AI Resource Allocation Under Uncertainty: Optimal Screening Strategies
A recent preprint on arXiv (2605.07979) explores the integration of machine learning-based algorithmic targeting with conventional screening methods to enhance resource distribution amid unavoidable aleatoric uncertainty. The research introduces a two-phase approach, where an initial screening phase assesses actual results for a selected group of units prior to the final allocation within a predetermined coverage budget. The ideal method involves screening units at the threshold of algorithmic allocation, striking a balance between cost and precision. The findings indicate that flawless risk predictions cannot eradicate misallocation caused by the intrinsic randomness of individual outcomes.
Key facts
- arXiv preprint 2605.07979
- Focus on aleatoric uncertainty in resource allocation
- Two-stage framework: screening then algorithmic allocation
- Optimal strategy screens marginal units
- Irreducible misallocation despite perfect predictions
- Machine learning for policy and humanitarian settings
- Fixed coverage budget constraint
- Compares algorithmic targeting to traditional screening
Entities
Institutions
- arXiv