The simulated agent, together with the original agent, are removed from the world to form a dependence, which is a world with holes (free variables)
I’m still having difficulty understanding the process that you’re following, but let’s see if I can correctly guess this. Firstly you make a list of all potential situations that an agent may experience or for which an agent may be simulated. Decisions are included in this list, even if they might be incoherent for particular agents. In this example, these are:
Actual_Decision → Co-operate/Defect
Simulated_Decision → Co-operate/Defect
We then group all necessarily linked decisions together:
You then consider the tuple (equivalent to an observation-action map) that leads to the best outcome.
I agree that this provides the correct outcome, but I’m not persuaded that the reasoning is particularly solid. At some point we’ll want to be able to tie these models back to the real world and explain exactly what kind of hitchhiker corresponds to a (Defect, Defect) tuple. A hitchhiker that doesn’t get a lift? Sure, but what property of the hitchhiker makes it not get a lift?
We can’t talk about any actions it chooses in the actual world history, as it is never given the chance to make this decision. Next we could try constructing a counterfactual as per CDT and consider what the hitchhiker does in the world model where we’ve performed model surgery to make the hitchhiker arrive in town. However, as this is an impossible situation, there’s no guarantee that this decision is connected to any decision the agent makes in a possible situation. TDT counterfactuals don’t help either as they are equivalent to these tuples.
Alternatively, we could take the approach that you seem to favour and say that the agent makes the decision to defect in a paraconsistent situation where it is in town. But this assumes that the agent has the ability to handle paraconsistent situations when only some agents have this ability. It’s not clear how to interpret this for other agents. However, inputs have neither of these problems—all real world agents must do something given an input even if it is doing nothing or crashing and these are easy to interpret. So modelling inputs allows us to more rigorously justify the use of these maps. I’m beginning to think that there would be a whole post worth of material if I expanded upon this comment.
How would you define equivalence?
I think I was using the wrong term. I meant linked in the logical counterfactual sense, say two identical calculators. Is there a term for this? I was trying to understand whether you were saying that we only care about the provable linkages, rather than all such linkages.
Edit: Actually, after rereading over UDT, I can see that it is much more similar than I realised. For example, it also separates inputs from models. More detailed information is included at the bottom of the post.
Firstly you make a list of all potential situations that an agent may experience or for which an agent may be simulated. Decisions are included in this list, even if they might be incoherent for particular agents.
No? Situations are not evaluated, they contain instances of the agent, but when they are considered, it’s not yet known what the decision will be, so decisions are unknown, even if in principle determined by the (agents in the) situation. There is no matiching or assignment of possible decisions when we identify instances of the agent. Next, the instances are removed from the situation. At this point, decisions are no longer determined in the situations-with-holes (dependencies), since there are no agents and no decisions remaining in them. So there won’t be a contradiction in putting in any decisions after that (without the agents!) and seeing what happens.
I meant linked in the logical counterfactual sense, say two identical calculators.
That doesn’t seem different from what I meant, if appropriately formulated.
I’m still having difficulty understanding the process that you’re following, but let’s see if I can correctly guess this. Firstly you make a list of all potential situations that an agent may experience or for which an agent may be simulated. Decisions are included in this list, even if they might be incoherent for particular agents. In this example, these are:
Actual_Decision → Co-operate/Defect
Simulated_Decision → Co-operate/Defect
We then group all necessarily linked decisions together:
(Actual_Decision, Simulated_Decision) → (Co-operate, Co-operate)/(Defect, Defect)
You then consider the tuple (equivalent to an observation-action map) that leads to the best outcome.
I agree that this provides the correct outcome, but I’m not persuaded that the reasoning is particularly solid. At some point we’ll want to be able to tie these models back to the real world and explain exactly what kind of hitchhiker corresponds to a (Defect, Defect) tuple. A hitchhiker that doesn’t get a lift? Sure, but what property of the hitchhiker makes it not get a lift?
We can’t talk about any actions it chooses in the actual world history, as it is never given the chance to make this decision. Next we could try constructing a counterfactual as per CDT and consider what the hitchhiker does in the world model where we’ve performed model surgery to make the hitchhiker arrive in town. However, as this is an impossible situation, there’s no guarantee that this decision is connected to any decision the agent makes in a possible situation. TDT counterfactuals don’t help either as they are equivalent to these tuples.
Alternatively, we could take the approach that you seem to favour and say that the agent makes the decision to defect in a paraconsistent situation where it is in town. But this assumes that the agent has the ability to handle paraconsistent situations when only some agents have this ability. It’s not clear how to interpret this for other agents. However, inputs have neither of these problems—all real world agents must do something given an input even if it is doing nothing or crashing and these are easy to interpret. So modelling inputs allows us to more rigorously justify the use of these maps. I’m beginning to think that there would be a whole post worth of material if I expanded upon this comment.
I think I was using the wrong term. I meant linked in the logical counterfactual sense, say two identical calculators. Is there a term for this? I was trying to understand whether you were saying that we only care about the provable linkages, rather than all such linkages.
Edit: Actually, after rereading over UDT, I can see that it is much more similar than I realised. For example, it also separates inputs from models. More detailed information is included at the bottom of the post.
No? Situations are not evaluated, they contain instances of the agent, but when they are considered, it’s not yet known what the decision will be, so decisions are unknown, even if in principle determined by the (agents in the) situation. There is no matiching or assignment of possible decisions when we identify instances of the agent. Next, the instances are removed from the situation. At this point, decisions are no longer determined in the situations-with-holes (dependencies), since there are no agents and no decisions remaining in them. So there won’t be a contradiction in putting in any decisions after that (without the agents!) and seeing what happens.
That doesn’t seem different from what I meant, if appropriately formulated.