个性化有助于竞争中的多元对齐
Personalization Aids Pluralistic Alignment Under Competition
AI总结：
本文研究了竞争中AI提供者通过个性化策略实现用户多元对齐的可能性，并探讨了不同对齐条件下的均衡结果。
摘要：
在多个对齐不佳的AI提供者之间竞争，能否为多样化的用户群体带来对齐的结果？模型个性化扮演什么角色？我们研究了一个场景，其中多个竞争的AI提供者与多个用户互动，这些用户必须做出下游决策，但他们的偏好不同。提供者有自己的目标，通过战略部署AI模型来促进这些目标。我们将互动建模为一个具有多个领导者（提供者）和追随者（用户）的斯塔克尔伯格博弈：提供者承诺对话策略，用户选择使用哪个模型、如何对话以及如何行动。通过用户特定的个性化，我们证明在弱市场对齐条件下，每个均衡给每个用户的结果与完美对齐的共同模型的结果相当——因此个性化可以诱导多元对齐的结果，即使提供者是自利的。相反，当提供者必须部署一个匿名策略时，在相同条件下存在具有无信息行为的均衡。我们随后给出一个更强的对齐条件，该条件保证在匿名设置下每个用户都能获得最优效用。
英文摘要：
Can competition among misaligned AI providers yield aligned outcomes for a diverse population of users, and what role does model personalization play? We study a setting where multiple competing AI providers interact with multiple users who must make downstream decisions but differ in preferences. Providers have their own objectives over users' actions and strategically deploy AI models to advance them. We model the interaction as a Stackelberg game with multiple leaders (providers) and followers (users): providers commit to conversational policies, and users choose which model to use, how to converse, and how to act. With user-specific personalization, we show that under a Weak Market Alignment condition, every equilibrium gives each user outcomes comparable to those from a perfectly aligned common model -- so personalization can induce pluralistically aligned outcomes, even when providers are self-interested. In contrast, when providers must deploy a single anonymous policy, there exist equilibria with uninformative behavior under the same condition. We then give a stronger alignment condition that guarantees each user their optimal utility in the anonymous setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
