2026-07-22T21:03:49Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:13616 2024-03-06T10:52:27Z ddc:004 open_access

Buying Data over Time: Approximately Optimal Strategies for Dynamic Data-Driven Decisions Immorlica, Nicole Kash, Ian A. Lucier, Brendan Online Algorithms Value of Data Markov Processes We consider a model where an agent has a repeated decision to make and wishes to maximize their total payoff. Payoffs are influenced by an action taken by the agent, but also an unknown state of the world that evolves over time. Before choosing an action each round, the agent can purchase noisy samples about the state of the world. The agent has a budget to spend on these samples, and has flexibility in deciding how to spread that budget across rounds. We investigate the problem of choosing a sampling algorithm that optimizes total expected payoff. For example: is it better to buy samples steadily over time, or to buy samples in batches? We solve for the optimal policy, and show that it is a natural instantiation of the latter. Under a more general model that includes per-round fixed costs, we prove that a variation on this batching policy is a 2-approximation. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nicole Immorlica and Ian A. Kash and Brendan Lucier 2021 Is Part Of LIPIcs, Volume 185, 12th Innovations in Theoretical Computer Science Conference (ITCS 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ITCS.2021.77 urn:nbn:de:0030-drops-136161 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2021.77 eng https://creativecommons.org/licenses/by/3.0/legalcode