Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare. However, if maximizing welfare is the ultimate goal, prediction is only a small piece of the puzzle. There are various other policy levers a social planner might pursue in order to improve bottom-line outcomes, such as expanding access to available goods, or increasing the effect sizes of interventions. Given this broad range of design decisions, a basic question to ask is: What is the relative value of prediction in algorithmic decision making? How do the improvements in welfare arising from better predictions compare to those of other policy levers? The goal of our work is to initiate the formal study of these questions. Our main results are theoretical in nature. We identify simple, sharp conditions determining the relative value of prediction vis-\`a-vis expanding access, within several statistical models that are popular amongst quantitative social scientists. Furthermore, we illustrate how these theoretical insights may be used to guide the design of algorithmic decision making systems in practice.

翻译：算法预测越来越多地被用于指导公共领域中的资源分配和干预措施。在这些领域中，预测是达成目的的手段。它们为利益相关者提供未来事件可能性的洞见，旨在提高决策质量并提升社会福利。然而，若将福利最大化作为最终目标，预测仅是整体解决方案中的一小部分。社会规划者可通过多种其他政策杠杆来改善最终结果，例如扩大可用资源的覆盖范围，或提升干预措施的效应规模。面对如此广泛的设计选择，一个根本性问题在于：预测在算法决策中的相对价值是什么？由更优预测带来的福利改进，与其他政策杠杆产生的效果相比如何？本研究旨在启动对这些问题的系统性探讨。我们的核心成果本质上是理论性的。我们在定量社会科学领域常用的若干统计模型中，确定了决定预测相对于扩大覆盖范围之相对价值的简洁而精确的条件。此外，我们阐述了如何运用这些理论洞见来指导实践中算法决策系统的设计。