Approximately Revenue-Maximizing Auctions for Deliberative Agents
Abstract
In many real-world auctions, a bidder does not know her exact value for an item, but can perform a costly deliberation to reduce her uncertainty. Relatively little is known about such deliberative environments, which are fundamentally different from classical auction environments. In this paper, we propose a new approach that allows us to leverage classical revenue-maximization results in deliberative environments. In particular, we use Myerson (1981) to construct the first non-trivial (i.e., dependent on deliberation costs) upper bound on revenue in deliberative auctions. This bound allows us to apply existing results in the classical environment to a deliberative environment. In addition, we show that in many deliberative environments the only optimal dominant-strategy mechanisms take the form of sequential posted-price auctions.
Cite
Text
Celis et al. "Approximately Revenue-Maximizing Auctions for Deliberative Agents." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8270Markdown
[Celis et al. "Approximately Revenue-Maximizing Auctions for Deliberative Agents." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/celis2012aaai-approximately/) doi:10.1609/AAAI.V26I1.8270BibTeX
@inproceedings{celis2012aaai-approximately,
title = {{Approximately Revenue-Maximizing Auctions for Deliberative Agents}},
author = {Celis, L. Elisa and Karlin, Anna R. and Leyton-Brown, Kevin and Nguyen, C. Thach and Thompson, David R. M.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2012},
pages = {1313-1318},
doi = {10.1609/AAAI.V26I1.8270},
url = {https://mlanthology.org/aaai/2012/celis2012aaai-approximately/}
}