重新定义资源分配的元设计问题，让规划者同时优化资源配置与政策设计。
On the Meta-Design of Allocation Problems
- 将分配参数（如数据收集、服务能力）视为可优化变量，而非固定约束。
- 提出实操工具，帮助规划者在德国就业服务和埃塞俄比亚现金援助中提升整体福利。
- 适用于需同时优化策略与系统设计的公共政策制定者。
现有研究多聚焦于在固定设计参数下寻找最优分配策略，如收集哪些数据、服务多少人、服务质量等。但从规划者视角看，这些参数本身也是可优化变量，其选择对整体福利的影响不亚于制定最优靶向规则。这一认识催生了关于预测投资、容量约束与治疗质量等上游决策的元设计问题。本文有三项贡献：首先，正式定义资源分配的广义元设计空间；其次，开发可供实践者可靠使用的经验工具；最后，通过德国就业服务与埃塞俄比亚定向现金转移项目的两个真实案例验证该框架的有效性。
原文摘要 · Abstract (English)
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.
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