Dynamic Welfare-Maximizing Pooled Testing
Quick summary
arXiv:2601.22419v2 Announce Type: replace-cross Abstract: Pooled testing uses one test to certify several agents as healthy when the pooled result is negative. We study a budget-constrained welfare problem in which agents have heterogeneous utilities and independent prior probabilities of being healthy. Welfare is earned when an agent is certified healthy, and a dynamic policy may choose each pool after observing earlier test outcomes. We ask how much such adaptation can improve over a static allocation that fixes all pools in advance. Our main result proves that the optimal dynamic policy has
Key takeaways
- arXiv:2601.22419v2 Announce Type: replace-cross Abstract: Pooled testing uses one test to certify several agents as healthy when the pooled result is negative.
- We study a budget-constrained welfare problem in which agents have heterogeneous utilities and independent prior probabilities of being healthy.
- Welfare is earned when an agent is certified healthy, and a dynamic policy may choose each pool after observing earlier test outcomes.
Why it matters
“Dynamic Welfare-Maximizing Pooled Testing” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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