Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter uncertainty does not necessarily improve downstream decisions, as only specific parameter directions relevant to the objective truly matter. We propose GoBOED, a goal-driven BOED framework that directly optimizes experimental designs for a specified decision-making objective. GoBOED combines an amortized variational posterior surrogate with a differentiable convex decision layer, enabling gradient-based design optimization that is fully decision-focused. We theoretically show that GoBOED gradients are insensitive to parameter directions irrelevant to the decision objective, providing a formal justification for why goal-driven design achieves equivalent decision quality over a wider set of experimental designs than information-gain maximization. Empirically, across source localization, epidemic management, and pharmacokinetic control, GoBOED identifies designs that better align with downstream decision objectives and reveals that near-optimal design windows are substantially wider than those predicted by goal-agnostic BOED approaches.

翻译：贝叶斯最优实验设计（BOED）通过选择实验来最大化关于模型参数的信息增益。然而，在决策关键场景中，降低参数不确定性不一定能改善下游决策，因为只有与目标相关的特定参数方向才真正重要。我们提出GoBOED，一个面向目标的BOED框架，它直接针对指定的决策目标优化实验设计。GoBOED将摊销变分后验替代模型与可微分的凸决策层相结合，实现了以决策为中心的梯度优化设计。我们在理论上证明，GoBOED的梯度对与决策目标无关的参数方向不敏感，这为面向目标的设计为何能在比信息增益最大化方法更广泛的实验设计集合上实现等效决策质量提供了形式化依据。在经验上，通过信源定位、流行病管理和药代动力学控制等任务，GoBOED识别出更符合下游决策目标的设计，并揭示了接近最优的设计窗口比面向目标无关的BOED方法所预测的窗口更宽。