License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2021.77
URN: urn:nbn:de:0030-drops-136161
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/13616/
Go to the corresponding LIPIcs Volume Portal

Immorlica, Nicole ; Kash, Ian A. ; Lucier, Brendan

Buying Data over Time: Approximately Optimal Strategies for Dynamic Data-Driven Decisions

pdf-format:
LIPIcs-ITCS-2021-77.pdf (0.5 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{immorlica_et_al:LIPIcs.ITCS.2021.77,
  author =	{Nicole Immorlica and Ian A. Kash and Brendan Lucier},
  title =	{{Buying Data over Time: Approximately Optimal Strategies for Dynamic Data-Driven Decisions}},
  booktitle =	{12th Innovations in Theoretical Computer Science Conference (ITCS 2021)},
  pages =	{77:1--77:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-177-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{185},
  editor =	{James R. Lee},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/13616},
  URN =		{urn:nbn:de:0030-drops-136161},
  doi =		{10.4230/LIPIcs.ITCS.2021.77},
  annote =	{Keywords: Online Algorithms, Value of Data, Markov Processes}
}

Keywords: Online Algorithms, Value of Data, Markov Processes
Collection: 12th Innovations in Theoretical Computer Science Conference (ITCS 2021)
Issue Date: 2021
Date of publication: 04.02.2021

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI