LW - Individually incentivized safe Pareto improvements in open-source bargaining by Nicolas Macé

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Individually incentivized safe Pareto improvements in open-source bargaining, published by Nicolas Macé on July 18, 2024 on LessWrong.
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 necessarily a direct corollary of intelligence-relevant capabilities).
Introduction
Early AGIs might fail to make compatible demands with each other in high-stakes negotiations (we call this a "bargaining failure"). Bargaining failures can have catastrophic consequences, including great power conflicts, or AI triggering a flash war. More generally, a "bargaining problem" is when multiple agents need to determine how to divide value among themselves.
Early AGIs might possess insufficient bargaining skills because intelligence-relevant capabilities don't necessarily imply these skills: For instance, being skilled at avoiding bargaining failures might not be necessary for taking over. Another problem is that there might be no single rational way to act in a given multi-agent interaction. Even arbitrarily capable agents might have different priors, or different approaches to reasoning under bounded computation.
Therefore they might fail to solve equilibrium selection, i.e., make incompatible demands (see Stastny et al. (2021) and Conitzer & Oesterheld (2023)). What, then, are sufficient conditions for agents to avoid catastrophic bargaining failures?
Sufficiently advanced AIs might be able to verify each other's decision algorithms (e.g. via verifying source code), as studied in open-source game theory. This has both potential downsides and upsides for bargaining problems. On one hand, transparency of decision algorithms might make aggressive commitments more credible and thus more attractive (see Sec. 5.2 of Dafoe et al. (2020) for discussion).
On the other hand, agents might be able to mitigate bargaining failures by verifying cooperative commitments.
Oesterheld & Conitzer (2022)'s safe Pareto improvements[1] (SPI) leverages transparency to reduce the downsides of incompatible commitments.
In an SPI, agents conditionally commit to change how they play a game relative to some default such that everyone is (weakly) better off than the default with certainty.[2] For example, two parties A and B who would otherwise go to war over some territory might commit to, instead, accept the outcome of a lottery that allocates the territory to A with the probability that A would have won the war (assuming this probability is common knowledge). See also our extended example below.
Oesterheld & Conitzer (2022) has two important limitations: First, many different SPIs are in general possible, such that there is an "SPI selection problem", similar to the equilibrium selection problem in game theory (Sec. 6 of Oesterheld & Conitzer (2022)).
And if players don't coordinate on which SPI to implement, they might fail to avoid conflict.[3] Second, if expected utility-maximizing agents need to individually adopt strategies to implement an SPI, it's unclear what conditions...