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New research explores dynamic welfare-maximizing pooled testing strategies

A new research paper explores dynamic welfare-maximizing pooled testing strategies under budget constraints. The study introduces a theoretical framework where agents have varying utilities and prior probabilities of being healthy, with welfare gained upon certification. The findings suggest that optimal dynamic policies can achieve at most twice the welfare of static allocations, and in some cases, adaptivity offers no benefit. The paper also details approximation algorithms and presents an exact study comparing dynamic and static benchmarks. AI

RANK_REASON The cluster contains a single arXiv paper detailing a new theoretical framework and algorithms for pooled testing. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.AI →

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New research explores dynamic welfare-maximizing pooled testing strategies

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The cluster contains a single arXiv paper detailing a new theoretical framework and algorithms for pooled testing. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Edwin Lock, Nicholas Lopez, Francisco Marmolejo-Coss\'io, Jose Roberto Tello Ayala, David C. Parkes ·

    Dynamic Welfare-Maximizing Pooled Testing

    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 probabi…