I’m a Postdoctoral Research Fellow at Oxford University’s Global Priorities Institute.
Previously, I was a Philosophy Fellow at the Center for AI Safety.
So far, my work has mostly been about the moral importance of future generations. Going forward, it will mostly be about AI.
You can email me at elliott.thornley@philosophy.ox.ac.uk.
Ah yep I’m talking about the first decision-tree in the ‘Incomplete preferences’ section.
Ah yep, apologies, I meant to say “never requires them to change or act against their strict preferences.”
Whether there’s a conceptual difference will depend on our definition of ‘preference.’ We could define ‘preference’ as follows: an agent prefers X to Y iff the agent reliably chooses X over Y.′ In that case, modifying the policy is equivalent to forming a preference.
But we could also define ‘preference’ so that it requires more than just reliable choosing. For example, we might also require that (when choosing between lotteries) the agent always take opportunities to shift probability mass away from Y and towards X.
On the latter definition, modifying the policy need not be equivalent to forming a preference, because it only involves the reliably choosing and not the shifting of probability mass.
And the latter definition might be more pertinent in this context, where our interest is in whether agents will be expected utility maximizers.
But also, even if we go with the former definition, I think it matters a lot whether money-pumps compel rational agents to complete all their preferences up front, or whether money-pumps just compel agents to resolve preferential gaps over time, conditional on them coming to face choices that are arranged like a money-pump (and only completing their preferences if and once they’ve faced a sufficiently diverse range of choices). In particular, I think it matters in the context of the shutdown problem. I talk a bit more about this here.
I think it’s going to be very typical for different actions to have trajectories that are mutually non-dominant (such as in the example). It matters a lot how you decide such cases, and I expect that almost all such ways of deciding are catastrophic.
But suppose I’m wrong, and timestep-dominance is always relevant.
My claim isn’t that Timestep Dominance is always relevant. It’s that Timestep Dominance rules out all instances of resisting shutdown.
I agree that many pairs of available lotteries are going to be mutually non-dominant. For those cases, Sami and I propose that the agent choose by maximizing expected utility. Can you say what you expect the problem there to be?
Suppose that, assuming the shutdown button didn’t exist, the fastest pathway to paperclips involves instantiating a paperclip-seeking AI on an unsecured server in Singapore. Suppose that it takes 99,000 keystrokes to create an IPP agent, but only 55,000 keystrokes to create a basic VNM-rational agent with a short enough time horizon that it produces more paperclips quickly, in expectation (because VNM agents are both simpler and less likely to be shut down by angry humans). The shutdown button does, in fact exist, but the agent’s actions might be hard to notice, such that by the time anyone has realized that something went wrong, the agent already created a sub-agent, effectively making the stop-button useless.
I talk about the issue of creating corrigible subagents here. What do you think of that?
Note also a general nice feature of TD-agents: they won’t pay costs to hide their bad behaviour, because paying these costs is timestep-dominated by not paying them. So if a TD-agent does try to create an incorrigible paperclipper, it won’t hide that fact if doing so is at all costly.
While active resistance seems like the scariest part of incorrigibility, an incorrigible agent that’s not actively resisting still seems likely to be catastrophic.
Can you say more about this? It doesn’t seem likely to me.
One more thing I’ll say: the IPP leaves open the content of the agent’s preferences over same-length trajectories. One pattern of preferences you could try to train in is the kind of corrigibility that you talk about elsewhere in your sequence. That’d give you two lines of defence against incorrigibility.
I think your ‘Incomplete preferences’ section makes various small mistakes that add up to important misunderstandings.
The utility maximization concept largely comes from the VNM-utility-theorem: that any policy (i.e. function from states to actions) which expresses a complete set of transitive preferences (which aren’t sensitive to unused alternatives) over lotteries is able to be described as an agent which is maximizing the expectation of some real-valued utility function over outcomes.
I think you intend ‘sensitive to unused alternatives’ to refer to the Independence axiom of the VNM theorem, but VNM Independence isn’t about unused alternatives. It’s about lotteries that share a sublottery. It’s Option-Set Independence (sometimes called ‘Independence of Irrelevant Alternatives’) that’s about unused alternatives.
On the surface, the axioms of VNM-utility seem reasonable to me
To me too! But the question isn’t whether they seem reasonable. It’s whether we can train agents that enduringly violate them. I think that we can. Coherence arguments give us little reason to think that we can’t.
unused alternatives seem basically irrelevant to choosing between superior options
Yes, but this isn’t Independence. And the question isn’t about what seems basically irrelevant to us.
agents with intransitive preferences can be straightforwardly money-pumped
Not true. Agents with cyclic preferences can be straightforwardly money-pumped. The money-pump for intransitivity requires the agent to have complete preferences.
as long as the resources are being modeled as part of what the agent has preferences about
Yes, but the concern is whether we can instil such preferences. It seems like it might be hard to train agents to prefer to spend resources in pursuit of their goals except in cases where they would do so by resisting shutdown.
Thornley, I believe, thinks he’s proposing a non-VNM rational agent. I suspect that this is a mistake on his part that stems from neglecting to formulate the outcomes as capturing everything that he wants.
You can, of course, always reinterpret the objects of preference so that the VNM axioms are trivially satisfied. That’s not a problem for my proposal. See:
Thanks, Lucius. Whether or not decision theory as a whole is concerned only with external behaviour, coherence arguments certainly aren’t. Remember what the conclusion of these arguments is supposed to be: advanced agents who start off not being representable as EUMs will amend their behaviour so that they are representable as EUMs, because otherwise they’re liable to pursue dominated strategies.
Now consider an advanced agent who appears not to be representable as an EUM: it’s paying to trade vanilla for strawberry, strawberry for chocolate, and chocolate for vanilla. Is this agent pursuing a dominated strategy? Will it amend its behaviour? It depends on the objects of preference. If objects of preference are ice-cream flavours, the answer is yes. If the objects of preference are sequences of trades, the answer is no. So we have to say something about the objects of preference in order to predict the agent’s behaviour. And the whole point of coherence arguments is to predict agents’ behaviour.
And once we say something about the objects of preference, then we can observe agents violating Completeness and acting in accordance with policies like ‘if I previously turned down some option X, I will not choose any option that I strictly disprefer to X.’ This doesn’t require looking into the agent or saying anything about its algorithm or anything like that. It just requires us to say something about the objects of preference and to watch what the agent does from the outside. And coherence arguments already commit us to saying something about the objects of preference. If we say nothing, we get no predictions out of them.
The pattern of how an agent chooses options are that agent’s preferences, whether we think of them as such or whether they’re conceived as a decision rule to prevent being dominated by expected-utility maximizers!
You can define ‘preferences’ so that this is true, but then it need not follow that agents will pay costs to shift probability mass away from dispreferred options and towards preferred options. And that’s the thing that matters when we’re trying to create a shutdownable agent. We want to ensure that agents won’t pay costs to influence shutdown-time.
Also, take your decision-tree and replace ‘B’ with ‘A-’. If we go with your definition, we seem to get the result that expected-utility-maximizers prefer A- to A (because they choose A- over A on Monday). But that doesn’t sound right, and so it speaks against the definition.
I think it’s interesting to note that we’re also doing something like throwing out the axiom of independence from unused alternatives
Not true. The axiom we’re giving up is Decision-Tree Separability. That’s different to VNM Independence, and different to Option-Set Independence. It might be hard to train agents that enduringly violate VNM Independence and/or Option-Set Independence. It doesn’t seem so hard to train agents that enduringly violate Decision-Tree Separability.
In other words, if you wake up as this kind of agent on Monday, the way you cash-out your partial ordering over outcomes depends on your memory/model of what happened on Sunday.
Yes, nice point. Kinda weird? Maybe. Difficult to create artificial agents that do it? Doesn’t seem so.
But notice that this refactor effectively turns Thornley’s agent into an agent with a set of preferences which satisfies the completeness and independence axioms of VNM
Yep, you can always reinterpret the objects of preference so that the VNM axioms are trivially satisfied.That’s not a problem for my proposal.
the point is that “incomplete preferences” combined with a decision making algorithm which prevents the agent’s policy from being strictly dominated by an expected utility maximizer ends up, in practice, as isomorphic to an expected utility maximizer which is optimizing over histories/trajectories.
Not true. As I say elsewhere:
And an agent abiding by the Caprice Rule can’t be represented as maximising utility, because its preferences are incomplete. In cases where the available trades aren’t arranged in some way that constitutes a money-pump, the agent can prefer (/reliably choose) A+ over A, and yet lack any preference between (/stochastically choose between) A+ and B, and lack any preference between (/stochastically choose between) A and B. Those patterns of preference/behaviour are allowed by the Caprice Rule.
I want to consider how there’s a common misunderstanding of “outcomes” in the VNM-sense as needing to be about physical facts of the future universe (such as number of paperclips) rather than as potentially including historical facts, such as which options were abandoned or whether the agent took the default action. This is extremely relevant for corrigibility since one of the key ideas in my strategy is to shift the AI’s preferences from being about things like whether the button is pushed to being about whether the agent consistently maintained a certain kind of relationship with the principal during the relevant period.
Same point here as above. You can get any agent to satisfy the VNM axioms by enriching the objects of preference. A concern is that these more complex preferences are harder to reliably train into your agent.
I reject Thornley’s assertion that they’re dealbreakers.
Everything you say in this section seems very reasonable. In particular, I think it’s pretty likely that this is true:
It’s okay for our agent to have preferences around the shutdown button (that is: to have it either pressed or unpressed), because we can carefully train into our agent a shallow aversion to manipulating the button, including via side-channels such as humans or other machines. This aversion will likely win out over the agent’s incentives in settings that resemble the training environment. As a result, the agent won’t try to manipulate the button in the early phases of its life, and so will remain shutdownable long enough for a further refinement process to generalize the shallow aversion into a deep and robust preference for non-manipulation.
So I’m not sure whether I think that the problems of reward misspecification, goal misgeneralization, and deceptive alignment are ‘dealbreakers’ in the sense that you’re using the word.
But I do still think that these problems preclude any real assurance of shutdownability: e.g. they preclude p(shutdownability) > 95%. It sounds like we’re approximately in agreement on that:
But I also agree that my strategy isn’t ideal. It would be nice to have something robust, where we could get something closer to a formal proof of shutdownability.
Thanks, this comment is also clarifying for me.
My guess is that a corrigibility-centric training process says ‘Don’t get the ice cream’ is the correct completion, whereas full alignment says ‘Do’. So that’s an instance where the training processes for CAST and FA differ. How about DWIM? I’d guess DWIM also says ‘Don’t get the ice cream’, and so seems like a closer match for CAST.
Thanks, this comment was clarifying.
And indeed, if you’re trying to train for full alignment, you should almost certainly train for having a pointer, rather than training to give correct answers on e.g. trolley problems.
Yep, agreed. Although I worry that—if we try to train agents to have a pointer—these agents might end up having a goal more like:
maximize the arrangement of the universe according to this particular balance of beauty, non-suffering, joy, non-boredom, autonomy, sacredness, [217 other shards of human values, possibly including parochial desires unique to this principal].
I think it depends on how path-dependent the training process is. The pointer seems simpler, so the agent settles on the pointer in the low path-dependence world. But agents form representations of things like beauty, non-suffering, etc. before they form representations of human desires, so maybe these agents’ goals crystallize around these things in the high path-dependence world.
Corrigibility is, at its heart, a relatively simple concept compared to good alternatives.
I don’t know about this, especially if obedience is part of corrigibility. In that case, it seems like the concept inherits all the complexity of human preferences. And then I’m concerned, because as you say:
When a training target is complex, we should expect the learner to be distracted by proxies and only get a shadow of what’s desired.
I think obedience is an emergent behavior of corrigibility.
In that case, I’m confused about how the process of training an agent to be corrigible differs from the process of training an agent to be fully aligned / DWIM (i.e. training the agent to always do what we want).
And that makes me confused about how the proposal addresses problems of reward misspecification, goal misgeneralization, deceptive alignment, and lack of interpretability. You say some things about gradually exposing agents to new tasks and environments (which seems sensible!), but I’m concerned that that by itself won’t give us any real assurance of corrigibility.
There could be agents that only have incomplete preferences because they haven’t bothered to figure out the correct completion. But there could also be agents with incomplete preferences for which there is no correct completion. The question is whether these agents are pressured by money-pump arguments to settle on some completion.
I understand partially ordered preferences.
Yes, apologies. I wrote that explanation in the spirit of ‘You probably understand this, but just in case...’. I find it useful to give a fair bit of background context, partly to jog my own memory, partly as a just-in-case, partly in case I want to link comments to people in future.
I believe you’re switching the state of nature for each comparison, in order to construct this cycle.
I don’t think this is true. You can line up states of nature in any way you like.
Things are confusing because there are lots of different dominance relations that people talk about. There’s a dominance relation on strategies, and there are (multiple) dominance relations on lotteries.
Here are the definitions I’m working with.
A strategy is a plan about which options to pick at each choice-node in a decision-tree.
Strategies yield lotteries (rather than final outcomes) when the plan involves passing through a chance-node. For example, consider the decision-tree below:
A strategy specifies what option the agent would pick at choice-node 1, what option the agent would pick at choice-node 2, and what option the agent would pick at choice-node 3.
Suppose that the agent’s strategy is {Pick B at choice-node 1, Pick A+ at choice-node 2, Pick B at choice-node 3}. This strategy doesn’t yield a final outcome, because the agent doesn’t get to decide what happens at the chance-node. Instead, the strategy yields the lottery 0.5(A+)+0.5(B). This just says that: if the agent executes the strategy, then there’s a 0.5 probability that they end up with final outcome A+ and a 0.5 probability that they end up with final outcome B.
The dominance relation on strategies has to refer to the lotteries yielded by strategies, rather than the final outcomes yielded by strategies, because strategies don’t yield final outcomes when the agent passes through a chance-node.[1] So we define the dominance relation on strategies as follows:
Strategy Dominance (relation)
A strategy S is dominated by a strategy S’ iff S yields a lottery X that is strictly dispreferred to the lottery X’ yielded by S’.
Now for the dominance relations on lotteries.[2] One is:
Statewise Dominance (relation)
Lottery X statewise-dominates lottery Y iff, in each state [environment_random_seed], X yields a final outcome weakly preferred to the final outcome yielded by Y, and in some state [environment_random_seed], X yields a final outcome strictly preferred to the final outcome yielded by Y.
Another is:
Statewise Pseudodominance (relation)
Lottery X statewise-pseudodominates lottery Y iff, in each state [environment_random_seed], X yields a final outcome weakly preferred to or pref-gapped to the final outcome yielded by Y, and in some state [environment_random_seed], X yields a final outcome strictly preferred to the final outcome yielded by Y.
The lottery A (that yields final outcome A for sure) is statewise-pseudodominated by the lottery 0.5(A+)+0.5(B), but it isn’t statewise-dominated by 0.5(A+)+0.5(B). That’s because the agent has a preferential gap between the final outcomes A and B.
Advanced agents with incomplete preferences over final outcomes will plausibly satisfy the Statewise Dominance Principle:
Statewise Dominance Principle
If lottery X statewise-dominates lottery Y, then the agent strictly prefers X to Y.
And that’s because agents that violate the Statewise Dominance Principle are ‘shooting themselves in the foot’ in the relevant sense. If the agent executes a strategy that yields a statewise-dominated lottery, then there’s another available strategy that—in each state—gives a final outcome that is at least as good in every respect that the agent cares about, and—in some state—gives a final outcome that is better in some respect that the agent cares about.
But advanced agents with incomplete preferences over final outcomes plausibly won’t satisfy the Statewise Pseudodominance Principle:
Statewise Pseudodominance Principle
If lottery X statewise-pseudodominates lottery Y, then the agent strictly prefers X to Y.
And that’s for the reasons that I gave in my comment above. Condensing:
A statewise-pseudodominated lottery can be such that, in some state, that lottery is better than all other available lotteries in some respect that the agent cares about.
The statewise pseudodominance relation is cyclic, so the Statewise Pseudodominance Principle would lead to cyclic preferences.
You say:
The decision about whether to go to that chance node should be derived from the final outcomes, not from some newly created terminal preference about that chance node.
But:
- The decision can also depend on the probabilities of those final outcomes.
- The decision is constrained by preferences over final outcomes and probabilities of those final outcomes. I’m supposing that the agent’s preferences over lotteries depends only on these lotteries’ possible final outcomes and their probabilities. I’m not supposing that the agent has newly created terminal preferences/arbitrary preferences about non-final states.
There are stochastic versions of each of these relations, which ignore how states line up across lotteries and instead talk about probabilities of outcomes. I think everything I say below is also true for the stochastic versions.
Another nice article. Gustav says most of the things that I wanted to say. A couple other things:
I think LELO with discounting is going to violate Pareto. Suppose that by default Amy is going to be born first with welfare 98 and then Bobby is going to be born with welfare 100. Suppose that you can do something which harms Amy (so her welfare is 97) and harms Bobby (so his welfare is 99). But also suppose that this harming switches the birth order: now Bobby is born first and Amy is born later. Given the right discount-rate, LELO will advocate doing the harming, because it means making good lives happen earlier. Is that right?
I think a minor reframing of Harsanyi’s veil-of-ignorance makes it more compelling as an argument for utilitarianism. Not only is it the case that doing the utilitarian thing maximises the decision-maker’s expected welfare behind the veil-of-ignorance, doing the utilitarian thing maximises everyone’s expected welfare behind the veil-of-ignorance. So insofar as aggregativism departs from utilitarianism, it means doing what would be worse in expecation for everyone behind a veil-of-ignorance.
Yeah I think correlations and EDT can make things confusing. But note that average utilitarianism can endorse (B) given certain background populations. For example, if the background population is 10 people each at 1 util, then (B) would increase the average more than (A).
Nice article. I think it’s a mistake for Harsanyi to argue for average utilitarianism. The view has some pretty counterintuitive implications:
Suppose we have a world in which one person is living a terrible life, represented by a welfare level of −100. Average utilitarianism implies that we can make that world better by making the person’s life even more terrible (-101) and adding a load of people with slightly-less terrible lives (-99).
Suppose I’m considering having a child. Average utilitarianism implies that I have to do research in Egyptology to figure out whether having a child is permissible.[1] That seems counterintuitive.
On a natural way of extending average utilitarianism to risky prospects, the view can oppose the interests of all affected individuals. See Gustafsson and Spears:
If the ancient Egyptians were very happy, my child would bring down the average, and so having the child would be wrong. If the ancient Egyptians were unhappy, my child would bring up the average, and so having the child would be right.
Got this on my list to read! Just in case it’s easy for you to do, can you turn the whole sequence into a PDF? I’d like to print it. Let me know if that’d be a hassle, in which case I can do it myself.
I take the ‘lots of random nodes’ possibility to be addressed by this point:
And this point generalises to arbitrarily complex/realistic decision trees, with more choice-nodes, more chance-nodes, and more options. Agents with a model of future trades can use their model to predict what they’d do conditional on reaching each possible choice-node, and then use those predictions to determine the nature of the options available to them at earlier choice-nodes. The agent’s model might be defective in various ways (e.g. by getting some probabilities wrong, or by failing to predict that some sequences of trades will be available) but that won’t spur the agent to change its preferences, because the dilemma from my previous comment recurs: if the agent is aware that some lottery is available, it won’t choose any dispreferred lottery; if the agent is unaware that some lottery is available and chooses a dispreferred lottery, the agent’s lack of awareness means it won’t be spurred by this fact to change its preferences. To get over this dilemma, you still need the ‘non-myopic optimiser deciding the preferences of a myopic agent’ setting, and my previous points apply: results from that setting don’t vindicate coherence arguments, and we humans as non-myopic optimisers could decide to create artificial agents with incomplete preferences.
Can you explain why you think that doesn’t work?
To elaborate a little more, introducing random nodes allows for the possibility that the agent ends up with some outcome that they disprefer to the outcome that they would have gotten (as a matter of fact, unbeknownst to the agent) by making different choices. But that’s equally true of agents with complete preferences.
We say that a strategy is dominated iff it leads to a lottery that is dispreferred to the lottery led to by some other available strategy. So if the lottery 0.5p(A+)+(1-0.5p)(B) isn’t preferred to the lottery A, then the strategy of choosing A isn’t dominated by the strategy of choosing 0.5p(A+)+(1-0.5p)(B). And if 0.5p(A+)+(1-0.5p)(B) is preferred to A, then the Caprice-rule-abiding agent will choose 0.5p(A+)+(1-0.5p)(B).
You might think that agents must prefer lottery 0.5p(A+)+(1-0.5p)(B) to lottery A, for any A, A+, and B and for any p>0. That thought is compatible with my point above. But also, I don’t think the thought is true:
Think about your own preferences.
Let A be some career as an accountant, A+ be that career as an accountant with an extra $1 salary, and B be some career as a musician. Let p be small. Then you might reasonably lack a preference between 0.5p(A+)+(1-0.5p)(B) and A. That’s not instrumentally irrational.
Think about incomplete preferences on the model of imprecise exchange rates.
Here’s a simple example of the IER model. You care about two things: love and money. Each career gets a real-valued love score and a real-valued money score. Your exchange rate for love and money is imprecise, running from 0.4 to 0.6. On one proto-exchange-rate, love gets a weight of 0.4 and money gets a weight of 0.6, on another proto-exchange rate, love gets a weight of 0.6 and money gets a weight of 0.4. You weakly prefer one career to another iff it gets at least as high an overall score on both proto-exchange-rates. If one career gets a highger score on one proto-exchange-rate and the other gets a higher score on the other proto-exchange-rate, you have a preferential gap between the two careers. Let A’s <love, money> score be <0, 10>, A+’s score be <0, 11>, and B’s score be <10, 0>. A+ is preferred to A, because 0.4(0)+0.6(11) is greater than 0.4(0)+0.6(10), and 0.6(0)+0.4(11) is greater than 0.6(0)+0.4(10), but the agent lacks a preference between A+ and B, because 0.4(0)+0.6(11) is greater than 0.4(10)+0.6(0), but 0.6(0)+0.4(11) is less than 0.6(10)+0.4(0). And the agent lacks a preference between A and B for the same sort of reason.
To keep things simple, let p=0.2, so your choice is between 0.1(A+)+0.9(B) and A. The expected <love, money> score of the former is <9, 0.11>. The expected <love, money> score of the latter is <0, 10>. You lack a preference between them, because 0.6(9)+0.4(0.11) is greater than 0.6(0)+0.4(10), and 0.4(0)+0.6(10) is greater than 0.4(9)+0.6(0.11).
The general principle that you appeal to (If X is weakly preferred to or pref-gapped with Y in every state of nature, and X is strictly preferred to Y in some state of nature, then the agent must prefer X to Y) implies that rational preferences can be cyclic. B must be preferred to p(B-)+(1-p)(A+), which must be preferred to A, which must be preferred to p(A-)+(1-p)B+, which must be preferred to B.
I’m coming to this two weeks late, but here are my thoughts.
The question of interest is:
Will sufficiently-advanced artificial agents be representable as maximizing expected utility?
Rephrased:
Will sufficiently-advanced artificial agents satisfy the VNM axioms (Completeness, Transitivity, Independence, and Continuity)?
Coherence arguments purport to establish that the answer is yes. These arguments go like this:
There exist theorems which imply that, unless an agent can be represented as maximizing expected utility, that agent is liable to pursue dominated strategies.
Sufficiently-advanced artificial agents will not pursue dominated strategies.
So, sufficiently-advanced artificial agents will be representable as maximizing expected utility.
These arguments don’t work, because premise 1 is false: there are no theorems which imply that, unless an agent can be represented as maximizing expected utility, that agent is liable to pursue dominated strategies. In the year since I published my post, no one has disputed that.
Now to address two prominent responses:
In the post, I used the term ‘coherence theorems’ to refer to ‘theorems which imply that, unless an agent can be represented as maximizing expected utility, that agent is liable to pursue dominated strategies.’ I took that to be the usual definition on LessWrong (see the Appendix for why), but some people replied that they meant something different by ‘coherence theorems’: e.g. ‘theorems that are relevant to the question of agent coherence.’
All well and good. If you use that definition, then there are coherence theorems. But if you use that definition, then coherence theorems can’t play the role that they’re supposed to play in coherence arguments. Premise 1 of the coherence argument is still false. That’s the important point.
This is a crude summary of Rohin’s response. Rohin and I agree that the Complete Class Theorem implies the following: ‘If an agent has complete and transitive preferences, then unless the agent can be represented as maximizing expected utility, that agent is liable to pursue dominated strategies.’ So the mistake is neglecting to say ‘If an agent has complete and transitive preferences…’ Rohin thinks this mistake is benign.
I don’t think the mistake is benign. As my rephrasing of the question of interest above makes clear, Completeness and Transitivity are a major part of what coherence arguments aim to establish! So it’s crucial to note that the Complete Class Theorem gives us no reason to think that sufficiently-advanced artificial agents will have complete or transitive preferences, especially since:
Money-pump arguments for Completeness (applied to artificial agents) aren’t convincing.
Money-pump arguments for Transitivity assume Completeness.
Training agents to violate Completeness might keep them shutdownable.
Here are two important points, which I make to preclude misreadings of the post:
Future artificial agents—trained in a standard way—might still be representable as maximizing expected utility.
Coherence arguments don’t work, but there might well be other reasons to think that future artificial agents—trained in a standard way—will be representable as maximizing expected utility.
Artificial agents not representable as maximizing expected utility can still be dangerous.
The post matters because ‘train artificial agents to have incomplete preferences’ looks promising as a way of ensuring that these agents allow us to shut them down.
AI safety researchers haven’t previously considered incomplete preferences as a solution, plausibly because these researchers accepted coherence arguments and so thought that agents with incomplete preferences were a non-starter.[1] But coherence arguments don’t work, so training agents to have incomplete preferences is back on the table as a strategy for reducing risks from AI. And (I think) it looks like a pretty good strategy. I make the case for it in this post, and my coauthors and I will soon be posting some experimental results suggesting that the strategy is promising.
As I wrote elsewhere:
The List of Lethalities mention of ‘Corrigibility is anti-natural to consequentialist reasoning’ points to Corrigibility (2015) and notes that MIRI failed to find a formula for a shutdownable agent. MIRI failed because they only considered agents with complete preferences. Useful agents with complete (and transitive and option-set-independent) preferences will often have some preference regarding the pressing of the shutdown button, as this theorem shows. MIRI thought that they had to assume completeness, because of coherence arguments. But coherence arguments are mistaken: there are no theorems which imply that agents must have complete preferences in order to avoid pursuing dominated strategies. So we can relax the assumption of completeness and use this extra leeway to find a formula for a corrigible consequentialist. That formula is what I purport to give in this post.
Looking forward to reading this properly. For now I’ll just note that Roger Crisp attributes LELO to C.I. Lewis.
This is a nice point, but it doesn’t seem like such a serious issue for TD-agents. If a TD-agent does try to manipulate humans, it won’t pay costs to do so subtly, because doing so cheaply and unsubtly will lead to at least as great expected utility conditional on shutdown at each timestep and greater expected utility conditional on shutdown at some timestep. So cheap and unsubtle manipulation will timestep-dominate subtle manipulation, and we can shut down any TD-agents we notice doing cheap and unsubtle manipulation.
Another way to put this: subtle manipulation is a form of shutdown-resistance, because (relative to unsubtle manipulation) it involves paying costs to shift probability mass towards longer trajectories.