Alf believes that in the rare, fluky event that he actually 1-boxes, then Omega won’t have predicted that.
Ah! Yes, this clarifies matters.
Sure, if Alf believes that Omega has a .95 chance of predicting Alf will two-box regardless of whether or not he does, then Alf should two-box. Similarly, if Beth believes Omega has a .95 chance of predicting Beth will one-box regardless of whether or not she does, then she also should two-box. (Though if she does, she should immediately lower her earlier confidence that she’s the sort of person who one-boxes.)
This is importantly different from the standard Newcomb’s problem, though.
You seem to be operating under the principle that if a condition is unlikely (e.g., Alf 1-boxing) then it is also unpredictable. I’m not sure where you’re getting that from.
By way of analogy… my fire alarm is, generally speaking, the sort of thing that remains silent… if I observe it in six-minute intervals for a thousand observations, I’m pretty likely to find it silent in each case. However, if I’m a good predictor of fire alarm behavior, I don’t therefore assume that if there’s a fire, it will still remain silent.
Rather, as a good predictor of fire alarms, what my model of fire alarms tells me is that “when there’s no fire, I’m .99+ confident it will remain silent; when there is a fire, I’m .99+ confident it will make noise.” I can therefore test to see if there’s a fire and, if there is, predict it will make noise. Its noise is rare, but predictable (for a good enough predictor of fire alarm behavior).
Remember I have two models of how Omega could work.
1) Omega is in essence an excellent judge of character. It can reliably decide which of its candidates is “the sort of person who 1-boxes” and which is “the sort of person who 2-boxes”. However, if he chooser actually does something extremely unlikely and out of character, Omega will get its prediction wrong. This is a model for Omega that I could actually see working, so it is the most natural way for me to interpret Newcomb’s thought experiment.
If Omega behaves like this, then I think causal and evidential decision theory align. Both tell the chooser to 2-box, unless the chooser has already pre-committed to 1-boxing. Both imply the chooser should pre-commit to 1-boxing (if they can).
2) Omega is a perfect predictor, and always gets its predictions right. I can’t actually see how his model would work without reverse causation. If reverse causatiion is implied by the problem statement, or choosers can reasonably think it is implied, then both causal and evidential decision theory align and tell the chooser to 1-box.
From the sound of things, you are describing a third model in which Omega can not only judge character, but can also reliably decide whether someone will act out of character or not. When faced with “the sort of person who 1-boxes”, but then—out of character − 2 boxes after all, Omega will still with high probability guess correctly that the 2-boxing is going to happen, and so withhold the $ 1 million.
I tend to agree that in this third model causal and evidential decision theory may become decoupled, but again I’m not really sure how this model works, or whether it requires backward causation again. I think it could work if the causal factors leading the chooser to act “out of character” in the particular case are already embedded in the chooser’s brain state when scanned by Omega, so at that stage it is already highly probable that the chooser will act out of character this time. But the model won’t work if the factors causing out of character behaviour arise because of very rare, random, brain events happening after the scanning (say a few stray neurons fire which in 99% of cases wouldn’t fire after the scanned brain state, and these cause a cascade eventually leading to a different choice). Omega can’t predict that type of event without being a pre-cog.
Thanks anyway though; you’ve certainly made me think about the problem a bit further...
So, what does it mean for a brain to do one thing 99% of the time and something else 1% of the time?
If the 1% case is a genuinely random event, or the result of some mysterious sort of unpredictable free will, or otherwise something that isn’t the effect of the causes that precede it, and therefore can’t be predicted short of some mysterious acausal precognition, then I agree that it follows that if Omega is a good-but-not-perfect predictor, then Omega cannot predict the 1% case, and Newcomb’s problem in its standard form can’t be implemented even in principle, with all the consequences previously discussed.
Conversely, if brain events—even rare ones—are instead the effects of causes that precede them, then a good-but-not-perfect predictor can make good-but-not-perfect predictions of the 1% case just as readily as the 99% case, and these problems don’t arise.
Personally, I consider brain events the effects of causes that precede them. So if I’m the sort of person who one-boxes 99% of the time and two-boxes 1% of the time, and Omega has a sufficient understanding of the causes of human behavior to make 95% accurate predictions of what I do, then Omega will predict 95% of my (common) one-boxing as well as 95% of my (rare) two-boxing. Further, if I somehow come to believe that Omega has such an understanding, then I will predict that Omega will predict my (rare) two-boxing, and therefore I will predict that two-boxing loses me money, and therefore I will one-box stably.
So, what does it mean for a brain to do one thing 99% of the time and something else 1% of the time?
For the sake of the least convenient world assume that the brain is particularly sensitive to quantum noise. This applies in the actual world too albeit at a far, far lower rate than 1% (but hey… perfect). That leaves a perfect predictor perfectly predicting that in the branches with most of the quantum goo (pick a word) the brain will make one choice while in the others it will make the other.
In this case it becomes a matter of how the counterfactual is specified. The most appropriate one seems to be with Omega filling the large box with an amount of money proportional to how much of the brain will be one boxing. A brain that actively flips a quantum coin would then be granted a large box with half the million.
The only other obvious alternative specification of Omega that wouldn’t break the counterfactual given this this context are a hard cutoff and some specific degree of ‘probability’.
As you say the one boxing remains stable under this uncertainty and even imperfect predictors.
I’m not sure what the quantum-goo explanation is adding here.
If Omega can’t predict the 1% case (whether because it’s due to unpredictable quantum goo, or for whatever other reason… picking a specific explanation only subjects me to a conjunction fallacy) then Omega’s behavior will not reflect the 1% case, and that completely changes the math. Someone for whom the 1% case is two-boxing is then entirely justified in two-boxing in the 1% case, since they ought to predict that Omega cannot predict their two-boxing. (Assuming that they can recognize that they are in such a case. If not, they are best off one-boxing in all cases. Though it follows from our premises that they will two-box 1% of the time anyway, though they might not have any idea why they did that. That said, compatibilist decision theory makes my teeth ache.)
Anyway, yeah, this is assuming some kind of hard cutoff strategy, where Omega puts a million dollars in a box for someone it has > N% confidence will one-box.
If instead Omega puts N% of $1m in the box if Omega has N% confidence the subject will one-box, the result isn’t terribly different if Omega is a good predictor.
I’m completely lost by the “proportional to how much of the brain will be one boxing” strategy. Can you say more about what you mean by this? It seems likely to me that most of the brain neither one-boxes nor two-boxes (that is, is not involved in this choice at all) and most of the remainder does both (that is, performs the same operations in the two-boxing case as in the one-boxing case).
I’m not sure what the quantum-goo explanation is adding here.
A perfect predictor will predict correctly and perfectly that the brain both one boxes and two boxes in different Everett branches (with vastly different weights). This is different in nature to an imperfect predictor that isn’t able to model the behavior of the brain with complete certainty yet given preferences that add up to normal it requires that you use the same math. It means you do not have to abandon the premise “perfect predictor” for the probabilistic reasoning to be necessary.
I’m completely lost by the “proportional to how much of the brain will be one boxing” strategy.
How much weight the everett branches in which it one box have relative to the everett branches in which it two boxes.
Allow me to emphasise:
As you say the one boxing remains stable under this uncertainty and even imperfect predictors.
Omega can’t predict that type of event without being a pre-cog.
Assume that the person choosing the boxes is a whole brain emulation, and that Omega runs a perfect simulation, which guarantees formal identity of Omega’s prediction and person’s actual decision, even though the computations are performed separately.
So the chooser in this case is a fully deterministic system, not a real-live human brain with some chance of random firings screwing up Omega’s prediction?
Wow, that’s an interesting case, and I hadn’t really thought about it! One interesting point though—suppose I am the chooser in that case; how can I tell which simulation I am? Am I the one which runs after Omega made its prediction? Or am I the one which Omega runs in order to make its prediction, and which does have a genuine causal effect on what goes in the boxes? It seems I have no way of telling, and I might (in some strange sense) be both of them. So causal decision theory might advise me to 1-box after all.
So the chooser in this case is a fully deterministic system, not a real-live human brain with some chance of random firings screwing up Omega’s prediction?
This is more of a way of pointing out a special case that shares relevant considerations with TDT-like approach to decision theory (in this extreme identical-simulation case it’s just Hofstadter’s “superrationality”).
If we start from this case and gradually make the prediction model and the player less and less similar to each other (perhaps by making the model less detailed), at which point do the considerations that make you one-box in this edge case break? Clearly, if you change the prediction model just a little bit, correct answer shouldn’t immediately flip, but CDT is no longer applicable out-of-the-box (arguably, even if you “control” two identical copies, it’s also not directly applicable). Thus, a need for generalization that admits imperfect acausal “control” over sufficiently similar decision-makers (and sufficiently accurate predictions) in the same sense in which you “control” your identical copies.
That might give you the right answer when Omega is simulating you perfectly, but presumably you’d want to one-box when Omega was simulating a slightly lossy, non-sentient version of you and only predicted correctly 90% of the time. For that (i.e. for all real world Newcomblike problems), you need a more sophisticated decision theory.
Well no, not necessarily. Again, let’s take Alf’s view. (Note I edited this post recently to correct the display of the matrices)
Remember that Alf has a high probability of 2 boxing, and he knows this about himself. Whether he would actually do better by 1-boxing will depend how well Omega’s “mistaken” simulations are correlated with the (rare, freaky) event that Alf 1 boxes. Basically, Alf knows that Omega is right at least 90% of the time, but this doesn’t require a very sophisticated simulation at all, certainly not in Alf’s own case. Omega can run a very crude simulation, say “a clear” 2-boxer, and not fill box B (so Alf won’t get the $ 1 million. Basically, the game outcome would have a probability matrix like this:
Box B filled. Box B empty.
0. 0.99. Alf 2 boxes
0. 0.01. Alf 1 boxes
Notice that Omega has less than 1% chance of a mistaken prediction.
But, I’m sure you’re thinking, won’t Omega run a fuller simulation with 90% accuracy and produce a probability matrix like this?
Box B filled. Box B empty.
0.099. 0.891. Alf 2 boxes
0.009. 0.001. Alf 1 boxes
Well Omega could do that, but now its probability of error has gone up from 1% to 10%, so why would Omega bother?
Let’s compare to a more basic case: weather forecasting. Say I have a simulation model which takes in the current atmospheric state above a land surface, runs it forward a day, and tries to predict rain. It’s pretty good: if there is going to be rain, then the simulation predicts rain 90% of the time; if there is not going to be rain, then it predicts rain only 10% of the time. But now someone shows me a desert, and asks me to predict rain: I’m not going to use a simulation with a 10% error rate, I’m just going to say “no rain”.
So it seems in the case of Alf. Provided Alf’s chance of 1-boxing is low enough (i.e. lower than the underlying error rate of Omega’s simulations) then Omega can always do best by just saying “a clear 2-boxer” and not filling the B box. Omega may also say to himself “what an utter schmuck” but he can’t fault Alf’s application of decision theory. And this applies whether or not Alf is a causal decision theorist or an evidential decision theorist.
Incidentally, your fire alarm may be practically useless in the circumstances you describe. Depending on the relative probabilities (small probability that the alarm goes off when there is not a fire versus even smaller probability that there genuinely is a fire) then you may find that essentially all the alarms are false alarms. You may get fed up responding to false alarms and ignore them. When predicting very rare events, the prediction system has to be extremely accurate.
This is related to the analysis below about Omega’s simulation being only 90% accurate versus a really convinced 2-boxer (who has only a 1% chance of 1-boxing). Or of simulating rain in a desert.
Ah! Yes, this clarifies matters.
Sure, if Alf believes that Omega has a .95 chance of predicting Alf will two-box regardless of whether or not he does, then Alf should two-box. Similarly, if Beth believes Omega has a .95 chance of predicting Beth will one-box regardless of whether or not she does, then she also should two-box. (Though if she does, she should immediately lower her earlier confidence that she’s the sort of person who one-boxes.)
This is importantly different from the standard Newcomb’s problem, though.
You seem to be operating under the principle that if a condition is unlikely (e.g., Alf 1-boxing) then it is also unpredictable. I’m not sure where you’re getting that from.
By way of analogy… my fire alarm is, generally speaking, the sort of thing that remains silent… if I observe it in six-minute intervals for a thousand observations, I’m pretty likely to find it silent in each case. However, if I’m a good predictor of fire alarm behavior, I don’t therefore assume that if there’s a fire, it will still remain silent.
Rather, as a good predictor of fire alarms, what my model of fire alarms tells me is that “when there’s no fire, I’m .99+ confident it will remain silent; when there is a fire, I’m .99+ confident it will make noise.” I can therefore test to see if there’s a fire and, if there is, predict it will make noise. Its noise is rare, but predictable (for a good enough predictor of fire alarm behavior).
Remember I have two models of how Omega could work.
1) Omega is in essence an excellent judge of character. It can reliably decide which of its candidates is “the sort of person who 1-boxes” and which is “the sort of person who 2-boxes”. However, if he chooser actually does something extremely unlikely and out of character, Omega will get its prediction wrong. This is a model for Omega that I could actually see working, so it is the most natural way for me to interpret Newcomb’s thought experiment.
If Omega behaves like this, then I think causal and evidential decision theory align. Both tell the chooser to 2-box, unless the chooser has already pre-committed to 1-boxing. Both imply the chooser should pre-commit to 1-boxing (if they can).
2) Omega is a perfect predictor, and always gets its predictions right. I can’t actually see how his model would work without reverse causation. If reverse causatiion is implied by the problem statement, or choosers can reasonably think it is implied, then both causal and evidential decision theory align and tell the chooser to 1-box.
From the sound of things, you are describing a third model in which Omega can not only judge character, but can also reliably decide whether someone will act out of character or not. When faced with “the sort of person who 1-boxes”, but then—out of character − 2 boxes after all, Omega will still with high probability guess correctly that the 2-boxing is going to happen, and so withhold the $ 1 million.
I tend to agree that in this third model causal and evidential decision theory may become decoupled, but again I’m not really sure how this model works, or whether it requires backward causation again. I think it could work if the causal factors leading the chooser to act “out of character” in the particular case are already embedded in the chooser’s brain state when scanned by Omega, so at that stage it is already highly probable that the chooser will act out of character this time. But the model won’t work if the factors causing out of character behaviour arise because of very rare, random, brain events happening after the scanning (say a few stray neurons fire which in 99% of cases wouldn’t fire after the scanned brain state, and these cause a cascade eventually leading to a different choice). Omega can’t predict that type of event without being a pre-cog.
Thanks anyway though; you’ve certainly made me think about the problem a bit further...
So, what does it mean for a brain to do one thing 99% of the time and something else 1% of the time?
If the 1% case is a genuinely random event, or the result of some mysterious sort of unpredictable free will, or otherwise something that isn’t the effect of the causes that precede it, and therefore can’t be predicted short of some mysterious acausal precognition, then I agree that it follows that if Omega is a good-but-not-perfect predictor, then Omega cannot predict the 1% case, and Newcomb’s problem in its standard form can’t be implemented even in principle, with all the consequences previously discussed.
Conversely, if brain events—even rare ones—are instead the effects of causes that precede them, then a good-but-not-perfect predictor can make good-but-not-perfect predictions of the 1% case just as readily as the 99% case, and these problems don’t arise.
Personally, I consider brain events the effects of causes that precede them. So if I’m the sort of person who one-boxes 99% of the time and two-boxes 1% of the time, and Omega has a sufficient understanding of the causes of human behavior to make 95% accurate predictions of what I do, then Omega will predict 95% of my (common) one-boxing as well as 95% of my (rare) two-boxing. Further, if I somehow come to believe that Omega has such an understanding, then I will predict that Omega will predict my (rare) two-boxing, and therefore I will predict that two-boxing loses me money, and therefore I will one-box stably.
For the sake of the least convenient world assume that the brain is particularly sensitive to quantum noise. This applies in the actual world too albeit at a far, far lower rate than 1% (but hey… perfect). That leaves a perfect predictor perfectly predicting that in the branches with most of the quantum goo (pick a word) the brain will make one choice while in the others it will make the other.
In this case it becomes a matter of how the counterfactual is specified. The most appropriate one seems to be with Omega filling the large box with an amount of money proportional to how much of the brain will be one boxing. A brain that actively flips a quantum coin would then be granted a large box with half the million.
The only other obvious alternative specification of Omega that wouldn’t break the counterfactual given this this context are a hard cutoff and some specific degree of ‘probability’.
As you say the one boxing remains stable under this uncertainty and even imperfect predictors.
I’m not sure what the quantum-goo explanation is adding here.
If Omega can’t predict the 1% case (whether because it’s due to unpredictable quantum goo, or for whatever other reason… picking a specific explanation only subjects me to a conjunction fallacy) then Omega’s behavior will not reflect the 1% case, and that completely changes the math. Someone for whom the 1% case is two-boxing is then entirely justified in two-boxing in the 1% case, since they ought to predict that Omega cannot predict their two-boxing. (Assuming that they can recognize that they are in such a case. If not, they are best off one-boxing in all cases. Though it follows from our premises that they will two-box 1% of the time anyway, though they might not have any idea why they did that. That said, compatibilist decision theory makes my teeth ache.)
Anyway, yeah, this is assuming some kind of hard cutoff strategy, where Omega puts a million dollars in a box for someone it has > N% confidence will one-box.
If instead Omega puts N% of $1m in the box if Omega has N% confidence the subject will one-box, the result isn’t terribly different if Omega is a good predictor.
I’m completely lost by the “proportional to how much of the brain will be one boxing” strategy. Can you say more about what you mean by this? It seems likely to me that most of the brain neither one-boxes nor two-boxes (that is, is not involved in this choice at all) and most of the remainder does both (that is, performs the same operations in the two-boxing case as in the one-boxing case).
A perfect predictor will predict correctly and perfectly that the brain both one boxes and two boxes in different Everett branches (with vastly different weights). This is different in nature to an imperfect predictor that isn’t able to model the behavior of the brain with complete certainty yet given preferences that add up to normal it requires that you use the same math. It means you do not have to abandon the premise “perfect predictor” for the probabilistic reasoning to be necessary.
How much weight the everett branches in which it one box have relative to the everett branches in which it two boxes.
Allow me to emphasise:
(I think we agree?)
Ah, I see what you mean.
Yes, I think we agree. (I had previously been unsure.)
Assume that the person choosing the boxes is a whole brain emulation, and that Omega runs a perfect simulation, which guarantees formal identity of Omega’s prediction and person’s actual decision, even though the computations are performed separately.
So the chooser in this case is a fully deterministic system, not a real-live human brain with some chance of random firings screwing up Omega’s prediction?
Wow, that’s an interesting case, and I hadn’t really thought about it! One interesting point though—suppose I am the chooser in that case; how can I tell which simulation I am? Am I the one which runs after Omega made its prediction? Or am I the one which Omega runs in order to make its prediction, and which does have a genuine causal effect on what goes in the boxes? It seems I have no way of telling, and I might (in some strange sense) be both of them. So causal decision theory might advise me to 1-box after all.
This is more of a way of pointing out a special case that shares relevant considerations with TDT-like approach to decision theory (in this extreme identical-simulation case it’s just Hofstadter’s “superrationality”).
If we start from this case and gradually make the prediction model and the player less and less similar to each other (perhaps by making the model less detailed), at which point do the considerations that make you one-box in this edge case break? Clearly, if you change the prediction model just a little bit, correct answer shouldn’t immediately flip, but CDT is no longer applicable out-of-the-box (arguably, even if you “control” two identical copies, it’s also not directly applicable). Thus, a need for generalization that admits imperfect acausal “control” over sufficiently similar decision-makers (and sufficiently accurate predictions) in the same sense in which you “control” your identical copies.
That might give you the right answer when Omega is simulating you perfectly, but presumably you’d want to one-box when Omega was simulating a slightly lossy, non-sentient version of you and only predicted correctly 90% of the time. For that (i.e. for all real world Newcomblike problems), you need a more sophisticated decision theory.
Well no, not necessarily. Again, let’s take Alf’s view. (Note I edited this post recently to correct the display of the matrices)
Remember that Alf has a high probability of 2 boxing, and he knows this about himself. Whether he would actually do better by 1-boxing will depend how well Omega’s “mistaken” simulations are correlated with the (rare, freaky) event that Alf 1 boxes. Basically, Alf knows that Omega is right at least 90% of the time, but this doesn’t require a very sophisticated simulation at all, certainly not in Alf’s own case. Omega can run a very crude simulation, say “a clear” 2-boxer, and not fill box B (so Alf won’t get the $ 1 million. Basically, the game outcome would have a probability matrix like this:
Notice that Omega has less than 1% chance of a mistaken prediction.
But, I’m sure you’re thinking, won’t Omega run a fuller simulation with 90% accuracy and produce a probability matrix like this?
Well Omega could do that, but now its probability of error has gone up from 1% to 10%, so why would Omega bother?
Let’s compare to a more basic case: weather forecasting. Say I have a simulation model which takes in the current atmospheric state above a land surface, runs it forward a day, and tries to predict rain. It’s pretty good: if there is going to be rain, then the simulation predicts rain 90% of the time; if there is not going to be rain, then it predicts rain only 10% of the time. But now someone shows me a desert, and asks me to predict rain: I’m not going to use a simulation with a 10% error rate, I’m just going to say “no rain”.
So it seems in the case of Alf. Provided Alf’s chance of 1-boxing is low enough (i.e. lower than the underlying error rate of Omega’s simulations) then Omega can always do best by just saying “a clear 2-boxer” and not filling the B box. Omega may also say to himself “what an utter schmuck” but he can’t fault Alf’s application of decision theory. And this applies whether or not Alf is a causal decision theorist or an evidential decision theorist.
Incidentally, your fire alarm may be practically useless in the circumstances you describe. Depending on the relative probabilities (small probability that the alarm goes off when there is not a fire versus even smaller probability that there genuinely is a fire) then you may find that essentially all the alarms are false alarms. You may get fed up responding to false alarms and ignore them. When predicting very rare events, the prediction system has to be extremely accurate.
This is related to the analysis below about Omega’s simulation being only 90% accurate versus a really convinced 2-boxer (who has only a 1% chance of 1-boxing). Or of simulating rain in a desert.