I would use this feature because there is a trade-off between using an epistemic status and losing valuable preview real estate to encourage people to read your posts. Currently I’ve taken to inserting notes after the first paragraph.
I’m not following what you’re saying about loopy causation. How are you constructing this graph?
Thanks, so much for organising this. Undoubtedly, it will be challenging, but if it works, it could greatly help a bunch of people fix up their lives.
I’m having difficulty following this comment. I don’t know what you mean by a “symbol pushing game”. Also, does box modality simply refer to modal logic?
Anyway, re: problems like Perfect Parfit’s Hitchhiker, proving you win by paying still requires you to define what the predictor actually predicts. Otherwise we can’t say that a never paying agent doesn’t end up in town and hence win. So I don’t understand how this avoids the “problematic consequences of non-actual decisions”.
I see. I think you are right, there is something wrong with Parfit’s Hitchhiker, when it’s understood in the way you did in the post, and UDT can’t handle this either.
This statement confuses me. My argument is that UDT already does do this, but that it does so without explicit explanation or justification of what it is doing.
So it’s fine to have an agent in town with the memory of the predictor expecting them to not pay in town and not taking them there
Hmm… An agent that defects in any possible situation, for example, can figure out that the situation with this memory is impossible is impossible. So perhaps they’re using a para-consistent logic. This would still work on a representation of a system, rather than the system. But the problem with doing this is that it assumes that the agent has the ability to represent para-consistent situations. And without knowing anything about para-consistent logic, I would suspect that there would be multiple approaches. How can we justify a specific approach? It seems much easier to avoid all of this and work directly with the inputs given that any real agent ultimately works on inputs. Or even if we do adopt a para-consistent logic, it seems like the justification for choosing the specific logic would be ultimately grounded in inputs.
Your take on this was to deny impossible situations and replace them with observations, which is easier to describe, but more difficult to reason about in unexpected examples.
How so? As I said, UDT already seems to do this.
So like some kind of clone situation where deterministic world views would say both must make the same decision?
How is my approach my more prescriptive than yours? Also, what do you mean by “Eliezer’s worlds”?
(Ps. I asked about observer 2′s behaviour, not observer 1′s)
In what kind of situation would we miss out on potential utility?
Perfect knowledge about everything is only possible if strict determinism holds
It’s normally quite trivial to extend results from deterministic situations to probabilistically deterministic situations. But are you concerned about the possible existence of libertarian free will?
The non existence of real ,as opposed to merely logical, counterfactuals follows trivially from determinism, but determinism is a very non trivial assumption
If we already know what decision you are going to take, we can’t answers questions about what decision is best in a non-trivial sense without constructing a new situation where this knowledge has been erased.
What do you mean by “metaphysical neutrality”?
Yeah, I’ve spent the last two weeks making slowly making my way through the posts on Ambient Decision Theory. I found that absolutely fascinating, but rather difficult for me to get my head around. I guess my question was more about whether there are any other simple scenarios that demonstrate that logical counterfactuals are more about your state of knowledge than the physical state of the universe. I think that would help me understand what exactly is going on better. This one took me much longer than you’d expect to construct.
What in particular would you love to understand better?
Yeah, given the tendency of multi-world to complicate things I usually ignore it and leave it up to others to figure out how to adapt my arguments to this theory.
1) What’s the reference to psychopathic pest control workers?
2) I suspect that there’s at least something imprecise in the claim that self-knowledge is harmful. Why can’t we figure out a way to throw away this useless information like we can in other situations? I know I’m expressing skepticism without a solid argument, but I haven’t quite figured out how to express what feels wrong about making that claim yet.
My comments were under the assumption that there is a decision to make, not an impossible situation to construct.
Well, the question is what should you do in Parfit’s Hitchhiker with a perfect predictor. And before you can even talk about the predictor, you need to define what it predicts. Maybe it would have been clearer if I’d written, “B could be an agent that defects in any coherent situation and we want to construct a coherent counterfactual so that the predictor can predict it defecting”
UDT assumes that the agent has a Mathematical Intuition Function so the input is only real observations.
I wrote this last sentence with UDT 1.0 in mind, which makes it confusing as I referred to Input-Output maps which are part of UDT 1.1. In UDT 1.0, even though you don’t perform Bayesian updates on input, they determine the observer set that is considered. Maybe it’d help to say that I think of UDT 1.1 as a modified version of UDT 1.0.
Still not clear what motivates considering the contradictory situations, what kinds of situations are to be considered, and what this has to do with UDT.
UDT is often argued to solve problems like Parfit’s Hitchhiker
My previous post examined a specific example in detail, although UDT handles it slightly different.
Where do such A and B in the example come from?
In the example of Parfit’s Hitchhiker, B is an agent for which we want to calculate a counterfactual, but we run into consistency issues. For example, B could be an agent that always defects and we could want to counterfactually calculate what B would do in town. A would be any agent which actually could actually arrive in town without an inconsistency.
And what data do they specify?
I don’t really follow this question. If you want to know what the inputs are, in my last past I pretended that they had direct access to an oracle with details about the situation, in addition to making observations as per the scenario. UDT assumes that the agent has a Mathematical Intuition Function so the input is only real observations.
The outputs are merely the actions that the agents take in particular scenarios or the actions that they are predicted to take if they counterfactually received a particular output.
Is decision an explicit part of this data, so that they can differ in decision without differing in code?
No. Agents with the same code will always produce the same decision given the same inputs.
An agent is not agent-plus-decision, and an agent-in-a-situation is not agent-in-a-situation-plus-decision, instead decision is a consequence of the agent or something considered apart from the agent
I don’t claim that it is. What did I say that made you think I might have believed this?
This seems to be an empty comment.
“To deny/ignore reality of what you see or could see altogether and say that observations are just index by which your decision is to be looked up in a global strategy seems like it’s throwing out useful understanding”—I’m not quite reducing them that mere indexes since I’m only making them represent indexes for conditionally consistent situations with incompatible agents. Suppose we have a conditionally consistent situation S and a compatible agent A who derives that they are in S when they see a set of observations O. Then we are using O as the lookup index representing the counterfactual of S for an incompatible agent B. Because O is interpreted as S by any compatible agent, it is hardly just an arbitrary index.
I honestly can’t see how we can do better than this. S is incompatible with B, so the only way to make this meaningful will be to ask a slightly different question. We could ask about what B does in S given a paraconsistent logic, but this would involve asking a slightly different question as well.
But anyway, even though I thought this was a new proposal in my last post, it seems to be what UDT is already doing, unless I’m misunderstanding it.
Thanks for this comment! The idea that only a single Uy can be provable, otherwise we get a contradiction and hence we can prove an arbitrarily high utility has greatly clarified this post for me. I still haven’t quite figured out why the agent can’t use the above proof that those are the only two provable and the fact that agent()==1 gives a higher value to prove that agent()!=2 and then create a contradiction.
Edit: Actually, I think I now understand this. After finding those two proofs, the agent can only conclude that there aren’t any more proofs of the form “agent()=_ implies world()=_”if its reasoning (say PA) is consistent. But it can’t actually prove this!