It wouldn’t work as you’ve stated it. The action of changing itself to a one-boxer would, according to its current decision theory, increase payoffs for every Newcomb’s Problem it would encounter from that moment forward, but not for any in which the Predictor had already made its decision.
What confuses me here is that a causal model of reality would still tell it that being a one-boxer now will maximize the payoff now, if it examines possible worlds in the right way. It seems to come down to cognitive contingencies—whether its heuristics manage to generate this observation, without it then being countered by a “can’t-change-the-past” heuristic.
I may need to examine the decision-theory literature to see what I can reasonably call a “CDT agent”, especially Gibbard & Harper, where the distinction with evidential decision theory is apparently defined.
I think it’s the only difference between CDT and TDT: TDT gets a semi-correct causal graph, CDT doesn’t. (Only semi-correct because the way Eliezer deals with Platonic nodes, i.e. straightforward Bayesian updating, doesn’t seem likely to work in general. This is where UDT seems better than TDT.)
It wouldn’t work as you’ve stated it. The action of changing itself to a one-boxer would, according to its current decision theory, increase payoffs for every Newcomb’s Problem it would encounter from that moment forward, but not for any in which the Predictor had already made its decision.
Seriously, you can work this out for yourself.
What confuses me here is that a causal model of reality would still tell it that being a one-boxer now will maximize the payoff now, if it examines possible worlds in the right way. It seems to come down to cognitive contingencies—whether its heuristics manage to generate this observation, without it then being countered by a “can’t-change-the-past” heuristic.
I may need to examine the decision-theory literature to see what I can reasonably call a “CDT agent”, especially Gibbard & Harper, where the distinction with evidential decision theory is apparently defined.
That’s the main difference between decision theories like CDT, TDT and UDT.
I think it’s the only difference between CDT and TDT: TDT gets a semi-correct causal graph, CDT doesn’t. (Only semi-correct because the way Eliezer deals with Platonic nodes, i.e. straightforward Bayesian updating, doesn’t seem likely to work in general. This is where UDT seems better than TDT.)