用预测识别最弱势群体，比扩大行政能力更有效。
The Value of Prediction in Identifying the Worst-Off
- 构建数学模型与德国失业案例，评估预测在识别最弱势者中的作用。
- 预测使援助精准聚焦最危困人群，显著提升公平性，优于单纯扩增行政资源。
- 为政策制定者提供可量化的决策工具，适合社会救助与公共福利系统设计者。
机器学习在政府项目中被越来越多地用于识别和援助最脆弱个体，优先帮助风险最高者而非优化整体结果。本文研究预测在以公平为导向的政策情境下的福利影响，并与扩展行政能力等其他政策工具进行对比。通过数学模型和对德国长期失业居民的真实案例研究，我们全面理解了预测在揭示最弱势群体中的相对有效性。研究结果提供了清晰的分析框架与数据驱动的实用工具，助力政策制定者在设计此类系统时做出有原则的决策。
原文摘要 · Abstract (English)
Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.
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