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arXiv · 2605.26093

Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty

Jinwoo Go·Xiaoning Qian·Byung-Jun Yoon
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Abstract

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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Jinwoo Go, Xiaoning Qian, Byung-Jun Yoon. 2026-05-25. Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty. https://arxiv.org/abs/2605.26093

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