same beliefs.

Is there a reason we can't make duplicate traders in LI and have their trades be zero-sum?

I'm generally confused about this. Do you have thoughts? 

What task? All the tasks I know of that are sufficient to reduce x-risk are really hard.

I think the term is very reasonable and basically accurate, even more so with regard to most RL methods. It's a good way of describing a training process without implying that the evolving system will head toward optimality deliberately. I don't know a better way to communicate this succinctly, especially while not being specific about what local search algorithm is being used.

Also, evolutionary algorithms can be used to approximate gradient descent (with noisier gradient estimates), so it's not unreasonable to use similar language about both.

I'm not a huge fan of the way you imply that it was chosen for rhetorical purposes.

I read about half of this post when it came out. I didn't want to comment without reading the whole thing, and reading the whole thing didn't seem worth it at the time. I've come back and read it because Dan seemed to reference it in a presentation the other day.

The core interesting claim is this:

My conclusion will be that most of the items on Bostrom's laundry list are not 'convergent' instrumental means, even in this weak sense. If Sia's desires are randomly selected, we should not give better than even odds to her making choices which promote her own survival, her own cognitive enhancement, technological innovation, or resource acquisition.

This conclusion doesn't follow from your arguments. None of your models even include actions that are analogous to the convergent actions on that list. 

The non-sequential theoretical model is irrelevant to instrumental convergence, because instrumental convergence is about putting yourself in a better position to pursue your goals later on. The main conclusion seems to come from proposition 3, but the model there is so simple it doesn’t include any possibility of Sia putting itself in a better position for later.

Section 4 deals with sequential decisions, but for some reason mainly gets distracted by a Newcomb-like problem, which seems irrelevant to instrumental convergence. I don't see why you didn't just remove Newcomb-like situations from the model? Instrumental convergence will show up regardless of the exact decision theory used by the agent.

Here's my suggestion for a more realistic model that would exhibit instrumental convergence, while still being fairly simple and having "random" goals across trajectories. Make an environment with 1,000,000 timesteps. Have the world state described by a vector of 1000 real numbers. Have a utility function that is randomly sampled from some Gaussian process (or any other high entropy distribution over functions) on $R^{1,000,000\times 1000}\to R$. Assume there exist standard actions which directly make small edits to the world-state vector. Assume that there exist actions analogous to cognitive enhancement, making technology and gaining resources. Intelligence can be used in the future to more precisely predict the consequences of actions on the future world state (you’d need to model a bounded agent for this). Technology can be used to increase the amount or change the type of effect your actions have on the world state. Resources can be spent in the future for more control over the world state. It seems clear to me that for the vast majority of the random utility functions, it's very valuable to have more control over the future world state. So most sampled agents will take the instrumentally convergent actions early in the game and use the additional power later on. 

The assumptions I made about the environment are inspired by the real world environment, and the assumptions I've made about the desires are similar to yours, maximally uninformative over trajectories.

Thanks for clarifying, I misunderstood your post and must have forgotten about the scope, sorry about that. I'll remove that paragraph. Thanks for the links, I hadn't read those, and I appreciate the pseudocode.

I think most likely I still don't understand what you mean by grader-optimizer, but it's probably better to discuss on your post after I've spent more time going over your posts and comments.

My current guess in my own words is: A grader-optimizer is something that approximates argmax (has high optimization power)?
And option (1) acts a bit like a soft optimizer, but with more specific structure related to shards, and how it works out whether to continue optimizing?