Information elicitation for Bayesian auctions

Jing Chen, Bo Li, Yingkai Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


In this paper we design information elicitation mechanisms for Bayesian auctions. While in Bayesian mechanism design the distributions of the players’ private types are often assumed to be common knowledge, information elicitation considers the situation where the players know the distributions better than the decision maker. To weaken the information assumption in Bayesian auctions, we consider an information structure where the knowledge about the distributions is arbitrarily scattered among the players. In such an unstructured information setting, we design mechanisms for auctions with unit-demand or additive valuation functions that aggregate the players’ knowledge, generating revenue that are constant approximations to the optimal Bayesian mechanisms with a common prior. Our mechanisms are 2-step dominant-strategy truthful and the revenue increases gracefully with the amount of knowledge the players collectively have.

Original languageEnglish
Title of host publicationAlgorithmic Game Theory - 11th International Symposium, SAGT 2018, Proceedings
EditorsXiaotie Deng
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319996592
Publication statusPublished - 2018
Externally publishedYes
Event11th International Symposium on Algorithmic Game Theory, SAGT 2018 - Beijing, China
Duration: 11 Sept 201813 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11059 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Symposium on Algorithmic Game Theory, SAGT 2018


  • Distributed knowledge
  • Information elicitation
  • Removing common prior

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Information elicitation for Bayesian auctions'. Together they form a unique fingerprint.

Cite this