There is an extensive literature that studies how to find optimal policies in resource allocation problems, taking the underlying design parameters that define the allocation, such as what data is collected, how many people can be served, and quality of service as fixed constraints. Yet, from a planner's perspective, these design parameters are themselves optimization variables that are just as important in determining overall welfare as selecting the optimal targeting rule for a given set of constraints. This realization motivates a rich set of meta-design questions exploring how planners should make principled decisions about investments in prediction, capacity constraints, and treatment quality, all of which lie upstream of classical policy optimization. Building on initial theoretical work in this space, our paper has three main contributions. First, we formally define the broad meta-design space of resource allocation problems. Second, we develop empirical tools that enable practitioners to reliably navigate it. Third, we demonstrate the framework in two real-world case studies on German employment services and targeted cash transfer programs in Ethiopia.

翻译：在资源分配问题中，如何寻找最优策略已有大量文献研究，这些研究将定义分配的底层设计参数（如收集哪些数据、可服务多少人、服务质量）视为固定约束。然而，从规划者的视角来看，这些设计参数本身即为优化变量，其对于决定整体福利的重要性，与在给定约束条件下选择最优目标规则不相上下。这一认识激发了一系列丰富的元设计问题，探讨规划者应如何在预测投入、容量约束及处理质量方面做出原则性决策——这些问题均位于经典政策优化的上游。基于该领域的初步理论工作，本文主要有三项贡献。首先，我们正式定义了资源分配问题的广义元设计空间。其次，我们开发了使实践者能够可靠驾驭该空间的实证工具。第三，我们通过德国就业服务与埃塞俄比亚定向现金转移项目这两个现实案例研究，展示了该框架的应用。