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.

翻译：当目标不一致的AI提供商相互竞争时，能否为多样化的用户群体产生对齐的结果？模型个性化在其中扮演何种角色？我们研究一个多竞争AI提供商与多用户交互的场景：用户需做出下游决策但偏好各异，提供商对用户行为持有自身目标，并策略性地部署AI模型以推进其目标。我们将该交互建模为具有多个领导者（提供商）与追随者（用户）的Stackelberg博弈：提供商承诺对话策略，用户则选择使用何种模型、如何进行对话以及如何行动。在用户特定个性化的设定下，我们证明在弱市场对齐条件下，每个均衡都能给予每位用户与完全对齐的公共模型相当的结果——因此即使提供商出于自利，个性化仍可促成多元化对齐的结果。相反，当提供商必须部署单一匿名策略时，在相同条件下存在具有非信息性行为的均衡。我们随后提出一个更强的对齐条件，以保证匿名设定下每位用户获得其最优效用。