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Item type:Publication, Access status: without files , Suggestion-based assistance of suboptimal users in sequential decision-making tasksAI agents can assist humans by offering suggestions which users may accept or reject. This creates an asymmetric collaboration where the user retains full control while the AI lacks direct agency. We demonstrate that merely suggesting task-optimal actions can yield worse outcomes than unassisted performance; effective assistance requires understanding the user’s decision-making. To address this, we propose a zero-shot method based on Bayesian estimation of the user’s acceptance and fallback behavior, relying on a parametric model rather than prior data. We validate our approach theoretically and empirically on a novel toy environment, assessing its performance against baselines and stability in situations where parameters are incorrectly estimated.Publicationtype: Conference Paper22