对抗性不确定性下的鲁棒贝叶斯决策

中文摘要 AI 辅助

科学实验常旨在最大化信息增益，但在许多应用中主要目标是支持可靠的下游决策。现有决策感知实验设计和主动学习方法通常假设结果模型明确，且隐含依赖最优决策在现实扰动下的稳定性。然而实际上，实验结果常受隐藏或弱建模效应影响，可大幅改变决策最优性并导致误导性结论。我们研究序列对抗鲁棒决策感知实验设计，数据采集须考虑对抗合理最坏情况意外效应的信息增益。基于贝叶斯决策理论，我们在此设置下形式化了对抗鲁棒最优决策并推导了有原则的贝叶斯实验设计标准。该标准明确针对决策稳定性而非名义最优性。在合成和真实科学数据集上的实验表明，传统决策感知设计能快速收敛到高置信度但脆弱的决策，而我们的鲁棒性感知方法在对抗变化下产生的决策显著更稳定可靠。

英文摘要

Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing decision-aware experimental design and active learning methods typically assume well-specified outcome models and implicitly rely on the stability of the optimal decision under real-world perturbations. In practice, however, experimental outcomes are frequently influenced by hidden or weakly modeled effects, which can substantially alter decision optimality and lead to misleading conclusions. We study sequential adversarially robust decision-aware experimental design, where data acquisition has to take into account information gain against plausible worst-case unexpected effects, modeled here as variation in adversarial variables. Building on Bayesian decision theory, we formalize an adversarially robust optimal decision under this setting and derive a principled Bayesian experimental design criterion. The criterion explicitly targets decision stability rather than nominal optimality. Experiments on synthetic and real-world scientific datasets show that conventional decision-aware design can converge rapidly to high confidence yet fragile decisions, while our robustness-aware approach yields decisions that are significantly more stable and reliable under adversarial variation.

发表机构

• The University of Manchester（曼彻斯特大学）
• ELLIS Institute Finland（芬兰ELLIS研究所）
• Aalto University（阿尔托大学）

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