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商品和干预措施的分配。在这些领域，预测是达成目的的手段，它们为利益相关者提供对未来事件可能性的洞察，以改善决策质量并提升社会福利。然而，如果最大化福利是最终目标，预测仅是拼图中的一小部分。社会规划者可能采取多种其他政策杠杆来改善最终成果，例如扩大可用商品的获取范围，或增加干预措施的效果量。面对如此广泛的设计选择，一个基本的问题是：在算法决策中，预测的相对价值是什么？更好的预测带来的福利改善与其他政策杠杆相比如何？我们工作的目标是启动对这些问题的正式研究。我们的主要结果属于理论性质。我们在定量社会科学家常用的几个统计模型中，确定了决定预测相对价值（与扩大获取范围相比）的简单而尖锐的条件。此外，我们说明了这些理论见解如何用于指导实践中算法决策系统的设计。