“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.
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.
Where do such A and B in the example come from, and what data do they specify? Is decision an explicit part of this data, so that they can differ in decision without differing in code? (Why is B interested in A’s location and not in their own?) 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. Do we need to consider together an agent, its observation, and a decision, something potentially “conditionally inconsistent”?
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?
For example, B could be an agent that always defects and we could want to counterfactually calculate what B would do in town.
But then you are not considering a decision. My comments were under the assumption that there is a decision to make, not an impossible situation to construct.
UDT assumes that the agent has a Mathematical Intuition Function so the input is only real observations.
I don’t understand this statement (I’m thinking of UDT1.1, i.e. decision is a decision about strategy, so there is no input to consider during decision making).
What did I say that made you think I might have believed this?
I was mistaken in thinking that you were discussing decision making by a particular agent, in which case this was a possible source of contradictions in descriptions of situations. 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.
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
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.
My guess is that usually it’s understood differently, and I wasn’t following the way you understood it in the post. The desert and the town are usually taken to be separate, so that they can always be instantiated separately, no matter what predictor in the desert or agent in town decide. 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 (and then for that agent to decide to pay).
It’s an impossible situation similar to open box Newcomb’s problem, but still a situation where the agent can be located, for purposes of finding dependencies to maximize over. These impossible situations need to be taken as “real enough” to find the agent in them. The dependency-based approach favored in UDT, TDT and now Functional Decision Theory doesn’t help with clarifying this issue. For these, there’s only the “living in impossible situations” philosophy that I alluded to in comments to your previous post that helps with setting up the problems so that they can be understood in terms of dependencies. 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.
(See also this comment for another way of tackling this issue.)
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.
“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.
Where do such A and B in the example come from, and what data do they specify? Is decision an explicit part of this data, so that they can differ in decision without differing in code? (Why is B interested in A’s location and not in their own?) 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. Do we need to consider together an agent, its observation, and a decision, something potentially “conditionally inconsistent”?
My previous post examined a specific example in detail, although UDT handles it slightly different.
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.
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.
No. Agents with the same code will always produce the same decision given the same inputs.
I don’t claim that it is. What did I say that made you think I might have believed this?
But then you are not considering a decision. My comments were under the assumption that there is a decision to make, not an impossible situation to construct.
I don’t understand this statement (I’m thinking of UDT1.1, i.e. decision is a decision about strategy, so there is no input to consider during decision making).
I was mistaken in thinking that you were discussing decision making by a particular agent, in which case this was a possible source of contradictions in descriptions of situations. 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.
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”
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.
UDT is often argued to solve problems like Parfit’s Hitchhiker
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.
My guess is that usually it’s understood differently, and I wasn’t following the way you understood it in the post. The desert and the town are usually taken to be separate, so that they can always be instantiated separately, no matter what predictor in the desert or agent in town decide. 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 (and then for that agent to decide to pay).
It’s an impossible situation similar to open box Newcomb’s problem, but still a situation where the agent can be located, for purposes of finding dependencies to maximize over. These impossible situations need to be taken as “real enough” to find the agent in them. The dependency-based approach favored in UDT, TDT and now Functional Decision Theory doesn’t help with clarifying this issue. For these, there’s only the “living in impossible situations” philosophy that I alluded to in comments to your previous post that helps with setting up the problems so that they can be understood in terms of dependencies. 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.
(See also this comment for another way of tackling this issue.)
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.
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.
How so? As I said, UDT already seems to do this.