by Wei Dai 29 days ago | Scott Garrabrant likes this | link | parent We can solve the problem in what seems like the right way by introducing a basic notion of counterfactual, which I’ll write □→. This is supposed to represent “what the agent’s code will do on different inputs”. The idea is that if we have the policy of dancing when we see the money, M□→H is true even in the world where we don’t see any money. (I’m confused about why this notation needs to be introduced. I haven’t been following all the DT discussions super closely, so I’d appreciate if someone could catch me up. Or, since I’m visiting MIRI soon, perhaps someone could catch me up in person.) In the language of my original UDT post, I would have written this as S(‘M’)=‘H’, where S is the agent’s code (M and H in quotes here to denote that they’re input/output strings rather than events). This is a logical statement about the output of S given ‘M’ as input, which I had conjectured could be conditioned on the same way we’d condition on any other logical statement (once we have a solution to logical uncertainty). Of course, issues like Agent Simulates Predictor has since come up, so is this new idea/notation an attempt to solve some of those issues? Can you explain what advantages this notation has over the S(‘M’)=‘H’ type of notation? It’s not clear where the beliefs about this correlation come from, so these counterfactuals are still almost as mysterious as explicitly giving conditional probabilities for everything given different policies. Intuitively, it comes from the fact that there’s a chunk of computation in Omega that’s analyzing S, which should be logically correlated with S’s actual output. Again, this was a guess of what a correct solution to logical uncertainty would say when you run the math. (Now that we have logical induction, can we tell if it actually says this?)

### NEW DISCUSSION POSTS

This is exactly the sort of
 by Stuart Armstrong on Being legible to other agents by committing to usi... | 0 likes

When considering an embedder
 by Jack Gallagher on Where does ADT Go Wrong? | 0 likes

The differences between this
 by Abram Demski on Policy Selection Solves Most Problems | 0 likes

Looking "at the very
 by Abram Demski on Policy Selection Solves Most Problems | 0 likes

Without reading closely, this
 by Paul Christiano on Policy Selection Solves Most Problems | 1 like

>policy selection converges
 by Stuart Armstrong on Policy Selection Solves Most Problems | 0 likes

Indeed there is some kind of
 by Vadim Kosoy on Catastrophe Mitigation Using DRL | 0 likes

Very nice. I wonder whether
 by Vadim Kosoy on Hyperreal Brouwer | 0 likes

Freezing the reward seems
 by Vadim Kosoy on Resolving human inconsistency in a simple model | 0 likes

Unfortunately, it's not just
 by Vadim Kosoy on Catastrophe Mitigation Using DRL | 0 likes

>We can solve the problem in
 by Wei Dai on The Happy Dance Problem | 1 like

Maybe it's just my browser,
 by Gordon Worley III on Catastrophe Mitigation Using DRL | 2 likes

At present, I think the main
 by Abram Demski on Looking for Recommendations RE UDT vs. bounded com... | 0 likes

In the first round I'm
 by Paul Christiano on Funding opportunity for AI alignment research | 0 likes

Fine with it being shared
 by Paul Christiano on Funding opportunity for AI alignment research | 0 likes