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[2602.08786] Empirically Understanding the Value of Prediction in Allocation
Summary
This paper explores the empirical value of prediction in resource allocation, comparing it to other investments like capacity expansion and treatment quality improvements.
Why It Matters
Understanding the effectiveness of predictive models in resource allocation is crucial for decision-makers in various sectors, including social services and healthcare. This research provides a framework to evaluate when and how to invest in predictive analytics versus other strategies, potentially leading to more efficient resource use and improved outcomes.
Key Takeaways
- The paper develops a toolkit for evaluating the value of predictions in resource allocation.
- It emphasizes the importance of context-specific analysis in decision-making.
- Case studies on employment services and poverty targeting illustrate practical applications.
- Investments in prediction should be compared against other policy levers like capacity expansion.
- The authors provide software and data to facilitate further research in this area.
Computer Science > Computers and Society arXiv:2602.08786 (cs) [Submitted on 9 Feb 2026 (v1), last revised 25 Feb 2026 (this version, v3)] Title:Empirically Understanding the Value of Prediction in Allocation Authors:Unai Fischer-Abaigar, Emily Aiken, Christoph Kern, Juan Carlos Perdomo View a PDF of the paper titled Empirically Understanding the Value of Prediction in Allocation, by Unai Fischer-Abaigar and Emily Aiken and Christoph Kern and Juan Carlos Perdomo View PDF HTML (experimental) Abstract:Institutions increasingly use prediction to allocate scarce resources. From a design perspective, better predictions compete with other investments, such as expanding capacity or improving treatment quality. Here, the big question is not how to solve a specific allocation problem, but rather which problem to solve. In this work, we develop an empirical toolkit to help planners form principled answers to this question and quantify the bottom-line welfare impact of investments in prediction versus other policy levers such as expanding capacity and improving treatment quality. Applying our framework in two real-world case studies on German employment services and poverty targeting in Ethiopia, we illustrate how decision-makers can reliably derive context-specific conclusions about the relative value of prediction in their allocation problem. We make our software toolkit, rvp, and parts of our data available in order to enable future empirical work in this area. Subjects: Computers...
