You don't promote C to the action node, it is the action node. That's the way the decision problem is specified: do you one-box or two-box? If you don't accept that, then you're talking about a different decision problem. But in Newcomb's problem, the algorithm is trying to decide that. It's not trying to decide which algorithm it should be (or should have been). Having the algorithm pretend - as a means of reaching a decision about C - that it's deciding which algorithm to be is somewhat reminiscent of the idea behind TDT and has nothing to do with CDT as traditionally conceived of, despite the use of causal reasoning.

You are applying a decision theory to the node C, which means you are implicitly stating: there are multiple possible choices to be made at this point, and this decision can be made independent of nodes not in front of this one.

Yes. That's basically the definition of CDT. That's also why CDT is no good. You can quibble about the word but in "the literature", 'CDT' means just that.

I don't see at all what's wrong or confusing about saying that an agent is trying to decide something; or even, for that matter, that an algorithm is trying to decide something, even though that's not a precise way of speaking.

More to the point, though, doesn't what you describe fit EDT and CDT both, with each theory having a different way of computing "what the world will be like if the decision is made in a specific way"?

Could you try to maybe give a straight answer to, what is your problem with my model above? It accurately models the situation. It allows CDT to give a correct answer.

No, it does not, that's what I was trying to explain. It's what I've been trying to explain to you all along: CDT cannot make use of the correlation between C and P. CDT cannot reason backwards in time. You do know how surgery works, don't you? In order for CDT to use the correlation, you need a causal arrow from C to P - that amounts to backward causation, which we don't want. Simple as that.

You are trying to use a decision theory to determine which choice an agent should make, after the agent has already had its algorithm fixed, which causally determines which choice the agent must make.

I'm not sure what the meaning of this is. Of course the decision algorithm is fixed before it's run, and therefore its output is predetermined. It just doesn't know its own output before it has computed it. And I'm not trying to figure out what the agent should do - the agent is trying to figure that out. Our job is to figure out which algorithm the agent should be using.

PS: The downvote on your post above wasn't from me.

Okay, so I take it to be the defining characteristic of CDT that it uses of counterfactuals. So far, I have been arguing on the basis of a Pearlean conception of counterfactuals, and then this is what happens:

Your causal network has three variables, A (the algorithm used), P (Omega's prediction), C (the choice). The causal connections are A -> P and A -> C. There is no causal connection between P and C.

Now the CDT algorithm looks at counterfactuals with the antecedent C1. In a Pearlean picture, this amounts to surgery on the C-node, so no inference contrary to the direction of causality is possible. Hence, whatever the value of the P-node, it will seem to the CDT algorithm not to depend on the choice.

Therefore, even if the CDT algorithm knows that its choice is predetermined, it cannot make use of that in its decision, because it cannot update contrary to the direction of causality.

Now it turns out that natural language counterfactuals work very much, but not quite like Pearl's counterfactuals: they allow a limited amount of backtracking contrary to the direction of causality, depending on a variety of psychological factors. So if you had a theory of counterfactuals that allowed backtracking in a case like Newcomb's problem, then a CDT-algorithm employing that conception of counterfactuals would one-box. The trouble would of course be to correctly state the necessary conditions for backtracking. The messy and diverse psychological and contextual factors that seem to be at play in natural language won't do.

As far as I have understood, your problem is that, if you apply CDT with an action node at T=4, it gives the wrong answer. At T=4, there is only one option to choose, so the choice of decision theory is not exactly critical.

Yes, it is. The point is that you run your algorithm at T=4, even if it is deterministic and therefore its output is already predetermined. Therefore, you want an algorithm that, executed at T=4, returns one-boxing. CDT does simply not do that.

Ultimately, it seems that we're disagreeing about terminology. You're apparently calling something CDT even though it does not work by surgically altering the node for the action under consideration (that action being the choice of box, not the precommitment at T<1) and then looking at the resulting expected utilities.

But isn't this precisely the basic idea behind TDT?

The algorithm you are suggesting goes something like this: Chose that action which, if it had been predetermined at T=0 that you would take it, would lead to the maximal-utility outcome. You can call that CDT, but it isn't. Sure, it'll use causal reasoning for evaluating the counterfactual, but not everything that uses causal reasoning is CDT. CDT is surgically altering the action node (and not some precommitment node) and seeing what happens.

Well, a practically important example is a deterministic agent which is copied and then copies play prisoner's dilemma against each other.

There you have agents that use physics. Those, when evaluating hypothetical choices, use some model of physics, where an agent can model itself as a copyable deterministic process which it can't directly simulate (i.e. it knows that the matter inside it's head obeys known laws of physics). In the hypothetical that it cooperates, after processing the physics, it is found that copy cooperates, in the hypothetical that it defects, it is found that copy defects.

And then there's philosophers. The worse ones don't know much about causality. They presumably have some sort of ill specified oracle that we don't know how to construct, which will tell them what is a 'consequence' and what is a 'cause' , and they'll only process the 'consequences' of the choice as the 'cause'. This weird oracle tells us that other agent's choice is not a 'consequence' of the decision, so it can not be processed. It's very silly and not worth spending brain cells on.

I'd be the first to agree on terminology here. I'm not suggesting that choice of the box causes money in the box, simply that those two are causally connected, in the physical sense. The whole issue seems to stem from taking the word 'causal' from causal decision theory, and treating it as more than mere name, bringing in enormous amounts of confused philosophy which doesn't capture very well how physics work.

When deciding, you evaluate hypotheticals of you making different decisions. A hypothetical is like a snapshot of the world state. Laws of physics very often have to be run backwards from the known state to deduce past state, and then forwards again to deduce future state. E.g. a military robot sees a hand grenade flying into it's field of view, it calculates motion backwards to find where it was thrown from, finding location of the grenade thrower, then uses model of grenade thrower to predict another grenade in the future.

So, you process the hypothetical where you picked up one box, to find how much money you get. You have the known state: you picked one box. You deduce that past state of deterministic you must have been Q which results in picking up one box, a copy of that state has been made, and that state resulted in prediction of 1 box. You conclude that you get 1 million. You do same for picking 2 boxes, the previous state must be R, etc, you conclude you get 1000 . You compare, and you pick the universe where you get 1 box.

(And with regards to the "smoking lesion" problem, smoking lesion postulates a blatant logical contradiction - it postulates that the lesion affects the choice, which contradicts that the choice is made by the agent we are speaking of. As a counter example to a decision theory, it is laughably stupid)

Underlying physics is symmetric in time. If you assume that the state of the world is such that one box is picked up by your arm, that imposes constraints on both the future and the past light cone. If you do not process the constraints on the past light cone then your simulator state does not adhere to the laws of physics, namely, the decision arises out of thin air by magic.

If you do process constraints fully then the action to take one box requires pre-copy state of "you" that leads to decision to pick one box, which requires money in one box; action to take 2 boxes likewise, after processing constraints, requires no money in the first box. ("you" is a black box which is assumed to be non-magical, copyable, and deterministic, for the purpose of the exercise).

edit: came up with an example. Suppose 'you' is a robotics controller, you know you're made of various electrical components, you're connected to the battery and some motors. You evaluate a counter factual where you put a current onto a wire for some time. Constraints imposed on the past: battery has been charged within last 10 hours, because else it couldn't supply enough current. If constraints contradict known reality then you know you can't do this action. Suppose there's a replacement battery pack 10 meters away from the robot, the robot is unsure if 5 hours ago the packs have been swapped; in the alternative that they haven't been, it would not have enough charge to get to the extra pack, in the alternative that they have been swapped, it doesn't need to get to the spent extra pack. Evaluating the hypothetical where it got to the extra pack it knows the packs have been swapped in the past and extra pack is spent. (Of course for simplicity one can do all sorts of stuff, such as electrical currents coming out of nowhere, but outside the context of philosophical speculation the cause of the error is very clear).