Academic website: https://www.andrew.cmu.edu/user/coesterh/
Caspar Oesterheld(Caspar Oesterheld)
Two-boxing, smoking and chewing gum in Medical Newcomb problems
Extracting Money from Causal Decision Theorists
I think in the social choice literature, people almost always mean preference utilitarianism when they say “utilitarianism”, whereas in the philosophical/ethics literature people are more likely to mean hedonic utilitarianism. I think the reason for this is that in the social choice and somewhat adjacent game (and decision) theory literature, utility functions have a fairly solid foundation as a representation of preferences of rational agents. (For example, Harsanyi’s “[preference] utilitarian theorem” paper and Nash’s paper on the Nash bargaining solution make very explicit reference to this foundation.) Whereas there is no solid foundation for numeric hedonic welfare (at least not in this literature, but also not elsewhere as far as I know).
Since Briggs [1] shows that EDT+SSA and CDT+SIA are both ex-ante-optimal policies in some class of cases, one might wonder whether the result of this post transfers to EDT+SSA. I.e., in memoryless POMDPs, is every (ex ante) optimal policy also consistent with EDT+SSA in a similar sense. I think it is, as I will try to show below.
Given some existing policy , EDT+SSA recommends that upon receiving observation we should choose an action from (For notational simplicity, I’ll assume that policies are deterministic, but, of course, actions may encode probability distributions.) Here, if and otherwise. is the SSA probability of being in state of the environment trajectory given the observation and the fact that one uses the policy .
The SSA probability is zero if and otherwise. Here, is the number of times occurs in . Note that this is the minimal reference class version of SSA, also known as the double-halfer rule (because it assigns 1⁄2 probability to tails in the Sleeping Beauty problem and sticks with 1⁄2 if it’s told that it’s Monday). is the (regular, non-anthropic) probability of the sequence of states , given that is played and is observed at least once. If (as in the sum above) is observed at least once in , we can rewrite this as Importantly, note that is constant in , i.e., the probability that you observe at least once cannot (in the present setting) depend on what you would do when you observe .
Inserting this into the above, we get where the first sum on the right-hand side is over all histories that give rise to observation at some point. Dividing by the number of agents with observation in a history and setting the policy for all agents at the same time cancel each other out, such that this equals Obviously, any optimal policy chooses in agreement with this. But the same disclaimers apply; if there are multiple observations, then multiple policies might satisfy the right-hand side of this equation and not all of these are optimal.
[1] Rachael Briggs (2010): Putting a value on Beauty. In Tamar Szabo Gendler and John Hawthorne, editors, Oxford Studies in Epistemology: Volume 3, pages 3–34. Oxford University Press, 2010. http://joelvelasco.net/teaching/3865/briggs10-puttingavalueonbeauty.pdf
- EDT with updating double counts by 12 Oct 2021 4:40 UTC; 56 points) (
- CDT=EDT=UDT by 13 Jan 2019 23:46 UTC; 39 points) (
- 16 Jan 2019 11:06 UTC; 19 points) 's comment on CDT=EDT=UDT by (
- 28 Mar 2018 12:19 UTC; 1 point) 's comment on Announcement: AI alignment prize winners and next round by (
As one further data point, I also heard people close to/working at Anthropic giving “We won’t advance the state of the art.”-type statements, though I never asked about specifics.
My sense is also that Claude 3 Opus is only slightly better than the best published GPT-4. To add one data point: I happen to work on a benchmark right now and on that benchmark, Opus is only very slightly better than gpt-4-1106. (See my X/Twitter post for detailed results.) So, I agree with LawrenceC’s comment that they’re arguably not significantly advancing the state of the art.
I suppose even if Opus is only slightly better (or even just perceived to be better) and even if we all expect OpenAI to release a better GPT-4.5 soon, Anthropic could still take a bunch of OpenAI’s GPT-4 business with this. (I’ll probably switch from ChatGPT-4 to Claude, for instance.) So it’s not that hard to imagine an internal OpenAI email saying, “Okay, folks, let’s move a bit faster with these top-tier models from now on, lest too many people switch to Claude.” I suppose that would already be quite worrying to people here. (Whereas, people would probably worry less if Anthropic took some of OpenAI’s business by having models that are slightly worse but cheaper or more aligned/less likely to say things you wouldn’t want models to say in production.)
Naturalized induction – a challenge for evidential and causal decision theory
Moral realism and AI alignment
I think I sort of agree, but...
It’s often difficult to prove a negative and I think the non-existence of a crisp definition of any given concept is no exception to this rule. Sometimes someone wants to come up with a crisp definition of a concept for which I suspect no such definition to exist. I usually find that I have little to say and can only wait for them to try to actually provide such a definition. And sometimes I’m surprised by what people can come up with. (Maybe this is the same point that Roman Leventov is making.)
Also, I think there are many different ways in which concepts can be crisp or non-crisp. I think cooperation can be made crisp in some ways and not in others.
For example, I do think that (in contrast to human values) there are approximate characterizations of cooperation that are useful, precise and short. For example: “Cooperation means playing Pareto-better equilibria.”
One way in which I think cooperation isn’t crisp, is that you can give multiple different sensible definitions that don’t fully agree with each other. (For example, some definitions (like the above) will include coordination in fully cooperative (i.e., common-payoff) games, and others won’t.) I think in that way it’s similar to comparing sets by size, where you can give lots of useful, insightful, precise definitions that disagree with each other. For example, bijection, isomorphism, and the subset relationship can each tell us when one set is larger than or as large as another, but they sometimes disagree and nobody expects that one can resolve the disagreement between the concepts or arrive at “one true definition” of whether one set is larger than another.
When applied to the real world rather than rational agent models, I would think we also inherit fuzziness from the application of the rational agent model to the real world. (Can we call the beneficial interaction between two cells cooperation? Etc.)
I think this is a good overview, but most of the views proposed here seem contentious and the arguments given in support shouldn’t suffice to change the mind of anyone who has thought about these questions for a bit or who is aware of the disagreements about them within the community.
Getting alignment right accounts for most of the variance in whether an AGI system will be positive for humanity.
If your values differ from those of the average human, then this may not be true/relevant. E.g., I would guess that for a utilitarian current average human values are worse than, e.g., 90% “paperclipping values” and 10% classical utilitarianism.
Also, if gains from trade between value systems are big, then a lot of value may come from ensuring that the AI engages in acausal trade (https://wiki.lesswrong.com/wiki/Acausal_trade ). This is doubly persuasive if you already see your own policies as determining what agents with similar decision theories but different values do elsewhere in the universe. (See, e.g., section 4.6.3 of “Multiverse-wide Cooperation via Correlated Decision Making”.)
Given timeline uncertainty, it’s best to spend marginal effort on plans that assume / work in shorter timelines.
Stated simply: If you don’t know when AGI is coming, you should make sure alignment gets solved in worlds where AGI comes soon.
I guess the question is what “soon” means. I agree with the argument provided in the quote. But there are also some arguments to work on longer timelines, e.g.:
If it’s hard and most value comes from full alignment, then why even try to optimize for very short timelines?
Similarly, there is a “social” difficulty of getting people in AI to notice your (or the AI safety community’s) work. Even if you think you could write down within a month a recipe for increasing the probability of AI being aligned by a significant amount, you would probably need much more than a month to make it significantly more likely to get people to consider applying your recipe.
It seems obvious that most people shouldn’t think too much about extremely short timelines (<2 years) or the longest plausible timelines (>300 years). So, these arguments together probably point to something in the middle of these and the question is where. Of course, it also depends on one’s beliefs about AI timelines.
To me it seems that the concrete recommendations (aside from the “do AI safety things”) don’t have anything to do with the background assumptions.
As one datapoint, fields like computer science, engineering and mathematics seem to make a lot more progress than ones like macroeconomics, political theory, and international relations.
For one, “citation needed”. But also: the alternative to doing technical AI safety work isn’t to do research in politics but to do political activism (or lobbying or whatever), i.e. to influence government policy.
As your “technical rather than political” point currently stands, it’s applicable to any problem, but it is obviously invalid at this level of generality. To argue plausibly that technical work on AI safety is more important than AI strategy (which is plausibly true), you’d have to refer to some specifics of the problems related to AI.
This means that the model can and will implicitly sacrifice next-token prediction accuracy for long horizon prediction accuracy.
Are you claiming this would happen even given infinite capacity?
I think that janus isn’t claiming this and I also think it isn’t true. I think it’s all about capacity constraints. The claim as I understand it is that there are some intermediate computations that are optimized both for predicting the next token and for predicting the 20th token and that therefore have to prioritize between these different predictions.
> I tried to understand Caspar’s EDT+SSA but was unable to figure it out. Can someone show how to apply it to an example like the AMD to help illustrate it?
Sorry about that! I’ll try to explain it some more. Let’s take the original AMD. Here, the agent only faces a single type of choice—whether to EXIT or CONTINUE. Hence, in place of a policy we can just condition on when computing our SSA probabilities. Now, when using EDT+SSA, we assign probabilities to being a specific instance in a specific possible history of the world. For example, we assign probabilities of the form , which denotes the probability that given I choose to CONTINUE with probability , history (a.k.a. CONTINUE, EXIT) is actual and that I am the instance intersection (i.e., the first intersection). Since we’re using SSA, these probabilities are computed as follows:That is, we first compute the probability that the history itself is actual (given ). Then we multiply it by the probability that within that history I am the instance at , which is just 1 divided by the number of instances of myself in that history, i.e. 2.
Now, the expected value according to EDT + SSA given can be computed by just summing over all possible situations, i.e. over all combinations of a history and a position within that history and multiplying the probability of that situation with the utility given that situation:
And that’s exactly the ex ante expected value (or UDT-expected value, I suppose) of continuing with probability . Hence, EDT+SSA’s recommendation in AMD is the ex ante optimal policy (or UDT’s recommendation, I suppose). This realization is not original to myself (though I came up with it independently in collaboration with Johannes Treutlein) -- the following papers make the same point:
Rachael Briggs (2010): Putting a value on Beauty. In Tamar Szabo Gendler and John Hawthorne, editors, Oxford Studies in Epistemology: Volume 3, pages 3–34. Oxford University Press, 2010. http://joelvelasco.net/teaching/3865/briggs10-puttingavalueonbeauty.pdf
Wolfgang Schwarz (2015): Lost memories and useless coins: revisiting the absentminded driver. In: Synthese. https://www.umsu.de/papers/driver-2011.pdf
My comment generalizes these results a bit to include cases in which the agent faces multiple different decisions.
- 16 Jan 2019 23:54 UTC; 2 points) 's comment on In memoryless Cartesian environments, every UDT policy is a CDT+SIA policy by (
Caveat: The version of EDT provided above only takes dependences between instances of EDT making the same observation into account. Other dependences are possible because different decision situations may be completely “isomorphic”/symmetric even if the observations are different. It turns out that the result is not valid once one takes such dependences into account, as shown by Conitzer [2]. I propose a possible solution in https://casparoesterheld.com/2017/10/22/a-behaviorist-approach-to-building-phenomenological-bridges/ . Roughly speaking, my solution is to identify with all objects in the world that are perfectly correlated with you. However, the underlying motivation is unrelated to Conitzer’s example.
[2] Vincent Conitzer: A Dutch Book against Sleeping Beauties Who Are Evidential Decision Theorists. Synthese, Volume 192, Issue 9, pp. 2887-2899, October 2015. https://arxiv.org/pdf/1705.03560.pdf
Request for feedback on a paper about (machine) ethics
At least in this case (celebrities and their largely unknown parents), I would predict the opposite. That is, people are more likely to be able to correctly answer “Who is Mary Lee Pfeiffer’s son?” than “Who is Tom Cruise’s mother?” Why? Because there are lots of terms / words / names that people can recognize passively but not produce. Since Mary Lee Pfeiffer is not very well known, I think Mary Lee Pfeiffer will be recognizable but not producable to lots of people. (Of people who know Mary Lee Pfeiffer in any sense, I think the fraction of people who can only recognize her name is high.) As another example, I think “Who was born in Ulm?” might be answered correctly by more people than “Where was Einstein born?”, even though “Einstein was born in Ulm” is a more common sentence for people to read than “Ulm is the city that Einstein was born in”.
If I had to run an experiment to test whether similar effects apply in humans, I’d probably try to find cases where A and B in and of themselves are equally salient but the association A → B is nonetheless more salient than the association B → A. The alphabet is an example of this (where the effect is already confirmed).
I looked at the version 2017-12-30 10:48:11Z.
Overall, I think it’s a nice, systematic overview. Below are some comments.
I should note that I’m not very expert on these things. This is also why the additional literature I mention is mostly weakly related stuff from FRI, the organization I work for. Sorry about that.
An abstract would be nice.
Locators in the citations would be useful, i.e. “Beckstead (2013, sect. XYZ)” instead of just “Beckstead (2013)” when you talk about some specific section of the Beckstead paper. (Cf. section “Pageless Documentation” of the humurous Academic Citation Practice: A Sinking Sheep? by Ole Bjørn Rekdal.)
>from a totalist, consequentialist, and welfarist (but not necessarily utilitarian) point of view
I don’t think much of your analysis assumes welfarism (as I understand it)? Q_w could easily denote things other than welfare (e.g., how virtue ethical, free, productive, autonomous, natural, the mean person is), right? (I guess some of the discussion sections are fairly welfarist, i.e. they talk about suffering, etc., rather than freedom and so forth.)
>an existential risk as one where an adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.
Maybe some people would interpret this definition as excluding some of the “shrieks” and “whimpers”, since in some of them, “humanity’s potential is realized” in that it colonizes space, but not in accordance with, e.g., the reader’s values. Anyway, I think this definition is essentially a quote from Bostrom (maybe use quotation marks?), so it’s alright.
>The first is the probability P of reaching time t.
Maybe say more about why you separate N_w(t) (in the continuous model) into P(t) and N(t)?
I also don’t quite understand whether equation 1 is intended as the expected value of the future or as the expected value of a set of futures w that all have the same N_w(t) and Q_w(t). The problem is that if it’s the expected value of the future, I don’t get how you can simplify something like
into the right side of your equation 1. (E.g., you can’t just let N(t) and Q(t) denote expected numbers of moral patients and expected mean qualities of life, because the mean qualities in larger worlds ought to count for more, right?)
I suspect that when reading the start of sect. 3.1, a lot of readers will wonder whether you endorse all the assumptions underlying your model of P(t). In particular, I would guess that people would disagree with the following two assumptions:
-> Short term x-risk reduction (r_1) doesn’t have any effect on long-term risk (r). Perhaps this is true for some fairly specific work on preventing extinction but it seems less likely for interventions like building up the UN (to avoid all kinds of conflict, coordinate against risks, etc.).
-> Long-term extinction risk is constant. I haven’t thought much about these issues but I would guess that extinction risk becomes much lower, once there is a self-sustaining colony on Mars.
Reading further, I see that you address these in sections 3.2 and 3.3. Maybe you could mention/refer to these somewhere near the start of sect. 3.1.
On page 3, you say that the derivative of -P(t) w.r.t. r_1 denotes the value of reducing r_1 by one unit. This is true in this case because P(t) is linear in r_1. But in general, the value of reducing r_1 by one unit is just P(t,r_1-1)-P(t,r_1), right?
Is equation 3, combined with the view that the cost of one unit of f1 is constant, consistent with Ord’s “A plausible model would be that it is roughly as difficult to halve the risk per century, regardless of its starting probability, and more generally, that it is equally difficult to reduce it by some proportion regardless of its absolute value beforehand.”? With your model, it looks like bringing f_1 from 0 to 0.5 and thus halfing r_1 is just as expensive as bringing f_1 from 0.5 to 1.
On p. 7, “not to far off”—probably you mean “too”?
>For example, perhaps we will inevitably develop some hypothetical weapons that give so large an advantage to offence over defence that civilisation is certain to be destroyed.
AI risk is another black ball that will become more accessible. But maybe you would rather not model it as extinction. At least AI risk doesn’t necessarily explain the Fermi paradox and AIs may create sentient beings.
>Ord argues that we may be able to expect future generations to be more interested in risk reduction, implying increasing f_i
I thought f_i was meant to model the impact that we can have on r_i? So, to me it seems more sensible to model the involvement of future generations, to the extent that we can’t influence it, as a “a kind of event E” (as you propose) or, more generally, as implying that the non-intervention risk levels r_i decrease.
>This would only reinforce the case for extinction risk reduction.
It seems that future generations caring about ERR makes short-term ERR more important (because the long-term future is longer and thus can contain more value). But it makes long-term ERR less important, because future generations will, e.g., do AI safety research anyway. (In section “Future resources” of my blog post Complications in evaluating neglectedness, I make the general point that for evaluating the neglectedness of an intervention, one has to look at how many resources future generations will invest into that intervention.)
>There is one case in which it clearly is not: if space colonisation is in fact likely to involve risk-independent islands. Then high population goes with low risk, increasing the value of the future relative to the basic model
(I find risk-independent islands fairly plausible.)
>The expected number of people who will live in period t is
You introduced N(t) as the number of morally relevant beings (rather than “people”).
>However, this increase in population may be due to stop soon,
Although it is well-known that some predict population to stagnate at 9 billion or so, a high-quality citation would be nice.
>The likelihood of space colonisation, a high-profile issue on which billions of dollars is spent per year (Masters, 2015), also seems relatively hard to affect. Extinction risk reduction, on the other hand, is relatively neglected (Bostrom, 2013; Todd, 2017), so it could be easier to achieve progress in this area.
I have only briefly (in part due to the lack of locators) checked the two sources, but it seems that this varies strongly between different extinction risks. For instance, according to Todd (2017), >300bn (and thus much more than on space colonization) is spent on climate change, 1-10bn on nuclear security, 1bn on extreme pandemic prevention. So, overall much more money goes into extinction risk reduction than into space colonization. (This is not too surprising. People don’t want to die, and they don’t want their children or grandchildren to die. They don’t care nearly as much about whether some elite group of people will live on Mars in 50 years.)
Of course, there a lot of complications to this neglectedness analysis. (All three points I discuss in Complications in evaluating neglectedness seem to apply.)
>Some people believe that it’s nearly impossible to have a consistent impact on Q(t) so far into the future.
Probably a reference would be good. I guess to the extent that we can’t affect far future Q(t), we also can’t affect far future r_i.
>However, this individual may be biased against ending things, for instance because of the survival instinct, and so could individuals or groups in the future. The extent of this bias is an open question.
It’s also a bit unclear (at least based on hat you write) what legitimizes calling this a bias, rather than simply a revealed preference not to die (even in cases in which you or I as outside observers might think it to be preferable not to live) and thus evidence that their lives are positive. Probably one has to argue via status quo bias or sth like that.
>We may further speculate that if the future is controlled by altruistic values, even powerless persons are likely to have lives worth living. If society is highly knowledgeable and technologically sophisticated, and decisions are made altruistically, it’s plausible that many sources of suffering would eventually be removed, and no new ones created unnecessarily. Selfish values, on the other hand, do not care about the suffering of powerless sentients.
This makes things sound a more binary than they actually are. (I’m sure you’re aware of this.) In the usual sense of the word, people could be “altruistic” but in a non-consequentialist way. There may be lots of suffering in such worlds. (E.g., some libertarians may be regard intervening in the economy as unethical even if companies start creating slaves. A socialist, on the other hand, may view capitalism as fundamentally unjust, try to regulate/control the economy and thus cause a lot of poverty.) Also, even if someone is altruistic in a fairly consequentialist way, they may still not care about all beings that you/I/the reader cares about. E.g., economists tend to be consequentialists but rarely consider animal welfare.
I think for the animal suffering (both wild animals and factory farming) it is worth noting that it seems fairly unlikely that this will be economically efficient in the long term, but that the general underlying principles (Darwinian suffering and exploiting the powerless) might carry over to other beings (like sentient AIs).
Another way in which the future may be negative would be the Malthusian trap btw. (Of course, some would regard at least some Malthusian trap scenarios as positive, see, e.g., Robin Hanson’s The Age of Em.) Presumably this belongs to 5.2.1, since it’s a kind of coordination failure.
As you say, I think the option value argument isn’t super persuasive, because it seems unlikely that the people in power in a million years share my (meta-)values (or agree with the way I do compromise).
Re 5.2.3: Another relevant reference on why one should cooperate—which is somewhat separate from the point that if mutual cooperation works out the gains from trade are great—is Brian Tomasik’s Reasons to Be Nice to Other Value Systems.
>One way to increase Q(t) is to advocate for positive value changes in the direction of greater consideration for powerless sentients, or to promote moral enhancement (Persson and Savulescu, 2008). Another approach might be to work to improve political stability and coordination, making conflict less likely as well as increasing the chance that moral progress continues.
Relevant:
https://foundational-research.org/international-cooperation-vs-ai-arms-race/
http://reducing-suffering.org/values-spreading-often-important-extinction-risk/
Publication on formalizing preference utilitarianism in physical world models
Here’s a simple toy model that illustrates the difference between 2 and 3 (that doesn’t talk about attention layers, etc.).
Say you have a bunch of triplets . Your want to train a model that predicts from and from .
Your model consists of three components: . It makes predictions as follows:
(Why have such a model? Why not have two completely separate models, one for predicting and one for predicting ? Because it might be more efficient to use a single both for predicting and for predicting , given that both predictions presumably require “interpreting” .)
So, intuitively, it first builds an “inner representation” (embedding) of . Then it sequentially makes predictions based on that inner representation.
Now you train and to minimize the prediction loss on the parts of the triplets. Simultaneously you train to minimize prediction loss on the full triplets. For example, you update and with the gradients
and you update and with the gradients
.
(The here is the “true” , not one generated by the model itself.)This training pressures to be myopic in the second and third sense described in the post. In fact, even if we were to train with the predicted by rather than the true , is pressured to be myopic.
Type 3 myopia: Training doesn’t pressure to output something that makes the follow an easier-to-predict (computationally or information-theoretically) distribution. For example, imagine that on the training data implies , while under , follows some distribution that depends in complicated ways on . Then will not try to predict more often.
Type 2 myopia: won’t try to provide useful information to in its output, even if it could. For example, imagine that the s are strings representing real numbers. Imagine that is always a natural number, that is the -th Fibonacci number and is the -th Fibonacci number. Imagine further that the model representing is large enough to compute the -th Fibonacci number, while the model representing is not. Then one way in which one might think one could achieve low predictive loss would be for to output the -th Fibonacci number and then encode, for example, the -th Fibonacci number in the decimal digits. (E.g., .) And then computes the -th Fibonacci number from the -th decimal. But the above training will not give rise to this strategy, because gets the true as input, not the one produced by . Further, even if we were to change this, there would still be pressure against this strategy because () is not optimized to give useful information to . (The gradient used to update doesn’t consider the loss on predicting .) If it ever follows the policy of encoding information in the decimal digits, it will quickly learn to remove that information to get higher prediction accuracy on .
Of course, still won’t be pressured to be type-1-myopic. If predicting requires predicting , then will be trained to predict (“plan”) .
(Obviously, $g_2$ is pressured to be myopic in this simple model.)
Now what about ? Well, is optimized both to enable predicting from and predicting from . Therefore, if resources are relevantly constrained in some way (e.g., the model computing is small, or the output of is forced to be small), will sometimes sacrifice performance on one to improve performance on the other. So, adapting a paragraph from the post: The trained model for (and thus in some sense the overall model) can and will sacrifice accuracy on to achieve better accuracy on . In particular, we should expect trained models to find an efficient tradeoff between accuracy on and accuracy on . When is relatively easy to predict, will spend most of its computation budget on predicting .
So, is not “Type 2” myopic. Or perhaps put differently: The calculations going into predicting aren’t optimized purely for predicting .
However, is still “Type 3” myopic. Because the prediction made by isn’t fed (in training) as an input to or the loss, there’s no pressure towards making influence the output of in a way that has anything to do with . (In contrast to the myopia of , this really does hinge on not using in training. If mattered in training, then there would be pressure for to trick into performing calculations that are useful for predicting . Unless you use stop-gradients...)
* This comes with all the usual caveats of course. In principle, the inductive bias may favor a situationally aware model that is extremely non-myopic in some sense.
Great to see more work on surrogate goals/SPIs!
>Personally, the author believes that SPI might “add up to normality”—that it will be a sort of reformulation of existing (informal) approaches used by humans, with similar benefits and limitations.
I’m a bit confused by this claim. To me it’s a bit unclear what you mean by “adding up to normality”. (E.g.: Are you claiming that A) humans in current-day strategic interactions shouldn’t change their behavior in response to learning about SPIs (because 1) they are already using them or 2) doing things that are somehow equivalent to them)? Or are you claiming that B) they don’t fundamentally change game-theoretic analysis (of any scenario/most scenarios)? Or C) are you saying they are irrelevant for AI v. AI interactions? Or D) that the invention of SPIs will not revolutionize human society, make peace in the middle east, …) Some of the versions seem clearly false to me. (E.g., re C, even if you think that the requirements for the use of SPIs are rarely satisfied in practice, it’s still easy to construct simple, somewhat plausible scenarios / assumptions (see our paper) under which SPIs do seem do matter substantially for game-theoretic analysis.) Some just aren’t justified at all in your post. (E.g., re A1, you’re saying that (like myself) you find this all confusing and hard to say.) And some are probably not contrary to what anyone else believes about surrogate goals / SPIs. (E.g., I don’t know anyone who makes particularly broad or grandiose claims about the use of SPIs by humans.)
My other complaint is that in some places you state some claim X in a way that (to me) suggests that you think that Tobi Baumann or Vince and I (or whoever else is talking/writing about surrogate goals/SPIs) have suggested that X is false, when really Tobi, Vince and I are very much aware of X and have (although perhaps to an insufficient extent) stated X. Here are three instances of this (I think these are the only three), the first one being most significant.
The main objection of the post is that while adopting an SPI, the original players must keep a bunch of things (at least approximately) constant(/analogous to the no-SPI counterfactual) even when they have an incentive to change that thing, and they need to do this credibly (or, rather, make it credible that they aren’t making any changes). You argue that this is often unrealistic. Well, the initial reaction of mine was: “Sure, I know these things!” (Relatedly: while I like the bandit v caravan example, this point can also be illustrated with any of the existing examples of SPIs and surrogate goals.) I also don’t think the assumption is that unrealistic. It seems that one substantial part of your complaint is that besides instructing the representative/self-modifying the original player/principal can do other things about the threat (like advocating a ban on real or water guns). I agree that this is important. If in 20 years I instruct an AI to manage my resources, it would be problematic if in the meantime I make tons of decisions (e.g., about how to train my AI systems) differently based on my knowledge that I will use surrogate goals anyway. But it’s easy to come up scenarios where this is not a problem. E.g., when an agent considers immediate self-modification, *all* her future decisions will be guided by the modified u.f. Or when the SPI is applied to some isolated interaction. When all is in the representative’s hand, we only need to ensure that the *representative* always acts in whatever way the representative acts in the same way it would act in a world where SPIs aren’t a thing.
And I don’t think it’s that difficult to come up with situations in which the latter thing can be comfortably achieved. Here is one scenario. Imagine the two of us play a particular game G with SPI G’. The way in which we play this is that we both send a lawyer to a meeting and then the lawyers play the game in some way. Then we could could mutually commit (by contract) to pay our lawyers in proportion to the utilities they obtain in G’ (and to not make any additional payments to them). The lawyers at this point may know exactly what’s going on (that we don’t really care about water guns, and so on) -- but they are still incentivized to play the SPI game G’ to the best of their ability. You might even beg your lawyer to never give in (or the like), but the lawyer is incentivized to ignore such pleas. (Obviously, there could still be various complications. If you hire the lawyer only for this specific interaction and you know how aggressive/hawkish different lawyers are (in terms of how they negotiate), you might be inclined to hire a more aggressive one with the SPI. But you might hire the lawyer you usually hire. And in practice I doubt that it’d be easy to figure out how hawkish different lawyers are.
Overall I’d have appreciated more detailed discussion of when this is realistic (or of why you think it rarely is realistic). I don’t remember Tobi’s posts very well, but our paper definitely doesn’t spend much space on discussing these important questions.On SPI selection, I think the point from Section 10 of our paper is quite important, especially in the kinds of games that inspired the creation of surrogate goals in the first place. I agree that in some games, the SPI selection problem is no easier than the equilibrium selection problem in the base game. But there are games where it does fundamentally change things because *any* SPI that cannot further be Pareto-improved upon drastically increases your utility from one of the outcomes.
Re the “Bargaining in SPI” section: For one, the proposal in Section 9 of our paper can still be used to eliminate the zeroes!
Also, the “Bargaining in SPI” and “SPI Selection” sections to me don’t really seem like “objections”. They are limitations. (In a similar way as “the small pox vaccine doesn’t cure cancer” is useful info but not an objection to the small pox vaccine.)
What’s the reasoning behind mentioning the fairly controversial, often deemed dangerous Roko’s basilisk over less risky forms of acausal trade (like superrational cooperation with human-aligned branches)?