Cooperation, Conflict, and Transformative Artificial Intelligence: A Research Agenda

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Preface to CLR's Research Agenda on Cooperation, Conflict, and TAI

We are now using a new definition of s-risks. I've edited this post to reflect the change.

New definition:

S-risks are risks of events that bring about suffering in cosmically significant amounts. By “significant”, we mean significant relative to expected future suffering.

Note that it may turn out that the amount of suffering that we can influence is dwarfed by suffering that we can’t influence. By “expectation of suffering in the future” we mean “expectation of action-relevant suffering in the future”.

What Multipolar Failure Looks Like, and Robust Agent-Agnostic Processes (RAAPs)

Ok, thanks for that. I’d guess then that I’m more uncertain than you about whether human leadership would delegate to systems who would fail to accurately forecast catastrophe.

It’s possible that human leadership just reasons poorly about whether their systems are competent in this domain. For instance, they may observe that their systems perform well in lots of other domains, and incorrectly reason that “well, these systems are better than us in many domains, so they must be better in this one, too”. Eagerness to deploy before a more thorough investigation of the systems’ domain-specific abilities may be exacerbated by competitive pressures. And of course there is historical precedent for delegation to overconfident military bureaucracies.

On the other hand, to the extent that human leadership is able to correctly assess their systems’ competence in this domain, it may be only because there has been a sufficiently successful AI cooperation research program. For instance, maybe this research program has furnished appropriate simulation environments to probe the relevant aspects of the systems’ behavior, transparency tools for investigating cognition about other AI systems, norms for the resolution of conflicting interests and methods for robustly instilling those norms, etc, along with enough researcher-hours applying these tools to have an accurate sense of how well the systems will navigate conflict.

As for irreversible delegation — there is the question of whether delegation is in principle reversible, and the question of whether human leaders would want to override their AI delegates once war is underway. Even if delegation is reversible, human leaders may think that their delegates are better suited to wage war on their behalf once it has started. Perhaps because things are simply happening so fast for them to have confidence that they could intervene without placing themselves at a decisive disadvantage.

What Multipolar Failure Looks Like, and Robust Agent-Agnostic Processes (RAAPs)

The US and China might well wreck the world by knowingly taking gargantuan risks even if both had aligned AI advisors, although I think they likely wouldn't.

But what I'm saying is really hard to do is to make the scenarios in the OP (with competition among individual corporate boards and the like) occur without extreme failure of 1-to-1 alignment

I'm not sure I understand yet. For example, here’s a version of Flash War that happens seemingly without either the principals knowingly taking gargantuan risks or extreme intent-alignment failure.

  1. The principals largely delegate to AI systems on military decision-making, mistakenly believing that the systems are extremely competent in this domain.

  2. The mostly-intent-aligned AI systems, who are actually not extremely competent in this domain, make hair-trigger commitments of the kind described in the OP. The systems make their principals aware of these commitments and (being mostly-intent-aligned) convince their principals “in good faith” that this is the best strategy to pursue. In particular they are convinced that this will not lead to existential catastrophe.

  3. The commitments are triggered as described in the OP, leading to conflict. The conflict proceeds too quickly for the principals to effectively intervene / the principals think their best bet at this point is to continue to delegate to the AIs.

  4. At every step both principals and AIs think they’re doing what’s best by the respective principals’ lights. Nevertheless, due to a combination of incompetence at bargaining and structural factors (e.g., persistent uncertainty about the other side’s resolve), the AIs continue to fight to the point of extinction or unrecoverable collapse.

Would be curious to know which parts of this story you find most implausible.

The Commitment Races problem

Yeah I agree the details aren’t clear. Hopefully your conditional commitment can be made flexible enough that it leaves you open to being convinced by agents who have good reasons for refusing to do this world-model agreement thing. It’s certainly not clear to me how one could do this. If you had some trusted “deliberation module”, which engages in open-ended generation and scrutiny of arguments, then maybe you could make a commitment of the form “use this protocol, unless my counterpart provides reasons which cause my deliberation module to be convinced otherwise”. Idk.

Your meta-level concern seems warranted. One would at least want to try to formalize the kinds of commitments we’re discussing and ask if they provide any guarantees, modulo equilibrium selection.

The Commitment Races problem

It seems like we can kind of separate the problem of equilibrium selection from the problem of “thinking more”, if “thinking more” just means refining one’s world models and credences over them. One can make conditional commitments of the form: “When I encounter future bargaining partners, we will (based on our models at that time) agree on a world-model according to some protocol and apply some solution concept (e.g. Nash or Kalai-Smorodinsky) to it in order to arrive at an agreement.”

The set of solution concepts you commit to regarding as acceptable still poses an equilibrium selection problem. But, on the face of it at least, the “thinking more” part is handled by conditional commitments to act on the basis of future beliefs.

I guess there’s the problem of what protocols for specifying future world-models you commit to regarding as acceptable. Maybe there are additional protocols that haven’t occurred to you, but which other agents may have committed to and which you would regard as acceptable when presented to you. Hopefully it is possible to specify sufficiently flexible methods for determining whether protocols proposed by your future counterparts are acceptable that this is not a problem.

Eight claims about multi-agent AGI safety

Nice post! I’m excited to see more attention being paid to multi-agent stuff recently.

A few miscellaneous points:

  • I get the impression that the added complexity of multi- relative to single-agent systems has not been adequately factored into folks’ thinking about timelines / the difficulty of making AGI that is competent in a multipolar world. But I’m not confident in that.

  • I think it’s possible that conflict / bargaining failure is a considerable source of existential risk, in addition to suffering risk. I don’t really have a view on how it compares to other sources, but I’d guess that it is somewhat underestimated, because of my impression that folks generally underestimate the difficulty of getting agents to get along (even if they are otherwise highly competent).

Homogeneity vs. heterogeneity in AI takeoff scenarios

Neat post, I think this is an important distinction. It seems right that more homogeneity means less risk of bargaining failure, though I’m not sure yet how much.

Cooperation and coordination between different AIs is likely to be very easy as they are likely to be very structurally similar to each other if not share basically all of the same weights

In what ways does having similar architectures or weights help with cooperation between agents with different goals? A few things that come to mind:

  • Having similar architectures might make it easier for agents to verify things about one another, which may reduce problems of private information and inability to credibly commit to negotiated agreements. But of course increased credibility is a double-edged sword as far as catastrophic bargaining failure is concerned, as it may make agents more likely to commit to carrying out coercive threats.
  • Agents with more similar architectures / weights will tend to have more similar priors / ways of modeling their counterparts and as well as notions of fairness in bargaining, which reduces risk of bargaining failure . But as systems are modified or used to produce successor systems, they may be independently tuned to do things like represent their principal in bargaining situations. This tuning may introduce important divergenes in whatever default priors or notions of fairness were present in the initial mostly-identical systems. I don’t have much intuition for how large these divergences would be relative to those in a regime that started out more heterogeneous.
  • If a technique for reducing bargaining failure only works if all of the bargainers use it (e.g., surrogate goals), then homogeneity could make it much more likely that all bargainers used the technique. On the other hand, it may be that such techniques would not be introduced until after the initial mostly-identical systems were modified / successor systems produced, in which case there might still need to be coordination on common adoption of the technique.

Also, the correlated success / failure point seems to apply to bargaining as well as alignment. For instance, multiple mesa-optimizers may be more likely under homogeneity, and if these have different mesa-objectives (perhaps due to being tuned by principals with different goals) then catastrophic bargaining failure may be more likely.

In a multipolar scenario, how do people expect systems to be trained to interact with systems developed by other labs?

Makes sense. Though you could have deliberate coordinated training even after deployment. For instance, I'm particularly interested in the question of "how will agents learn to interact in high stakes circumstances which they will rarely encounter?" One could imagine the overseers of AI systems coordinating to fine-tune their systems in simulations of such encounters even after deployment. Not sure how plausible that is though.

Against strong bayesianism

I don't think bayesianism gives you particular insight into that for the same reasons I don't think it gives you particular insight into human cognition

In the areas I focus on, at least, I wouldn’t know where to start if I couldn’t model agents using Bayesian tools. Game-theoretic concepts like social dilemma, equilibrium selection, costly signaling, and so on seem indispensable, and you can’t state these crisply without a formal model of preferences and beliefs. You might disagree that these are useful concepts, but at this point I feel like the argument has to take place at the level of individual applications of Bayesian modeling, rather than a wholesale judgement about Bayesianism.

misleading concepts like "boundedly rational" (compare your claim with the claim that a model in which all animals are infinitely large helps us identify properties that are common to "boundedly sized" animals)

I’m not saying that the idealized model helps us identify properties common to more realistic agents just because it's idealized. I agree that many idealized models may be useless for their intended purpose. I’m saying that, as it happens, whenever I think of various agentlike systems it strikes me as useful to model those systems in a Bayesian way when reasoning about some of their aspects --- even though the details of their architectures may differ a lot.

I didn’t quite understand why you said “boundedly rational” is a misleading concept, I’d be interested to see you elaborate.

if we have no good reason to think that explicit utility functions are something that is feasible in practical AGI

I’m not saying that we should try to design agents who are literally doing expected utility calculations over some giant space of models all the time. My suggestion was that it might be good --- for the purpose of attempting to guarantee safe behavior --- to design agents which in limited circumstances make decisions by explicitly distilling their preferences and beliefs into utilities and probabilities. It's not obvious to me that this is intractable. Anyway, I don't think this point is central to the disagreement.

Against strong bayesianism

I agree with the rejection of strong Bayesianism. I don’t think it follows from what you’ve written, though, that “bayesianism is not very useful as a conceptual framework for thinking either about AGI or human reasoning”.

I'm probably just echoing things that have been said many times before, but:

You seem to set up a dichotomy between two uses of Bayesianism: modeling agents as doing something like "approximate Solomonoff induction", and Bayesianism as just another tool in our statistical toolkit. But there is a third use of Bayesianism, the way that sophisticated economists and political scientists use it: as a useful fiction for modeling agents who try to make good decisions in light of their beliefs and preferences. I’d guess that this is useful for AI, too. These will be really complicated systems and we don’t know much about their details yet, but it will plausibly be reasonable to model them as “trying to make good decisions in light of their beliefs and preferences”. In turn, the Bayesian framework plausibly allows us to see failure modes that are common to many boundedly rational agents.

Perhaps a fourth use is that we might actively want to try to make our systems more like Bayesian reasoners, at least in some cases. For instance, I mostly think about failure modes in multi-agent systems. I want AIs to compromise with each other instead of fighting. I’d feel much more optimistic about this if the AIs could say “these are our preferences encoded as utility functions, these are our beliefs encoded as priors, so here is the optimal bargain for us given some formal notion of fairness” --- rather than hoping that compromise is a robust emergent property of their training.

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