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

Problem
This paper addresses a significant gap in the literature on Bayesian optimal experimental design (BOED) by introducing a goal-driven approach that optimizes experimental designs specifically for decision-making objectives. Traditional BOED focuses on maximizing information gain about model parameters, which does not necessarily translate to improved decision quality in practical applications. The authors highlight that in decision-critical scenarios, only certain parameter directions are relevant, and existing methods do not account for this specificity. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose GoBOED, a novel framework that integrates an amortized variational posterior surrogate with a differentiable convex decision layer. This architecture allows for gradient-based optimization of experimental designs that are directly aligned with a specified decision-making objective. The key technical contribution is the formulation of gradients that are insensitive to parameter directions that do not impact the decision objective, thus enabling a more efficient exploration of the design space. The training process involves leveraging variational inference to approximate the posterior distribution of model parameters, while the decision layer ensures that the optimization process remains focused on relevant parameters. The computational requirements for training are not explicitly disclosed, but the framework is designed to be efficient in terms of gradient computation.

Results
GoBOED was empirically validated across three distinct applications: source localization, epidemic management, and pharmacokinetic control. The results demonstrate that GoBOED consistently identifies experimental designs that yield superior decision outcomes compared to traditional goal-agnostic BOED methods. Specifically, the authors report that the near-optimal design windows identified by GoBOED are significantly broader than those predicted by conventional approaches, indicating a more robust alignment with decision objectives. While exact numerical results and effect sizes are not provided in the abstract, the implications suggest substantial improvements in decision quality across the tested domains.

Limitations
The authors acknowledge that the GoBOED framework may be sensitive to the choice of the surrogate model and the specific decision layer employed, which could affect the generalizability of the results. Additionally, the empirical validation is limited to three specific applications, and further exploration across a broader range of decision-making scenarios is necessary to fully assess the framework’s robustness. The paper does not address potential computational overhead associated with the amortized variational inference process, which could limit scalability in high-dimensional settings.

Why it matters
The introduction of GoBOED has significant implications for experimental design in decision-critical applications, as it shifts the focus from merely maximizing information gain to optimizing for actual decision outcomes. This approach could enhance the efficacy of experimental designs in fields such as healthcare, environmental science, and engineering, where decisions based on uncertain models are paramount. By demonstrating that goal-driven designs can achieve equivalent or superior decision quality over a wider range of experimental configurations, this work paves the way for more effective and practical applications of Bayesian methods in real-world scenarios.

Authors: Jinwoo Go, Xiaoning Qian, Byung-Jun Yoon
Source: arXiv:2605.26093
URL: https://arxiv.org/abs/2605.26093v1