Author: Jesse Clifton

Individually incentivized safe Pareto improvements in open-source bargaining

Summary Agents might fail to peacefully trade in high-stakes negotiations. Such bargaining failures can have catastrophic consequences, including great power conflicts, and AI flash wars. This post is a distillation of DiGiovanni et al. (2024) (DCM), whose central result is that agents that are sufficiently transparent to each other have individual incentives to avoid catastrophic bargaining failures. More precisely, DCM constructs strategies that are plausibly individually incentivized, and, if adopted by all, guarantee each player no less than their least preferred trade outcome. Figure 0 below illustrates this. This result is significant because artificial general intelligences (AGIs) might (i) be involved in high-stakes negotiations, (ii) be designed with the capabilities required for the type of strategy we’ll present, and (iii) bargain poorly by default (since bargaining competence isn’t […]

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Making AIs less likely to be spiteful

This report is also posted to LessWrong here. Which forms of misalignment might result in particularly bad outcomes? And to what extent can we prevent them even if we fail at ​​intent alignment? We define spite as a terminal preference for frustrating others’ preferences, at least under some conditions. Reducing the chances that an AI system is spiteful is a candidate class of interventions for reducing risks of AGI conflict, as well as risks from malevolence. This post summarizes some of our thinking on the topic. We give an overview of why spite might lead to catastrophic conflict; how we might intervene to reduce it; ways in which the intervention could fail to be impactful, or have negative impact; and things we could learn […]

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Open-minded updatelessness

Summary Bounded agents might be unaware of possibilities relevant to their decision-making. That is, they may not just be uncertain but fail to conceive of some relevant hypotheses entirely. What's more, commitment races might pressure early AGIs into adopting an updateless policy from a position of limited awareness. What happens then when a committed AGI becomes aware of a possibility that’d have changed which commitment it’d have wanted to make in the first place? Motivated by this question, we develop "open-minded" extensions of updatelessness, where agents revise their priors upon experiencing awareness growth and reevaluate their commitment to a plan relative to the revised prior. Introduction Bounded agents may be unaware of propositions relevant to the decision problem they face.1 […]

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When is intent alignment sufficient or necessary to reduce AGI conflict?

In this post, we look at conditions under which Intent Alignment isn't Sufficient or Intent Alignment isn't Necessary for interventions on AGI systems to reduce the risks of (unendorsed) conflict to be effective. We then conclude this sequence by listing what we currently think are relatively promising directions for technical research and intervention to reduce AGI conflict. Intent alignment is not sufficient to prevent unendorsed conflict In the previous post, we outlined possible causes of conflict and directions for intervening on those causes. Many of the causes of conflict seem like they would be addressed by successful AI alignment. For example: if AIs acquire conflict-prone preferences from their training data when we didn’t want them to, that is a clear case of misalignment. […]

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When would AGIs engage in conflict?

Here we will look at two of the claims introduced in the previous post: AGIs might not avoid conflict that is costly by their lights (Capabilities aren’t Sufficient) and conflict that is costly by our lights might not be costly by the AGIs’ (Conflict isn’t Costly).  Explaining costly conflict First we’ll focus on conflict that is costly by the AGIs’ lights. We’ll define “costly conflict” as (ex post) inefficiency: There is an outcome that all of the agents involved in the interaction prefer to the one that obtains. This raises the inefficiency puzzle of war: Why would intelligent, rational actors behave in a way that leaves them all worse off than they could be?  We’ll operationalize “rational and intelligent” actors […]

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When does technical work to reduce AGI conflict make a difference?: Introduction

This is a pared-down version of a longer draft report. We went with a more concise version to get it out faster, so it ended up being more of an overview of definitions and concepts, and is thin on concrete examples and details. Hopefully subsequent work will help fill those gaps. Sequence Summary Some researchers are focused on reducing the risks of conflict between AGIs. In this sequence, we’ll present several necessary conditions for technical work on AGI conflict reduction to be effective, and survey circumstances under which these conditions hold. We’ll also present some tentative thoughts on promising directions for research and intervention to prevent AGI conflict. This post We give a breakdown of necessary conditions for technical work […]

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Collaborative game specification: arriving at common models in bargaining

Conflict is often an inefficient outcome to a bargaining problem. This is true in the sense that, for a given game-theoretic model of a strategic interaction, there is often some equilibrium in which all agents are better off than the conflict outcome. But real-world agents may not make decisions according to game-theoretic models, and when they do, they may use different models. This makes it more difficult to guarantee that real-world agents will avoid bargaining failure than is suggested by the observation that conflict is often inefficient.   In another post, I described the "prior selection problem", on which different agents having different models of their situation can lead to bargaining failure. Moreover, techniques for addressing bargaining problems like coordination on […]

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Weak identifiability and its consequences in strategic settings

One way that agents might become involved in catastrophic conflict is if they have mistaken beliefs about one another. Maybe I think you are bluffing when you threaten to launch the nukes, but you are dead serious. So we should understand why agents might sometimes have such mistaken beliefs. In this post I'll discuss one obstacle to the formation of accurate beliefs about other agents, which has to do with identifiability. As with my post on equilibrium and prior selection problems, this is a theme that keeps cropping up in my thinking about AI cooperation and conflict, so I thought it might be helpful to have it written up. We say that a model is unidentifiable if there are several […]

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