Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system parameters as in traditional Bayesian experimental design (BED), but also plays a key part in facilitating downstream decision-making. Most recent BED methods use an amortized policy network to rapidly design experiments. However, the information gathered through these methods is suboptimal for down-the-line decision-making, as the experiments are not inherently designed with downstream objectives in mind. In this paper, we present an amortized decision-aware BED framework that prioritizes maximizing downstream decision utility. We introduce a novel architecture, the Transformer Neural Decision Process (TNDP), capable of instantly proposing the next experimental design, whilst inferring the downstream decision, thus effectively amortizing both tasks within a unified workflow. We demonstrate the performance of our method across several tasks, showing that it can deliver informative designs and facilitate accurate decision-making.

翻译：许多关键决策，例如个性化医疗诊断和产品定价，都是基于通过设计、观察和分析一系列实验所获得的洞见而制定的。这凸显了实验设计的关键作用，它不仅超越了传统贝叶斯实验设计（BED）中仅收集系统参数信息的范畴，而且在促进下游决策制定方面发挥着核心作用。大多数近期的BED方法采用摊销式策略网络来快速设计实验。然而，通过这些方法收集的信息对于后续的决策制定而言是次优的，因为这些实验在设计之初并未将下游目标纳入考量。本文提出了一种摊销式决策感知BED框架，该框架优先考虑最大化下游决策效用。我们引入了一种新颖的架构——Transformer神经决策过程（TNDP），该架构能够即时提出下一个实验设计，同时推断下游决策，从而在一个统一的工作流中有效地摊销这两项任务。我们在多个任务上展示了我们方法的性能，结果表明它能够提供信息丰富的设计并促进准确的决策制定。