Approximately Revenue-Maximizing Auctions for Deliberative Agents

Authors

  • L. Celis University of Washington
  • Anna Karlin University of Washington
  • Kevin Leyton-Brown University of British Columbia
  • C. Nguyen Facebook
  • David Thompson University of British Columbia

DOI:

https://doi.org/10.1609/aaai.v26i1.8270

Keywords:

Multiagent Systems::Auctions And Market-Based Systems ** Multiagent Systems::E-Commerce ** Multiagent Systems::Mechanism Design

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.

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Published

2021-09-20

How to Cite

Celis, L., Karlin, A., Leyton-Brown, K., Nguyen, C., & Thompson, D. (2021). Approximately Revenue-Maximizing Auctions for Deliberative Agents. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 1313-1318. https://doi.org/10.1609/aaai.v26i1.8270

Issue

Section

AAAI Technical Track: Multiagent Systems