让实验设计直接服务于决策目标，提升关键场景下的可靠性。
Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty
- 基于决策目标优化实验设计，而非单纯追求参数不确定性降低
- 在源定位、疫情管理等任务中显著提升决策质量
- 适用于需要稳健决策的高风险场景，如医疗或公共安全
贝叶斯最优实验设计（BOED）旨在通过选择实验以最大化模型参数的信息增益。但在决策关键场景中，减少参数不确定性并不一定改善下游决策，因为只有与目标相关的特定参数方向才真正重要。本文提出一种目标驱动的贝叶斯最优实验设计框架——GoBOED，其直接优化实验设计以达成指定决策目标。GoBOED结合了近似变分后验代理模型与可微分凸决策层，实现基于梯度的、完全聚焦决策的实验设计优化。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，为目标驱动设计在更广泛的设计空间中保持同等决策质量提供了形式化依据。实证结果表明，在源定位、疫情管理及药代动力学控制任务中，GoBOED识别出更符合下游决策目标的设计，并揭示近优设计窗口远宽于传统无目标导向的BOED方法预测范围。
原文摘要 · Abstract (English)
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.
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