Delegated online search
Braun, Pirmin
Hahn, Niklas
Hoefer, Martin
Schecker, Conrad
ddc:000
In a delegation problem, a principal P with commitment power tries to pick one out of 𝑛 options.
Each option is drawn independently from a known distribution. Instead of inspecting the options
herself, P delegates the information acquisition to a rational and self-interested agent A. After
inspection, A proposes one of the options, and P can accept or reject.
Delegation is a classic setting in economic information design with many prominent applications,
but the computational problems are only poorly understood. In this paper, we study a natural
online variant of delegation, in which the agent searches through the options in an online fashion.
For each option, he has to irrevocably decide if he wants to propose the current option or discard
it, before seeing information on the next option(s). How can we design algorithms for P that
approximate the utility of her best option in hindsight?
We show that in general P can obtain a Θ(1∕𝑛)-approximation and extend this result to ratios
of Θ(𝑘∕𝑛) in case (1) A has a lookahead of 𝑘 rounds, or (2) A can propose up to 𝑘 different
options. We provide fine-grained bounds independent of 𝑛 based on three parameters. If the ratio
of maximum and minimum utility for A is bounded by a factor 𝛼, we obtain an Ω(loglog 𝛼∕ log 𝛼)-
approximation algorithm, and we show that this is best possible. Additionally, if P cannot
distinguish options with the same value for herself, we show that ratios polynomial in 1∕𝛼 cannot
be avoided. If there are at most 𝛽 different utility values for A, we show a Θ(1∕𝛽)-approximation.
If the utilities of P and A for each option are related by a factor 𝛾, we obtain an Ω(1∕ log 𝛾)-
approximation, where 𝑂(log log 𝛾∕ log 𝛾) is best possible.
Elsevier
2024
info:eu-repo/semantics/article
doc-type:article
text
http://purl.org/coar/resource_type/c_6501
https://research-explorer.ista.ac.at/record/17188
Braun P, Hahn N, Hoefer M, Schecker C. Delegated online search. <i>Artificial Intelligence</i>. 2024;334. doi:<a href="https://doi.org/10.1016/j.artint.2024.104171">10.1016/j.artint.2024.104171</a>
eng
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.artint.2024.104171
info:eu-repo/semantics/altIdentifier/issn/0004-3702
info:eu-repo/semantics/altIdentifier/arxiv/2203.01084
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess